Add files using upload-large-folder tool

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/agent_runs/gen_36_task_message.txt

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+=== Generation 36 Evaluation ===
+
+📁 File Locations:
+- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318
+- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md
+- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py
+- All generations: $RESULTS_DIR/gen_0/ through gen_36/
+- Each generation has: main.py and results/metrics.json
+- Current code: $RESULTS_DIR/gen_36/main.py
+- Current metrics: $RESULTS_DIR/gen_36/results/metrics.json
+- Toolbox public API: eval_agent/tool_box/api.py
+- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py
+- Current score: 2.5414
+
+⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):
+- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py
+- This file defines the GROUND TRUTH evaluation metric
+- The primary score comes from this evaluator
+- You MUST NOT modify or replace this evaluator
+- You can READ it to understand what is being optimized
+- Your job is to create AUXILIARY metrics that complement it
+
+🎯 Generation-Specific Focus (follow system prompt for full policy):
+1. Analyze progress up to generation 36 and diagnose current bottlenecks.
+2. Update auxiliary_metrics.py with this generation's metric set.
+   - Keep evaluate_aux as the single entry point and return a flat metric dict.
+   - Smoke-test metric execution on current generation artifacts before finishing.
+3. Update EVAL_AGENTS.md for this generation's actions and rationale.
+
+Use toolbox public APIs when needed:
+- from eval_agent.tool_box import call_vision, call_tool
+- Do NOT import eval_agent/tool_box/_internal/*
+
+📈 Combined Score Trend (previous up to 10 generations):
+- gen_26: 2.5414 | gen_27: 2.5414 | gen_28: 0.0000 | gen_29: 2.5349 | gen_30: 2.5237 | gen_31: 2.5257 | gen_32: 0.0000 | gen_33: 2.4947 | gen_34: 2.5057 | gen_35: 0.0000
+- Net change (gen_26 -> gen_35): -2.5414

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/agent_runs/gen_48_task_message.txt

@@ -0,0 +1,36 @@
+=== Generation 48 Evaluation ===
+
+📁 File Locations:
+- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318
+- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md
+- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py
+- All generations: $RESULTS_DIR/gen_0/ through gen_48/
+- Each generation has: main.py and results/metrics.json
+- Current code: $RESULTS_DIR/gen_48/main.py
+- Current metrics: $RESULTS_DIR/gen_48/results/metrics.json
+- Toolbox public API: eval_agent/tool_box/api.py
+- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py
+- Current score: 0.0000
+
+⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):
+- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py
+- This file defines the GROUND TRUTH evaluation metric
+- The primary score comes from this evaluator
+- You MUST NOT modify or replace this evaluator
+- You can READ it to understand what is being optimized
+- Your job is to create AUXILIARY metrics that complement it
+
+🎯 Generation-Specific Focus (follow system prompt for full policy):
+1. Analyze progress up to generation 48 and diagnose current bottlenecks.
+2. Update auxiliary_metrics.py with this generation's metric set.
+   - Keep evaluate_aux as the single entry point and return a flat metric dict.
+   - Smoke-test metric execution on current generation artifacts before finishing.
+3. Update EVAL_AGENTS.md for this generation's actions and rationale.
+
+Use toolbox public APIs when needed:
+- from eval_agent.tool_box import call_vision, call_tool
+- Do NOT import eval_agent/tool_box/_internal/*
+
+📈 Combined Score Trend (previous up to 10 generations):
+- gen_38: 0.0000 | gen_39: 2.5414 | gen_40: 0.0000 | gen_41: 2.5414 | gen_42: 0.0000 | gen_43: 2.5414 | gen_44: 0.0000 | gen_45: 0.0000 | gen_46: 0.0000 | gen_47: 0.0000
+- Net change (gen_38 -> gen_47): +0.0000

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/agent_runs/gen_4_task_message.txt

@@ -0,0 +1,36 @@
+=== Generation 4 Evaluation ===
+
+📁 File Locations:
+- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318
+- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md
+- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py
+- All generations: $RESULTS_DIR/gen_0/ through gen_4/
+- Each generation has: main.py and results/metrics.json
+- Current code: $RESULTS_DIR/gen_4/main.py
+- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json
+- Toolbox public API: eval_agent/tool_box/api.py
+- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py
+- Current score: 1.6221
+
+⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):
+- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py
+- This file defines the GROUND TRUTH evaluation metric
+- The primary score comes from this evaluator
+- You MUST NOT modify or replace this evaluator
+- You can READ it to understand what is being optimized
+- Your job is to create AUXILIARY metrics that complement it
+
+🎯 Generation-Specific Focus (follow system prompt for full policy):
+1. Analyze progress up to generation 4 and diagnose current bottlenecks.
+2. Update auxiliary_metrics.py with this generation's metric set.
+   - Keep evaluate_aux as the single entry point and return a flat metric dict.
+   - Smoke-test metric execution on current generation artifacts before finishing.
+3. Update EVAL_AGENTS.md for this generation's actions and rationale.
+
+Use toolbox public APIs when needed:
+- from eval_agent.tool_box import call_vision, call_tool
+- Do NOT import eval_agent/tool_box/_internal/*
+
+📈 Combined Score Trend (previous up to 10 generations):
+- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530
+- Net change (gen_0 -> gen_3): +1.3932

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/agent_runs/gen_58_result.json

@@ -0,0 +1,23 @@
+{
+  "success": true,
+  "generation": 58,
+  "workspace": "/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory",
+  "insights": [
+    "- `aux_num_zero_radii`: Count of circles with radius 0.0, indicating non-contributing components or issues in radius calculation. (Component Contribution)",
+    "- `aux_average_radii`: Average radius of all circles. (Component Contribution)",
+    "- `aux_packed_area_ratio`: Ratio of the total area covered by circles to the unit square area. (Efficiency Metric)",
+    "- `aux_inter_circle_distance_variance`: Variance of distances between centers of adjacent non-overlapping circles. (Structural Sensitivity)",
+    "- `aux_center_distribution_uniformity`: Variance of circle counts in a 5x5 grid, indicating uniformity of center distribution. (Structural Sensitivity)",
+    "- REPLACE `aux_total_overlap_distance` -> `aux_inter_circle_distance_variance` (Reason: Provides a structural sensitivity signal to help escape local optima by encouraging more uniform spacing between non-overlapping circles.)",
+    "- REPLACE `aux_boundary_violation_distance` -> `aux_center_distribution_uniformity` (Reason: Provides a structural sensitivity signal to encourage a more uniform distribution of circle centers, potentially leading to better packing arrangements.)",
+    "- ADD `aux_average_radii` (Reason: Corrected omission, provides a component contribution signal indicating the overall size of circles.)",
+    "- ADD `aux_packed_area_ratio` (Reason: Provides an efficiency metric, indicating how densely packed the circles are, which is directly related to the optimization goal.)",
+    "- The `aux_extra_npz_found` metric was already implicitly part of the codebase logic; its explicit inclusion in the registry clarifies the active metrics."
+  ],
+  "auxiliary_metrics": {
+    "auxiliary_metrics_file_exists": true,
+    "auxiliary_metrics_path": "/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py",
+    "file_size_bytes": 6385
+  },
+  "elapsed_seconds": 89.76434564590454
+}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/agent_runs/gen_70_result.json

@@ -0,0 +1,23 @@
+{
+  "success": true,
+  "generation": 70,
+  "workspace": "/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory",
+  "insights": [
+    "- The total number of active auxiliary metrics remains 10.",
+    "- The new metrics aim to provide stronger gradient signals for exploring more efficient and diverse packing configurations, potentially helping to escape the current plateau.",
+    "- DELETE `aux_main_code_execution_error` (Reason: Consistently 0.0, providing no gradient signal, and exceeding the 10-metric limit).",
+    "- REPLACE `aux_inter_circle_distance_variance` -> `aux_min_pairwise_gap_positive` (Reason: To provide a stronger, more direct gradient signal for achieving tight packing by minimizing the positive gap between circles, helping to escape the plateau.)",
+    "- The total number of active auxiliary metrics is now 10, adhering to the limit.",
+    "- The new `aux_min_pairwise_gap_positive` metric aims to encourage configurations where circles are very close to touching without overlapping, which is crucial for optimal packing density.",
+    "- REPLACE `aux_min_pairwise_gap_positive` -> `aux_solution_validity_ratio` (Reason: To provide a direct signal on the robustness and validity of generated solutions, which is critical given the frequent 0.0 primary scores. This falls under the Search Velocity pillar, focusing on Feasibility.)",
+    "- ADD `aux_run_count` (Reason: To provide context to the `aux_solution_validity_ratio` and monitor the number of attempts made by the main code.)",
+    "- The total number of active auxiliary metrics remains 10.",
+    "- Focusing on diagnosing the root cause of 0.0 scores (invalid solutions) and stability before refining other packing characteristics."
+  ],
+  "auxiliary_metrics": {
+    "auxiliary_metrics_file_exists": true,
+    "auxiliary_metrics_path": "/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py",
+    "file_size_bytes": 6515
+  },
+  "elapsed_seconds": 150.43992233276367
+}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073694.079-582d.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "1LKfaYjHJ_HE694P3OSx-A0", "created": 1772073684, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": 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"function": {"arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}", "name": "file_editor"}, "id": 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"type": "function"}], "images": [], "reasoning_content": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 806, "prompt_tokens": 5492, "total_tokens": 6298, "completion_tokens_details": {"reasoning_tokens": 709, "text_tokens": 97}, "prompt_tokens_details": {"text_tokens": 5492}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0036626000000000002, "timestamp": 1772073694.0792696, "latency_sec": 9.750221490859985, "usage_summary": {"prompt_tokens": 5492, "completion_tokens": 806, "reasoning_tokens": 709, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073696.380-5c9a.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": "call_ad172c287e414c36a7999f9b4215__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__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", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "3rKfaeO2CJ3F694PuZW8wQw", "created": 1772073694, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG"}, "function": {"arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}", "name": "file_editor"}, "id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function"}], "images": [], "reasoning_content": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n"}], "provider_specific_fields": {"thought_signatures": ["CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG"]}}}], "usage": {"completion_tokens": 147, "prompt_tokens": 6431, "total_tokens": 6578, "completion_tokens_details": {"reasoning_tokens": 54, "text_tokens": 93}, "prompt_tokens_details": {"text_tokens": 6431}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0022968, "timestamp": 1772073696.3800557, "latency_sec": 2.275329351425171, "usage_summary": {"prompt_tokens": 6431, "completion_tokens": 147, "reasoning_tokens": 54, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073699.394-e3cc.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": "call_ad172c287e414c36a7999f9b4215__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "signature": null}], "tool_calls": [{"id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\tdef evaluate_aux(results_dir, primary_result=None):\n     2\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     3\t    return {}\n     4\t\n"}], "role": "tool", "tool_call_id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "4LKfaeT7Gor0694P6Y_ImAo", "created": 1772073696, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw="}, "function": {"arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"summary\": \"View evaluate_ori.py to understand the primary evaluation metric.\", \"command\": \"view\"}", "name": "file_editor"}, "id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "type": "function"}], "images": [], "reasoning_content": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n"}], "provider_specific_fields": {"thought_signatures": ["CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw="]}}}], "usage": {"completion_tokens": 103, "prompt_tokens": 6710, "total_tokens": 6813, "completion_tokens_details": {"reasoning_tokens": 53, "text_tokens": 50}, "prompt_tokens_details": {"text_tokens": 6710}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0022705, "timestamp": 1772073699.393709, "latency_sec": 2.9893293380737305, "usage_summary": {"prompt_tokens": 6710, "completion_tokens": 103, "reasoning_tokens": 53, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073710.093-f6fc.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": "call_ad172c287e414c36a7999f9b4215__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "signature": null}], "tool_calls": [{"id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\tdef evaluate_aux(results_dir, primary_result=None):\n     2\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     3\t    return {}\n     4\t\n"}], "role": "tool", "tool_call_id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n", "signature": null}], "tool_calls": [{"id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"summary\": \"View evaluate_ori.py to understand the primary evaluation metric.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py:\n     1\t\"\"\"\n     2\tEvaluator for circle packing example (n=26) with improved timeout handling\n     3\t\"\"\"\n     4\t\n     5\timport os\n     6\timport argparse\n     7\timport sys\n     8\timport numpy as np\n     9\tfrom typing import Tuple, Optional, List, Dict, Any\n    10\tfrom pathlib import Path\n    11\t\n    12\t# Add project root to import path for direct file execution.\n    13\tsys.path.insert(0, str(Path(__file__).resolve().parents[2]))\n    14\t\n    15\tfrom shinka.core import run_shinka_eval\n    16\t\n    17\t\n    18\tdef format_centers_string(centers: np.ndarray) -> str:\n    19\t    \"\"\"Formats circle centers into a multi-line string for display.\"\"\"\n    20\t    return \"\\n\".join(\n    21\t        [\n    22\t            f\"  centers[{i}] = ({x_coord:.4f}, {y_coord:.4f})\"\n    23\t            for i, (x_coord, y_coord) in enumerate(centers)\n    24\t        ]\n    25\t    )\n    26\t\n    27\t\n    28\tdef adapted_validate_packing(\n    29\t    run_output: Tuple[np.ndarray, np.ndarray, float],\n    30\t    atol=1e-6,\n    31\t) -> Tuple[bool, Optional[str]]:\n    32\t    \"\"\"\n    33\t    Validates circle packing results based on the output of 'run_packing'.\n    34\t\n    35\t    Args:\n    36\t        run_output: Tuple (centers, radii, reported_sum) from run_packing.\n    37\t\n    38\t    Returns:\n    39\t        (is_valid: bool, error_message: Optional[str])\n    40\t    \"\"\"\n    41\t    centers, radii, reported_sum = run_output\n    42\t    msg = \"The circles are placed correctly. There are no overlaps or any circles outside the unit square.\"\n    43\t    if not isinstance(centers, np.ndarray):\n    44\t        centers = np.array(centers)\n    45\t    if not isinstance(radii, np.ndarray):\n    46\t        radii = np.array(radii)\n    47\t\n    48\t    n_expected = 26\n    49\t    if centers.shape != (n_expected, 2):\n    50\t        msg = (\n    51\t            f\"Centers shape incorrect. Expected ({n_expected}, 2), got {centers.shape}\"\n    52\t        )\n    53\t        return False, msg\n    54\t    if radii.shape != (n_expected,):\n    55\t        msg = f\"Radii shape incorrect. Expected ({n_expected},), got {radii.shape}\"\n    56\t        return False, msg\n    57\t\n    58\t    if np.any(radii < 0):\n    59\t        negative_indices = np.where(radii < 0)[0]\n    60\t        msg = f\"Negative radii found for circles at indices: {negative_indices}\"\n    61\t        return False, msg\n    62\t\n    63\t    if not np.isclose(np.sum(radii), reported_sum, atol=atol):\n    64\t        msg = (\n    65\t            f\"Sum of radii ({np.sum(radii):.6f}) does not match \"\n    66\t            f\"reported ({reported_sum:.6f})\"\n    67\t        )\n    68\t        return False, msg\n    69\t\n    70\t    for i in range(n_expected):\n    71\t        x, y = centers[i]\n    72\t        r = radii[i]\n    73\t        is_outside = (\n    74\t            x - r < -atol or x + r > 1 + atol or y - r < -atol or y + r > 1 + atol\n    75\t        )\n    76\t        if is_outside:\n    77\t            msg = (\n    78\t                f\"Circle {i} (x={x:.4f}, y={y:.4f}, r={r:.4f}) is outside unit square.\"\n    79\t            )\n    80\t            return False, msg\n    81\t\n    82\t    for i in range(n_expected):\n    83\t        for j in range(i + 1, n_expected):\n    84\t            dist = np.sqrt(np.sum((centers[i] - centers[j]) ** 2))\n    85\t            if dist < radii[i] + radii[j] - atol:\n    86\t                msg = (\n    87\t                    f\"Circles {i} & {j} overlap. Dist: {dist:.4f}, \"\n    88\t                    f\"Sum Radii: {(radii[i] + radii[j]):.4f}\"\n    89\t                )\n    90\t                return False, msg\n    91\t    return True, msg\n    92\t\n    93\t\n    94\tdef get_circle_packing_kwargs(run_index: int) -> Dict[str, Any]:\n    95\t    \"\"\"Provides keyword arguments for circle packing runs (none needed).\"\"\"\n    96\t    return {}\n    97\t\n    98\t\n    99\tdef aggregate_circle_packing_metrics(\n   100\t    results: List[Tuple[np.ndarray, np.ndarray, float]], results_dir: str\n   101\t) -> Dict[str, Any]:\n   102\t    \"\"\"\n   103\t    Aggregates metrics for circle packing. Assumes num_runs=1.\n   104\t    Saves extra.npz with detailed packing information.\n   105\t    \"\"\"\n   106\t    if not results:\n   107\t        return {\"combined_score\": 0.0, \"error\": \"No results to aggregate\"}\n   108\t\n   109\t    centers, radii, reported_sum = results[0]\n   110\t\n   111\t    public_metrics = {\n   112\t        \"centers_str\": format_centers_string(centers),\n   113\t        \"num_circles\": centers.shape[0],\n   114\t    }\n   115\t    private_metrics = {\n   116\t        \"reported_sum_of_radii\": float(reported_sum),\n   117\t    }\n   118\t    metrics = {\n   119\t        \"combined_score\": float(reported_sum),\n   120\t        \"public\": public_metrics,\n   121\t        \"private\": private_metrics,\n   122\t    }\n   123\t\n   124\t    extra_file = os.path.join(results_dir, \"extra.npz\")\n   125\t    try:\n   126\t        np.savez(\n   127\t            extra_file,\n   128\t            centers=centers,\n   129\t            radii=radii,\n   130\t            reported_sum=reported_sum,\n   131\t        )\n   132\t        print(f\"Detailed packing data saved to {extra_file}\")\n   133\t    except Exception as e:\n   134\t        print(f\"Error saving extra.npz: {e}\")\n   135\t        metrics[\"extra_npz_save_error\"] = str(e)\n   136\t\n   137\t    return metrics\n   138\t\n   139\t\n   140\tdef main(program_path: str, results_dir: str):\n   141\t    \"\"\"Runs the circle packing evaluation using shinka.eval.\"\"\"\n   142\t    print(f\"Evaluating program: {program_path}\")\n   143\t    print(f\"Saving results to: {results_dir}\")\n   144\t    os.makedirs(results_dir, exist_ok=True)\n   145\t\n   146\t    num_experiment_runs = 1\n   147\t\n   148\t    # Define a nested function to pass results_dir to the aggregator\n   149\t    def _aggregator_with_context(\n   150\t        r: List[Tuple[np.ndarray, np.ndarray, float]],\n   151\t    ) -> Dict[str, Any]:\n   152\t        return aggregate_circle_packing_metrics(r, results_dir)\n   153\t\n   154\t    metrics, correct, error_msg = run_shinka_eval(\n   155\t        program_path=program_path,\n   156\t        results_dir=results_dir,\n   157\t        experiment_fn_name=\"run_packing\",\n   158\t        num_runs=num_experiment_runs,\n   159\t        get_experiment_kwargs=get_circle_packing_kwargs,\n   160\t        validate_fn=adapted_validate_packing,\n   161\t        aggregate_metrics_fn=_aggregator_with_context,\n   162\t    )\n   163\t\n   164\t    if correct:\n   165\t        print(\"Evaluation and Validation completed successfully.\")\n   166\t    else:\n   167\t        print(f\"Evaluation or Validation failed: {error_msg}\")\n   168\t\n   169\t    print(\"Metrics:\")\n   170\t    for key, value in metrics.items():\n   171\t        if isinstance(value, str) and len(value) > 100:\n   172\t            print(f\"  {key}: <string_too_long_to_display>\")\n   173\t        else:\n   174\t            print(f\"  {key}: {value}\")\n   175\t\n   176\t\n   177\tif __name__ == \"__main__\":\n   178\t    parser = argparse.ArgumentParser(\n   179\t        description=\"Circle packing evaluator using shinka.eval\"\n   180\t    )\n   181\t    parser.add_argument(\n   182\t        \"--program_path\",\n   183\t        type=str,\n   184\t        default=\"tasks/circle_packing/initial.py\",\n   185\t        help=\"Path to program to evaluate (must contain 'run_packing')\",\n   186\t    )\n   187\t    parser.add_argument(\n   188\t        \"--results_dir\",\n   189\t        type=str,\n   190\t        default=\"tasks/circle_packing/results/debug_eval\",\n   191\t        help=\"Dir to save results (metrics.json, correct.json, extra.npz)\",\n   192\t    )\n   193\t    parsed_args = parser.parse_args()\n   194\t    main(parsed_args.program_path, parsed_args.results_dir)\n   195\t\n"}], "role": "tool", "tool_call_id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "47Kfaaa8HN-G694Po_7k8Qs", "created": 1772073699, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": 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"function": {"arguments": "{\"file_text\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"create\"}", "name": "file_editor"}, "id": 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"type": "function"}], "images": [], "reasoning_content": "Okay, here's what I'm thinking. I'm looking at `evaluate_ori.py` and immediately see the core problem: packing 26 circles within a unit square. The goal, as judged by the `combined_score`, is to maximize the sum of the radii.\n\nThe key constraints are no negative radii, all circles inside the square, and no overlaps. The `adapted_validate_packing` function is the gatekeeper here, ensuring these constraints are met. A problem in Gen 4 has become apparent, with a dramatic score drop from Gen 3. The `combined_score` has significantly decreased, indicating something is seriously wrong with the circle packing in Gen 4.\n\nI need to understand why. My initial hypothesis is that either the constraints are being violated (overlaps or boundary violations) or the radii of the circles have shrunk dramatically, causing a lower total. I'll need to dig into this by analyzing the `primary_result` and `main.py` files for Gen 4. It will be helpful to find out where the `extra.npz` files are being saved, since that's where the critical `centers` and `radii` data are stored. The `results_dir` is the place to start.\n\nTo help diagnose this, I need to create some auxiliary metrics. I need to define them in `EVAL_AGENTS.md` since it's currently empty, and then write the code in `auxiliary_metrics.py`.\n\nThe key will be to confirm constraint violations:\n\n*   I'll calculate `aux_total_overlap_distance` and `aux_max_overlap_distance` to quantify the overlap problem.\n*   `aux_boundary_violation_distance` to measure how much circles are going outside the square.\n*   `aux_min_radius` to see if the radii are becoming suspiciously small.\n*   `aux_num_violated_circles`, a simple count of constraint violations.\n\nEssentially, I'm now in a diagnosis phase – either `EXPLORATION` or `OPTIMIZATION` -- where I am trying to figure out why the packing quality has regressed so significantly. The next step is to update `auxiliary_metrics.py` based on this analysis.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking. I'm looking at `evaluate_ori.py` and immediately see the core problem: packing 26 circles within a unit square. The goal, as judged by the `combined_score`, is to maximize the sum of the radii.\n\nThe key constraints are no negative radii, all circles inside the square, and no overlaps. The `adapted_validate_packing` function is the gatekeeper here, ensuring these constraints are met. A problem in Gen 4 has become apparent, with a dramatic score drop from Gen 3. The `combined_score` has significantly decreased, indicating something is seriously wrong with the circle packing in Gen 4.\n\nI need to understand why. My initial hypothesis is that either the constraints are being violated (overlaps or boundary violations) or the radii of the circles have shrunk dramatically, causing a lower total. I'll need to dig into this by analyzing the `primary_result` and `main.py` files for Gen 4. It will be helpful to find out where the `extra.npz` files are being saved, since that's where the critical `centers` and `radii` data are stored. The `results_dir` is the place to start.\n\nTo help diagnose this, I need to create some auxiliary metrics. I need to define them in `EVAL_AGENTS.md` since it's currently empty, and then write the code in `auxiliary_metrics.py`.\n\nThe key will be to confirm constraint violations:\n\n*   I'll calculate `aux_total_overlap_distance` and `aux_max_overlap_distance` to quantify the overlap problem.\n*   `aux_boundary_violation_distance` to measure how much circles are going outside the square.\n*   `aux_min_radius` to see if the radii are becoming suspiciously small.\n*   `aux_num_violated_circles`, a simple count of constraint violations.\n\nEssentially, I'm now in a diagnosis phase – either `EXPLORATION` or `OPTIMIZATION` -- where I am trying to figure out why the packing quality has regressed so significantly. The next step is to update `auxiliary_metrics.py` based on this analysis.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 1125, "prompt_tokens": 9601, "total_tokens": 10726, "completion_tokens_details": {"reasoning_tokens": 697, "text_tokens": 428}, "prompt_tokens_details": {"text_tokens": 9601}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0056928, "timestamp": 1772073710.0929775, "latency_sec": 10.657350778579712, "usage_summary": {"prompt_tokens": 9601, "completion_tokens": 1125, "reasoning_tokens": 697, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073714.492-b80e.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": "call_ad172c287e414c36a7999f9b4215__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "signature": null}], "tool_calls": [{"id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\tdef evaluate_aux(results_dir, primary_result=None):\n     2\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     3\t    return {}\n     4\t\n"}], "role": "tool", "tool_call_id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n", "signature": null}], "tool_calls": [{"id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"summary\": \"View evaluate_ori.py to understand the primary evaluation metric.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py:\n     1\t\"\"\"\n     2\tEvaluator for circle packing example (n=26) with improved timeout handling\n     3\t\"\"\"\n     4\t\n     5\timport os\n     6\timport argparse\n     7\timport sys\n     8\timport numpy as np\n     9\tfrom typing import Tuple, Optional, List, Dict, Any\n    10\tfrom pathlib import Path\n    11\t\n    12\t# Add project root to import path for direct file execution.\n    13\tsys.path.insert(0, str(Path(__file__).resolve().parents[2]))\n    14\t\n    15\tfrom shinka.core import run_shinka_eval\n    16\t\n    17\t\n    18\tdef format_centers_string(centers: np.ndarray) -> str:\n    19\t    \"\"\"Formats circle centers into a multi-line string for display.\"\"\"\n    20\t    return \"\\n\".join(\n    21\t        [\n    22\t            f\"  centers[{i}] = ({x_coord:.4f}, {y_coord:.4f})\"\n    23\t            for i, (x_coord, y_coord) in enumerate(centers)\n    24\t        ]\n    25\t    )\n    26\t\n    27\t\n    28\tdef adapted_validate_packing(\n    29\t    run_output: Tuple[np.ndarray, np.ndarray, float],\n    30\t    atol=1e-6,\n    31\t) -> Tuple[bool, Optional[str]]:\n    32\t    \"\"\"\n    33\t    Validates circle packing results based on the output of 'run_packing'.\n    34\t\n    35\t    Args:\n    36\t        run_output: Tuple (centers, radii, reported_sum) from run_packing.\n    37\t\n    38\t    Returns:\n    39\t        (is_valid: bool, error_message: Optional[str])\n    40\t    \"\"\"\n    41\t    centers, radii, reported_sum = run_output\n    42\t    msg = \"The circles are placed correctly. There are no overlaps or any circles outside the unit square.\"\n    43\t    if not isinstance(centers, np.ndarray):\n    44\t        centers = np.array(centers)\n    45\t    if not isinstance(radii, np.ndarray):\n    46\t        radii = np.array(radii)\n    47\t\n    48\t    n_expected = 26\n    49\t    if centers.shape != (n_expected, 2):\n    50\t        msg = (\n    51\t            f\"Centers shape incorrect. Expected ({n_expected}, 2), got {centers.shape}\"\n    52\t        )\n    53\t        return False, msg\n    54\t    if radii.shape != (n_expected,):\n    55\t        msg = f\"Radii shape incorrect. Expected ({n_expected},), got {radii.shape}\"\n    56\t        return False, msg\n    57\t\n    58\t    if np.any(radii < 0):\n    59\t        negative_indices = np.where(radii < 0)[0]\n    60\t        msg = f\"Negative radii found for circles at indices: {negative_indices}\"\n    61\t        return False, msg\n    62\t\n    63\t    if not np.isclose(np.sum(radii), reported_sum, atol=atol):\n    64\t        msg = (\n    65\t            f\"Sum of radii ({np.sum(radii):.6f}) does not match \"\n    66\t            f\"reported ({reported_sum:.6f})\"\n    67\t        )\n    68\t        return False, msg\n    69\t\n    70\t    for i in range(n_expected):\n    71\t        x, y = centers[i]\n    72\t        r = radii[i]\n    73\t        is_outside = (\n    74\t            x - r < -atol or x + r > 1 + atol or y - r < -atol or y + r > 1 + atol\n    75\t        )\n    76\t        if is_outside:\n    77\t            msg = (\n    78\t                f\"Circle {i} (x={x:.4f}, y={y:.4f}, r={r:.4f}) is outside unit square.\"\n    79\t            )\n    80\t            return False, msg\n    81\t\n    82\t    for i in range(n_expected):\n    83\t        for j in range(i + 1, n_expected):\n    84\t            dist = np.sqrt(np.sum((centers[i] - centers[j]) ** 2))\n    85\t            if dist < radii[i] + radii[j] - atol:\n    86\t                msg = (\n    87\t                    f\"Circles {i} & {j} overlap. Dist: {dist:.4f}, \"\n    88\t                    f\"Sum Radii: {(radii[i] + radii[j]):.4f}\"\n    89\t                )\n    90\t                return False, msg\n    91\t    return True, msg\n    92\t\n    93\t\n    94\tdef get_circle_packing_kwargs(run_index: int) -> Dict[str, Any]:\n    95\t    \"\"\"Provides keyword arguments for circle packing runs (none needed).\"\"\"\n    96\t    return {}\n    97\t\n    98\t\n    99\tdef aggregate_circle_packing_metrics(\n   100\t    results: List[Tuple[np.ndarray, np.ndarray, float]], results_dir: str\n   101\t) -> Dict[str, Any]:\n   102\t    \"\"\"\n   103\t    Aggregates metrics for circle packing. Assumes num_runs=1.\n   104\t    Saves extra.npz with detailed packing information.\n   105\t    \"\"\"\n   106\t    if not results:\n   107\t        return {\"combined_score\": 0.0, \"error\": \"No results to aggregate\"}\n   108\t\n   109\t    centers, radii, reported_sum = results[0]\n   110\t\n   111\t    public_metrics = {\n   112\t        \"centers_str\": format_centers_string(centers),\n   113\t        \"num_circles\": centers.shape[0],\n   114\t    }\n   115\t    private_metrics = {\n   116\t        \"reported_sum_of_radii\": float(reported_sum),\n   117\t    }\n   118\t    metrics = {\n   119\t        \"combined_score\": float(reported_sum),\n   120\t        \"public\": public_metrics,\n   121\t        \"private\": private_metrics,\n   122\t    }\n   123\t\n   124\t    extra_file = os.path.join(results_dir, \"extra.npz\")\n   125\t    try:\n   126\t        np.savez(\n   127\t            extra_file,\n   128\t            centers=centers,\n   129\t            radii=radii,\n   130\t            reported_sum=reported_sum,\n   131\t        )\n   132\t        print(f\"Detailed packing data saved to {extra_file}\")\n   133\t    except Exception as e:\n   134\t        print(f\"Error saving extra.npz: {e}\")\n   135\t        metrics[\"extra_npz_save_error\"] = str(e)\n   136\t\n   137\t    return metrics\n   138\t\n   139\t\n   140\tdef main(program_path: str, results_dir: str):\n   141\t    \"\"\"Runs the circle packing evaluation using shinka.eval.\"\"\"\n   142\t    print(f\"Evaluating program: {program_path}\")\n   143\t    print(f\"Saving results to: {results_dir}\")\n   144\t    os.makedirs(results_dir, exist_ok=True)\n   145\t\n   146\t    num_experiment_runs = 1\n   147\t\n   148\t    # Define a nested function to pass results_dir to the aggregator\n   149\t    def _aggregator_with_context(\n   150\t        r: List[Tuple[np.ndarray, np.ndarray, float]],\n   151\t    ) -> Dict[str, Any]:\n   152\t        return aggregate_circle_packing_metrics(r, results_dir)\n   153\t\n   154\t    metrics, correct, error_msg = run_shinka_eval(\n   155\t        program_path=program_path,\n   156\t        results_dir=results_dir,\n   157\t        experiment_fn_name=\"run_packing\",\n   158\t        num_runs=num_experiment_runs,\n   159\t        get_experiment_kwargs=get_circle_packing_kwargs,\n   160\t        validate_fn=adapted_validate_packing,\n   161\t        aggregate_metrics_fn=_aggregator_with_context,\n   162\t    )\n   163\t\n   164\t    if correct:\n   165\t        print(\"Evaluation and Validation completed successfully.\")\n   166\t    else:\n   167\t        print(f\"Evaluation or Validation failed: {error_msg}\")\n   168\t\n   169\t    print(\"Metrics:\")\n   170\t    for key, value in metrics.items():\n   171\t        if isinstance(value, str) and len(value) > 100:\n   172\t            print(f\"  {key}: <string_too_long_to_display>\")\n   173\t        else:\n   174\t            print(f\"  {key}: {value}\")\n   175\t\n   176\t\n   177\tif __name__ == \"__main__\":\n   178\t    parser = argparse.ArgumentParser(\n   179\t        description=\"Circle packing evaluator using shinka.eval\"\n   180\t    )\n   181\t    parser.add_argument(\n   182\t        \"--program_path\",\n   183\t        type=str,\n   184\t        default=\"tasks/circle_packing/initial.py\",\n   185\t        help=\"Path to program to evaluate (must contain 'run_packing')\",\n   186\t    )\n   187\t    parser.add_argument(\n   188\t        \"--results_dir\",\n   189\t        type=str,\n   190\t        default=\"tasks/circle_packing/results/debug_eval\",\n   191\t        help=\"Dir to save results (metrics.json, correct.json, extra.npz)\",\n   192\t    )\n   193\t    parsed_args = parser.parse_args()\n   194\t    main(parsed_args.program_path, parsed_args.results_dir)\n   195\t\n"}], "role": "tool", "tool_call_id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking. I'm looking at `evaluate_ori.py` and immediately see the core problem: packing 26 circles within a unit square. The goal, as judged by the `combined_score`, is to maximize the sum of the radii.\n\nThe key constraints are no negative radii, all circles inside the square, and no overlaps. The `adapted_validate_packing` function is the gatekeeper here, ensuring these constraints are met. A problem in Gen 4 has become apparent, with a dramatic score drop from Gen 3. The `combined_score` has significantly decreased, indicating something is seriously wrong with the circle packing in Gen 4.\n\nI need to understand why. My initial hypothesis is that either the constraints are being violated (overlaps or boundary violations) or the radii of the circles have shrunk dramatically, causing a lower total. I'll need to dig into this by analyzing the `primary_result` and `main.py` files for Gen 4. It will be helpful to find out where the `extra.npz` files are being saved, since that's where the critical `centers` and `radii` data are stored. The `results_dir` is the place to start.\n\nTo help diagnose this, I need to create some auxiliary metrics. I need to define them in `EVAL_AGENTS.md` since it's currently empty, and then write the code in `auxiliary_metrics.py`.\n\nThe key will be to confirm constraint violations:\n\n*   I'll calculate `aux_total_overlap_distance` and `aux_max_overlap_distance` to quantify the overlap problem.\n*   `aux_boundary_violation_distance` to measure how much circles are going outside the square.\n*   `aux_min_radius` to see if the radii are becoming suspiciously small.\n*   `aux_num_violated_circles`, a simple count of constraint violations.\n\nEssentially, I'm now in a diagnosis phase – either `EXPLORATION` or `OPTIMIZATION` -- where I am trying to figure out why the packing quality has regressed so significantly. The next step is to update `auxiliary_metrics.py` based on this analysis.\n", "signature": null}], "tool_calls": [{"id": "call_93797a325ec240039ed95cf3c33e__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"file_text\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"create\"}"}}]}, {"content": [{"type": "text", "text": "[An error occurred during execution.]\n"}, {"type": "text", "text": "Invalid `path` parameter: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. File already exists at: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. Cannot overwrite files using command `create`."}], "role": "tool", "tool_call_id": "call_93797a325ec240039ed95cf3c33e__thought__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", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "7rKfaY6pCPHE694P3OSx-A0", "created": 1772073710, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw=="}, "function": {"arguments": "{\"security_risk\": \"LOW\", \"command\": \"str_replace\", \"old_str\": \"# EV2 Agent Memory\\n\\n- Initialized by eval service.\\n- Use this file as compact cross-generation memory.\\n\", \"new_str\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\"}", "name": "file_editor"}, "id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "type": "function"}], "images": [], "reasoning_content": "Right, a slip of the mind, completely my fault. I got ahead of myself and forgot that the system's already laid the groundwork with `EVAL_AGENTS.md`, even if it's currently a blank slate. Silly me, I should have been using `str_replace` or something similar for the update, not a fresh `create`. \n\nOkay, no problem. I'll utilize `str_replace` now. That's the correct way to inject the relevant registry and generation log into that pre-existing file. Time to make sure the initial content is correctly appended.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Right, a slip of the mind, completely my fault. I got ahead of myself and forgot that the system's already laid the groundwork with `EVAL_AGENTS.md`, even if it's currently a blank slate. Silly me, I should have been using `str_replace` or something similar for the update, not a fresh `create`. \n\nOkay, no problem. I'll utilize `str_replace` now. That's the correct way to inject the relevant registry and generation log into that pre-existing file. 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tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073728.928-e1a1.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": "call_ad172c287e414c36a7999f9b4215__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "signature": null}], "tool_calls": [{"id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\tdef evaluate_aux(results_dir, primary_result=None):\n     2\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     3\t    return {}\n     4\t\n"}], "role": "tool", "tool_call_id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n", "signature": null}], "tool_calls": [{"id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"summary\": \"View evaluate_ori.py to understand the primary evaluation metric.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py:\n     1\t\"\"\"\n     2\tEvaluator for circle packing example (n=26) with improved timeout handling\n     3\t\"\"\"\n     4\t\n     5\timport os\n     6\timport argparse\n     7\timport sys\n     8\timport numpy as np\n     9\tfrom typing import Tuple, Optional, List, Dict, Any\n    10\tfrom pathlib import Path\n    11\t\n    12\t# Add project root to import path for direct file execution.\n    13\tsys.path.insert(0, str(Path(__file__).resolve().parents[2]))\n    14\t\n    15\tfrom shinka.core import run_shinka_eval\n    16\t\n    17\t\n    18\tdef format_centers_string(centers: np.ndarray) -> str:\n    19\t    \"\"\"Formats circle centers into a multi-line string for display.\"\"\"\n    20\t    return \"\\n\".join(\n    21\t        [\n    22\t            f\"  centers[{i}] = ({x_coord:.4f}, {y_coord:.4f})\"\n    23\t            for i, (x_coord, y_coord) in enumerate(centers)\n    24\t        ]\n    25\t    )\n    26\t\n    27\t\n    28\tdef adapted_validate_packing(\n    29\t    run_output: Tuple[np.ndarray, np.ndarray, float],\n    30\t    atol=1e-6,\n    31\t) -> Tuple[bool, Optional[str]]:\n    32\t    \"\"\"\n    33\t    Validates circle packing results based on the output of 'run_packing'.\n    34\t\n    35\t    Args:\n    36\t        run_output: Tuple (centers, radii, reported_sum) from run_packing.\n    37\t\n    38\t    Returns:\n    39\t        (is_valid: bool, error_message: Optional[str])\n    40\t    \"\"\"\n    41\t    centers, radii, reported_sum = run_output\n    42\t    msg = \"The circles are placed correctly. There are no overlaps or any circles outside the unit square.\"\n    43\t    if not isinstance(centers, np.ndarray):\n    44\t        centers = np.array(centers)\n    45\t    if not isinstance(radii, np.ndarray):\n    46\t        radii = np.array(radii)\n    47\t\n    48\t    n_expected = 26\n    49\t    if centers.shape != (n_expected, 2):\n    50\t        msg = (\n    51\t            f\"Centers shape incorrect. Expected ({n_expected}, 2), got {centers.shape}\"\n    52\t        )\n    53\t        return False, msg\n    54\t    if radii.shape != (n_expected,):\n    55\t        msg = f\"Radii shape incorrect. Expected ({n_expected},), got {radii.shape}\"\n    56\t        return False, msg\n    57\t\n    58\t    if np.any(radii < 0):\n    59\t        negative_indices = np.where(radii < 0)[0]\n    60\t        msg = f\"Negative radii found for circles at indices: {negative_indices}\"\n    61\t        return False, msg\n    62\t\n    63\t    if not np.isclose(np.sum(radii), reported_sum, atol=atol):\n    64\t        msg = (\n    65\t            f\"Sum of radii ({np.sum(radii):.6f}) does not match \"\n    66\t            f\"reported ({reported_sum:.6f})\"\n    67\t        )\n    68\t        return False, msg\n    69\t\n    70\t    for i in range(n_expected):\n    71\t        x, y = centers[i]\n    72\t        r = radii[i]\n    73\t        is_outside = (\n    74\t            x - r < -atol or x + r > 1 + atol or y - r < -atol or y + r > 1 + atol\n    75\t        )\n    76\t        if is_outside:\n    77\t            msg = (\n    78\t                f\"Circle {i} (x={x:.4f}, y={y:.4f}, r={r:.4f}) is outside unit square.\"\n    79\t            )\n    80\t            return False, msg\n    81\t\n    82\t    for i in range(n_expected):\n    83\t        for j in range(i + 1, n_expected):\n    84\t            dist = np.sqrt(np.sum((centers[i] - centers[j]) ** 2))\n    85\t            if dist < radii[i] + radii[j] - atol:\n    86\t                msg = (\n    87\t                    f\"Circles {i} & {j} overlap. Dist: {dist:.4f}, \"\n    88\t                    f\"Sum Radii: {(radii[i] + radii[j]):.4f}\"\n    89\t                )\n    90\t                return False, msg\n    91\t    return True, msg\n    92\t\n    93\t\n    94\tdef get_circle_packing_kwargs(run_index: int) -> Dict[str, Any]:\n    95\t    \"\"\"Provides keyword arguments for circle packing runs (none needed).\"\"\"\n    96\t    return {}\n    97\t\n    98\t\n    99\tdef aggregate_circle_packing_metrics(\n   100\t    results: List[Tuple[np.ndarray, np.ndarray, float]], results_dir: str\n   101\t) -> Dict[str, Any]:\n   102\t    \"\"\"\n   103\t    Aggregates metrics for circle packing. Assumes num_runs=1.\n   104\t    Saves extra.npz with detailed packing information.\n   105\t    \"\"\"\n   106\t    if not results:\n   107\t        return {\"combined_score\": 0.0, \"error\": \"No results to aggregate\"}\n   108\t\n   109\t    centers, radii, reported_sum = results[0]\n   110\t\n   111\t    public_metrics = {\n   112\t        \"centers_str\": format_centers_string(centers),\n   113\t        \"num_circles\": centers.shape[0],\n   114\t    }\n   115\t    private_metrics = {\n   116\t        \"reported_sum_of_radii\": float(reported_sum),\n   117\t    }\n   118\t    metrics = {\n   119\t        \"combined_score\": float(reported_sum),\n   120\t        \"public\": public_metrics,\n   121\t        \"private\": private_metrics,\n   122\t    }\n   123\t\n   124\t    extra_file = os.path.join(results_dir, \"extra.npz\")\n   125\t    try:\n   126\t        np.savez(\n   127\t            extra_file,\n   128\t            centers=centers,\n   129\t            radii=radii,\n   130\t            reported_sum=reported_sum,\n   131\t        )\n   132\t        print(f\"Detailed packing data saved to {extra_file}\")\n   133\t    except Exception as e:\n   134\t        print(f\"Error saving extra.npz: {e}\")\n   135\t        metrics[\"extra_npz_save_error\"] = str(e)\n   136\t\n   137\t    return metrics\n   138\t\n   139\t\n   140\tdef main(program_path: str, results_dir: str):\n   141\t    \"\"\"Runs the circle packing evaluation using shinka.eval.\"\"\"\n   142\t    print(f\"Evaluating program: {program_path}\")\n   143\t    print(f\"Saving results to: {results_dir}\")\n   144\t    os.makedirs(results_dir, exist_ok=True)\n   145\t\n   146\t    num_experiment_runs = 1\n   147\t\n   148\t    # Define a nested function to pass results_dir to the aggregator\n   149\t    def _aggregator_with_context(\n   150\t        r: List[Tuple[np.ndarray, np.ndarray, float]],\n   151\t    ) -> Dict[str, Any]:\n   152\t        return aggregate_circle_packing_metrics(r, results_dir)\n   153\t\n   154\t    metrics, correct, error_msg = run_shinka_eval(\n   155\t        program_path=program_path,\n   156\t        results_dir=results_dir,\n   157\t        experiment_fn_name=\"run_packing\",\n   158\t        num_runs=num_experiment_runs,\n   159\t        get_experiment_kwargs=get_circle_packing_kwargs,\n   160\t        validate_fn=adapted_validate_packing,\n   161\t        aggregate_metrics_fn=_aggregator_with_context,\n   162\t    )\n   163\t\n   164\t    if correct:\n   165\t        print(\"Evaluation and Validation completed successfully.\")\n   166\t    else:\n   167\t        print(f\"Evaluation or Validation failed: {error_msg}\")\n   168\t\n   169\t    print(\"Metrics:\")\n   170\t    for key, value in metrics.items():\n   171\t        if isinstance(value, str) and len(value) > 100:\n   172\t            print(f\"  {key}: <string_too_long_to_display>\")\n   173\t        else:\n   174\t            print(f\"  {key}: {value}\")\n   175\t\n   176\t\n   177\tif __name__ == \"__main__\":\n   178\t    parser = argparse.ArgumentParser(\n   179\t        description=\"Circle packing evaluator using shinka.eval\"\n   180\t    )\n   181\t    parser.add_argument(\n   182\t        \"--program_path\",\n   183\t        type=str,\n   184\t        default=\"tasks/circle_packing/initial.py\",\n   185\t        help=\"Path to program to evaluate (must contain 'run_packing')\",\n   186\t    )\n   187\t    parser.add_argument(\n   188\t        \"--results_dir\",\n   189\t        type=str,\n   190\t        default=\"tasks/circle_packing/results/debug_eval\",\n   191\t        help=\"Dir to save results (metrics.json, correct.json, extra.npz)\",\n   192\t    )\n   193\t    parsed_args = parser.parse_args()\n   194\t    main(parsed_args.program_path, parsed_args.results_dir)\n   195\t\n"}], "role": "tool", "tool_call_id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking. I'm looking at `evaluate_ori.py` and immediately see the core problem: packing 26 circles within a unit square. The goal, as judged by the `combined_score`, is to maximize the sum of the radii.\n\nThe key constraints are no negative radii, all circles inside the square, and no overlaps. The `adapted_validate_packing` function is the gatekeeper here, ensuring these constraints are met. A problem in Gen 4 has become apparent, with a dramatic score drop from Gen 3. The `combined_score` has significantly decreased, indicating something is seriously wrong with the circle packing in Gen 4.\n\nI need to understand why. My initial hypothesis is that either the constraints are being violated (overlaps or boundary violations) or the radii of the circles have shrunk dramatically, causing a lower total. I'll need to dig into this by analyzing the `primary_result` and `main.py` files for Gen 4. It will be helpful to find out where the `extra.npz` files are being saved, since that's where the critical `centers` and `radii` data are stored. The `results_dir` is the place to start.\n\nTo help diagnose this, I need to create some auxiliary metrics. I need to define them in `EVAL_AGENTS.md` since it's currently empty, and then write the code in `auxiliary_metrics.py`.\n\nThe key will be to confirm constraint violations:\n\n*   I'll calculate `aux_total_overlap_distance` and `aux_max_overlap_distance` to quantify the overlap problem.\n*   `aux_boundary_violation_distance` to measure how much circles are going outside the square.\n*   `aux_min_radius` to see if the radii are becoming suspiciously small.\n*   `aux_num_violated_circles`, a simple count of constraint violations.\n\nEssentially, I'm now in a diagnosis phase – either `EXPLORATION` or `OPTIMIZATION` -- where I am trying to figure out why the packing quality has regressed so significantly. The next step is to update `auxiliary_metrics.py` based on this analysis.\n", "signature": null}], "tool_calls": [{"id": "call_93797a325ec240039ed95cf3c33e__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"file_text\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"create\"}"}}]}, {"content": [{"type": "text", "text": "[An error occurred during execution.]\n"}, {"type": "text", "text": "Invalid `path` parameter: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. File already exists at: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. Cannot overwrite files using command `create`."}], "role": "tool", "tool_call_id": "call_93797a325ec240039ed95cf3c33e__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Right, a slip of the mind, completely my fault. I got ahead of myself and forgot that the system's already laid the groundwork with `EVAL_AGENTS.md`, even if it's currently a blank slate. Silly me, I should have been using `str_replace` or something similar for the update, not a fresh `create`. \n\nOkay, no problem. I'll utilize `str_replace` now. That's the correct way to inject the relevant registry and generation log into that pre-existing file. Time to make sure the initial content is correctly appended.\n", "signature": null}], "tool_calls": [{"id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"str_replace\", \"old_str\": \"# EV2 Agent Memory\\n\\n- Initialized by eval service.\\n- Use this file as compact cross-generation memory.\\n\", \"new_str\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, now that I've meticulously updated `EVAL_AGENTS.md`, ensuring all the agent evaluation criteria are up to snuff, it's time to dive into the code. My next task is to implement the `evaluate_aux` function within `auxiliary_metrics.py`. This is where the real analytical work begins. I'll need to crack open `extra.npz` to extract the crucial `centers` and `radii` data. These parameters are fundamental; they'll serve as the foundation for the upcoming analysis. It’s all about the details; they underpin the whole operation.\n", "signature": null}], "tool_calls": [{"id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"command\": \"str_replace\", \"old_str\": \"def evaluate_aux(results_dir, primary_result=None):\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    return {}\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"new_str\": \"import numpy as np\\nimport os\\nfrom typing import Dict, Any\\n\\ndef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    metrics = {}\\n    try:\\n        extra_file_path = os.path.join(results_dir, \\\"extra.npz\\\")\\n        if not os.path.exists(extra_file_path):\\n            print(f\\\"Warning: extra.npz not found at {extra_file_path}\\\")\\n            return metrics\\n\\n        data = np.load(extra_file_path)\\n        centers = data[\\\"centers\\\"]\\n        radii = data[\\\"radii\\\"]\\n\\n        num_circles = centers.shape[0]\\n        \\n        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\\n        total_overlap_distance = 0.0\\n        max_overlap_distance = 0.0\\n        num_overlapping_pairs = 0\\n\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    overlap = min_dist_for_no_overlap - dist\\n                    total_overlap_distance += overlap\\n                    if overlap > max_overlap_distance:\\n                        max_overlap_distance = overlap\\n                    num_overlapping_pairs += 1\\n\\n        metrics[\\\"aux_total_overlap_distance\\\"] = float(total_overlap_distance)\\n        metrics[\\\"aux_max_overlap_distance\\\"] = float(max_overlap_distance)\\n        \\n        # --- aux_boundary_violation_distance ---\\n        boundary_violation_distance = 0.0\\n        \\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n\\n            # Check x-bounds\\n            if x - r < 0:\\n                boundary_violation_distance += abs(x - r)\\n            if x + r > 1:\\n                boundary_violation_distance += abs(x + r - 1)\\n            \\n            # Check y-bounds\\n            if y - r < 0:\\n                boundary_violation_distance += abs(y - r)\\n            if y + r > 1:\\n                boundary_violation_distance += abs(y + r - 1)\\n                \\n        metrics[\\\"aux_boundary_violation_distance\\\"] = float(boundary_violation_distance)\\n\\n        # --- aux_min_radius ---\\n        metrics[\\\"aux_min_radius\\\"] = float(np.min(radii)) if radii.size > 0 else 0.0\\n        \\n        # --- aux_num_violated_circles ---\\n        num_violated_circles = 0\\n        violated_circles_indices = set()\\n\\n        # Check for overlaps (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    violated_circles_indices.add(i)\\n                    violated_circles_indices.add(j)\\n\\n        # Check for boundary violations (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\\n                violated_circles_indices.add(i)\\n                \\n        metrics[\\\"aux_num_violated_circles\\\"] = len(violated_circles_indices)\\n\\n\\n    except Exception as e:\\n        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        # Return default 0.0 for all metrics in case of failure\\n        metrics[\\\"aux_total_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_max_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_boundary_violation_distance\\\"] = 0.0\\n        metrics[\\\"aux_min_radius\\\"] = 0.0\\n        metrics[\\\"aux_num_violated_circles\\\"] = 0\\n        \\n    return metrics\\n\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"]\n    17\t\n    18\t        num_circles = centers.shape[0]\n    19\t        \n    20\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    21\t        total_overlap_distance = 0.0\n    22\t        max_overlap_distance = 0.0\n    23\t        num_overlapping_pairs = 0\n    24\t\n    25\t        for i in range(num_circles):\n    26\t            for j in range(i + 1, num_circles):\n    27\t                dist = np.linalg.norm(centers[i] - centers[j])\n    28\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    29\t                if dist < min_dist_for_no_overlap:\n    30\t                    overlap = min_dist_for_no_overlap - dist\n    31\t                    total_overlap_distance += overlap\n    32\t                    if overlap > max_overlap_distance:\n    33\t                        max_overlap_distance = overlap\n    34\t                    num_overlapping_pairs += 1\n    35\t\n    36\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    37\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    38\t        \n    39\t        # --- aux_boundary_violation_distance ---\n    40\t        boundary_violation_distance = 0.0\n    41\t        \n    42\t        for i in range(num_circles):\n    43\t            x, y = centers[i]\n    44\t            r = radii[i]\n    45\t\n    46\t            # Check x-bounds\n    47\t            if x - r < 0:\n    48\t                boundary_violation_distance += abs(x - r)\n    49\t            if x + r > 1:\n    50\t                boundary_violation_distance += abs(x + r - 1)\n    51\t            \n    52\t            # Check y-bounds\n    53\t            if y - r < 0:\n    54\t                boundary_violation_distance += abs(y - r)\n    55\t            if y + r > 1:\n    56\t                boundary_violation_distance += abs(y + r - 1)\n    57\t                \n    58\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    59\t\n    60\t        # --- aux_min_radius ---\n    61\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    62\t        \n    63\t        # --- aux_num_violated_circles ---\n    64\t        num_violated_circles = 0\n    65\t        violated_circles_indices = set()\n    66\t\n    67\t        # Check for overlaps (re-do from above to get specific circles)\n    68\t        for i in range(num_circles):\n    69\t            for j in range(i + 1, num_circles):\n    70\t                dist = np.linalg.norm(centers[i] - centers[j])\n    71\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    72\t                if dist < min_dist_for_no_overlap:\n    73\t                    violated_circles_indices.add(i)\n    74\t                    violated_circles_indices.add(j)\n    75\t\n    76\t        # Check for boundary violations (re-do from above to get specific circles)\n    77\t        for i in range(num_circles):\n    78\t            x, y = centers[i]\n    79\t            r = radii[i]\n    80\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    81\t                violated_circles_indices.add(i)\n    82\t                \n    83\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    84\t\n    85\t\n    86\t    except Exception as e:\n    87\t        print(f\"Error in evaluate_aux: {e}\")\n    88\t        # Return default 0.0 for all metrics in case of failure\n    89\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    90\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    91\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    92\t        metrics[\"aux_min_radius\"] = 0.0\n    93\t        metrics[\"aux_num_violated_circles\"] = 0\n    94\t        \n    95\t    return metrics\n    96\t\n    97\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "-bKfaY_eN4r0694P6Y_ImAo", "created": 1772073721, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "malformed_function_call", "index": 0, "message": {"role": "assistant"}}], "usage": {"completion_tokens": 0, "prompt_tokens": 13285, "total_tokens": 13285, "prompt_tokens_details": {"text_tokens": 13285}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0039854999999999995, "timestamp": 1772073728.9276693, "latency_sec": 7.0309295654296875, "usage_summary": {"prompt_tokens": 13285, "completion_tokens": 0, "reasoning_tokens": 0, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073732.624-f993.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": 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"type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__CsgVAY89a18JKxNqYuyMWnFRF4tiIexCucOwvORuKQb531qK+Xr63p094q7yUSWBZVs+5DAZvLL9J6ngP+SUrpzAlJxmoupeIyW41LbWmh9XKKIvdy0Jj2/+KrOkdHPv6yqWTSw6xtqMkO3CbJ5oo2tbvCFa0Mp5Q8RqIA9hWGyUrBWkkZun/9jkMNQE0jNsN4+oMpFY7n+NHt+5xGODqzPxJS6W7dfBNWz2Co4SjjfrVoRqQ3JsRxz0wnaV24Qa+RBlBDdg+AJLVCy2OdqUtRz1LUkomLKxG7CSNyGdmAlH5FCreoKXXYJaUML2o8r0ZdEQhsBft4hsRkqS7WTEywFcPL2lTgAa1+Ne2YW+9Nonh14LaLqfKT37fgpGM4K6kYQJozDfqHgfkCpm043FBm5Gbkh7FMvP0Oe3Wmr5PJx777VbUvTB443zRwmXbuhXc5JmSzPkm1nK0y6QfUC19aglV9gw9iMOZTxF5Fe1eKTmXsz7d+qzgvNpx76oCABu1riqidb+3RTsatRDWeetzsmDuZZFghKHgmADOacmP4QTTttLT5HIiaIU7ic704AcOqzqkmnatfIrAHHqNZGeWn12IrDiGS/t4fsoysVXFRjJJQscSWqc6Y7f0ELMmMk9ca36dz+xGRcakGvEmA+m7kx1fpv+ATBXZjkE75XW6hCTYUTmCaE9sJzdfyxXuONbQgj+p+O62cOHINop7zQ9dcqx5hOIQQ+o8+kYGkvQgWap43u4zXRdYTkrRBmvWzAojQUIDZKaPrVOeZKgozRSxQY0W8aWC0XMRr6hQvqzt2FyGUQ5DPH2BhCEyglgNFric/k0L4dSdLmTksCzA+6CgdbzApXBx74AB3mYYg7wVfqP2Sx5otX6n/tbT4W6DuH/ZWDIx+C4SpcP/4AurVrH3+LpGvmVwdFvQnU85IOTxDrJ7uo3+z5G61qtIblsMdhP3dcjVd4KWaJtJg3paKoi3jGyzTcAaMg5NBmR5RewGjOu7F1uoz+jyv4pMmYZ9gaCEkKIBpO3eaX2TqRJ5aN4w55gxkcRySofibnyWxAEsqGIbHBj/oGDeb7Q4iDw33Hljlcqe5MWaXGHTs925r6qJG+a+PvPv/Ev2ggjgRUYdCSzINEoQanMkCMc9yiM3HmDSpU5sAoQG9vOdW2zB+pU3iJ9v/bQoCMlLdajhoJDFQFJZO2cBWS+c+W3mzx5sxl7BFwvw4VkOn8mG4QukAzAGvx5hDI7sdnO8JNiY86cr6J71/wa7nMfz3vvy+z2vnKuFfBFfxoQ4sFn0T7Gz1NLxJPvNpacdK84hfylxBHIw50zCmqoyRSThOIJmZvRbKpWHno0GcOk9YX807E458NdtztETByeyUyyBFNZjDnyuqsrjm/wgLU+PZspXALP7mr8qICrrWlH1lH4YBa8RioVcp88Mx4trNItuuAQQm2T/NbiwqryOFjtKHzV12Oq010fCTD7MTrnoAJW9ltn/El3IwwBpjszLSsoPHYFScP1+d2j4Hpm7phbsXD8tepd1rZjKd/vc66y2ukq2yhxXWgv87N6DGWW0zpy+hGOFtiyyr98pRaUiQjogzLxZDwGkWt9fVaJC/8z0V1bDlTtr0UkR+YSNGYq4Eyr627RECeA/D9HmUN/ZxyQKjvltTrxajdQQCLXE9TvFXpetUgEf/jaMtOEGKuGEUyUuxm50w6CVxsHs852GFqzXSR9cv9+QNZwVWC25yzsX+lAht+kdwoHRwQN0oCQmypMDG8SYJPnHoKh5kv38ubO1sVgANL5YDWpDvyiAAgEV1YYKetdT5RBpqgamK7mI3wKu9p0GGVtGokSg+ydu6ipzeN+3viWkAQ4uSK8cMzn1PtPuUloX7/jei+04g6wgjhTLQOEAbdwwpZMnCsIsHAll7uP6nAVML267hZscp7TLxWbMTYDCu4VosJAjkOUqwfogVzgASYjMBM0fCqbazqUcuC7HHMHs86IDwkoXQqXEHA230lM9askAqJiJZ1SeH41AK478r7lIwwu9nmnWahMGdZq8utNX7firXkfnm2GhJbD58kampTG2jAg67873lXyzkZ2M9RIAqCFK3ncuTOJ1rPEgDfYDGg0nu2xsdu4lD/fiUXhxeI4MfrOWj/9mjcATgi9he7WvMTYj8f+vqRnisk654vovrlbLA0triOs5YdkXmG2I/vyLvV0pbSoVwmfm2wJN9FxZtLK4rh7dcdzd0G65aXFyohzIGsblOP9G5yFmJdbtlPTUFmZRzi/5s4KL6f8WmiElob8IpXHScxDSr3jUZXetH0qoAzUYOiMXDS8kQK4Bur69rVxMV0mCC3rOdvQ0UtS2Y1M7f22jmW38kQAgQgxTyfNNAAEa6yoPf1C5UyCNxGajPQKCL/C2jfVFII9H9dLKfCZ3l1tTV62blQQrZWTZjr8D8NaMu8W3WSeG8MwL2F1y6FvFLStDvvwJFRJXQPsO+wuOPn0Oz/WFR1P7AYF47111rwuvLdm2CWu9y0UzMIZOcv4tsde9HcQJueVMKlj0RogeA2bSiEWWzwpSQIuBI3yMar8ceYbtdThDUWhSuQeTrUw1E6hKRnjB60+7d0B3OsdjcvLLxSCR8rm9VdCYGweBFvYshqXqbFzzpVguu6mRSPETeqPKuPmtLto0ahhwb9RLF3fuaX1KVGGF4Km6o1zi3ZsbBmX1NloEVp93E7iGo/Po3DSHqi0YSyJnPxhnvjDuzYS6I3s3/hZ/GGXteZqdu+6wM9SemXrN5VpaO1k49S6dwu1oNX33vsWiCAbzH7203xVkJby7/GSRUGHBrwr2xFw74dJJIJk2LCc1NvyYP0aDVvThTfFmQWzExLUijzJLU38EgSa/XrlSliEo7f+E6rY8z4xtzJaTcxQaBx2baLUlZP5zfCUrx9cUMc3VC7bZoRcOVBaePuBa7V8waObhAcmwt72D+sA9a95G7EThdfM5I3lEFP1Uv5AK2qRQyARM8zctKu3cwnKsH5r/H9Z8ltSXuNGuvkcbVYkMdyJUfpug7dRt0LLPICjtYC57I2AJWSO5uqOiuci57EkNLYsqYM3w6cb/pheqoCDL905eQbybN7itVMDwAGDrteDFWhwvjFluBW7m6f/jJy6r4WRmOADMbs5vZTFB7RFRiTSKx3cmjQJtAvYYx3y4zcPgcOm1INpq+7egfBZuCjGDKCNt/oJVstBwnpdiNvTinNwknVnrAGYmI5pHaOcO24meKGEMu0K+U8i0n1XXTFq3hT18jyEeQBiJ6YwalT8plmOwdSk6/JgYwqRkKuR7OldSz7CCuX7475a2WTaL8PDyjIserwVg9B9m//yTvbEOCf8RSeY1X7JDjIkCfbp6kjuIXaV2EwJ68FBXtcehnbXqrmNbzzinNUwL4QB0Dsk9kokbfMLwkpmaN0WXSToQYle8Ai3EKFTb4v5YERsUSWUXhCiWjLU6ED3sq+QWe9Mrej3c812Gnxggu7HuxhVf6/eC/0yj2YBXNDb72FIoZOJPrA0pf7nL9ndb8hjbcxFnwNkKQTvISz1TGl65i77I077bMO5oj/oviwPgt34XHjDOirqhT0cDDwiH6GZoCS1/uzFCc1P1vI409SK0zUbCHvckxPGcOC71sLwRfGTrprmyGv1q6i9p95o9yrhzs7/Vt/KmG3x5gFegmbCrg1r", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "signature": null}], "tool_calls": [{"id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\tdef evaluate_aux(results_dir, primary_result=None):\n     2\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     3\t    return {}\n     4\t\n"}], "role": "tool", "tool_call_id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n", "signature": null}], "tool_calls": [{"id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"summary\": \"View evaluate_ori.py to understand the primary evaluation metric.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py:\n     1\t\"\"\"\n     2\tEvaluator for circle packing example (n=26) with improved timeout handling\n     3\t\"\"\"\n     4\t\n     5\timport os\n     6\timport argparse\n     7\timport sys\n     8\timport numpy as np\n     9\tfrom typing import Tuple, Optional, List, Dict, Any\n    10\tfrom pathlib import Path\n    11\t\n    12\t# Add project root to import path for direct file execution.\n    13\tsys.path.insert(0, str(Path(__file__).resolve().parents[2]))\n    14\t\n    15\tfrom shinka.core import run_shinka_eval\n    16\t\n    17\t\n    18\tdef format_centers_string(centers: np.ndarray) -> str:\n    19\t    \"\"\"Formats circle centers into a multi-line string for display.\"\"\"\n    20\t    return \"\\n\".join(\n    21\t        [\n    22\t            f\"  centers[{i}] = ({x_coord:.4f}, {y_coord:.4f})\"\n    23\t            for i, (x_coord, y_coord) in enumerate(centers)\n    24\t        ]\n    25\t    )\n    26\t\n    27\t\n    28\tdef adapted_validate_packing(\n    29\t    run_output: Tuple[np.ndarray, np.ndarray, float],\n    30\t    atol=1e-6,\n    31\t) -> Tuple[bool, Optional[str]]:\n    32\t    \"\"\"\n    33\t    Validates circle packing results based on the output of 'run_packing'.\n    34\t\n    35\t    Args:\n    36\t        run_output: Tuple (centers, radii, reported_sum) from run_packing.\n    37\t\n    38\t    Returns:\n    39\t        (is_valid: bool, error_message: Optional[str])\n    40\t    \"\"\"\n    41\t    centers, radii, reported_sum = run_output\n    42\t    msg = \"The circles are placed correctly. There are no overlaps or any circles outside the unit square.\"\n    43\t    if not isinstance(centers, np.ndarray):\n    44\t        centers = np.array(centers)\n    45\t    if not isinstance(radii, np.ndarray):\n    46\t        radii = np.array(radii)\n    47\t\n    48\t    n_expected = 26\n    49\t    if centers.shape != (n_expected, 2):\n    50\t        msg = (\n    51\t            f\"Centers shape incorrect. Expected ({n_expected}, 2), got {centers.shape}\"\n    52\t        )\n    53\t        return False, msg\n    54\t    if radii.shape != (n_expected,):\n    55\t        msg = f\"Radii shape incorrect. Expected ({n_expected},), got {radii.shape}\"\n    56\t        return False, msg\n    57\t\n    58\t    if np.any(radii < 0):\n    59\t        negative_indices = np.where(radii < 0)[0]\n    60\t        msg = f\"Negative radii found for circles at indices: {negative_indices}\"\n    61\t        return False, msg\n    62\t\n    63\t    if not np.isclose(np.sum(radii), reported_sum, atol=atol):\n    64\t        msg = (\n    65\t            f\"Sum of radii ({np.sum(radii):.6f}) does not match \"\n    66\t            f\"reported ({reported_sum:.6f})\"\n    67\t        )\n    68\t        return False, msg\n    69\t\n    70\t    for i in range(n_expected):\n    71\t        x, y = centers[i]\n    72\t        r = radii[i]\n    73\t        is_outside = (\n    74\t            x - r < -atol or x + r > 1 + atol or y - r < -atol or y + r > 1 + atol\n    75\t        )\n    76\t        if is_outside:\n    77\t            msg = (\n    78\t                f\"Circle {i} (x={x:.4f}, y={y:.4f}, r={r:.4f}) is outside unit square.\"\n    79\t            )\n    80\t            return False, msg\n    81\t\n    82\t    for i in range(n_expected):\n    83\t        for j in range(i + 1, n_expected):\n    84\t            dist = np.sqrt(np.sum((centers[i] - centers[j]) ** 2))\n    85\t            if dist < radii[i] + radii[j] - atol:\n    86\t                msg = (\n    87\t                    f\"Circles {i} & {j} overlap. Dist: {dist:.4f}, \"\n    88\t                    f\"Sum Radii: {(radii[i] + radii[j]):.4f}\"\n    89\t                )\n    90\t                return False, msg\n    91\t    return True, msg\n    92\t\n    93\t\n    94\tdef get_circle_packing_kwargs(run_index: int) -> Dict[str, Any]:\n    95\t    \"\"\"Provides keyword arguments for circle packing runs (none needed).\"\"\"\n    96\t    return {}\n    97\t\n    98\t\n    99\tdef aggregate_circle_packing_metrics(\n   100\t    results: List[Tuple[np.ndarray, np.ndarray, float]], results_dir: str\n   101\t) -> Dict[str, Any]:\n   102\t    \"\"\"\n   103\t    Aggregates metrics for circle packing. Assumes num_runs=1.\n   104\t    Saves extra.npz with detailed packing information.\n   105\t    \"\"\"\n   106\t    if not results:\n   107\t        return {\"combined_score\": 0.0, \"error\": \"No results to aggregate\"}\n   108\t\n   109\t    centers, radii, reported_sum = results[0]\n   110\t\n   111\t    public_metrics = {\n   112\t        \"centers_str\": format_centers_string(centers),\n   113\t        \"num_circles\": centers.shape[0],\n   114\t    }\n   115\t    private_metrics = {\n   116\t        \"reported_sum_of_radii\": float(reported_sum),\n   117\t    }\n   118\t    metrics = {\n   119\t        \"combined_score\": float(reported_sum),\n   120\t        \"public\": public_metrics,\n   121\t        \"private\": private_metrics,\n   122\t    }\n   123\t\n   124\t    extra_file = os.path.join(results_dir, \"extra.npz\")\n   125\t    try:\n   126\t        np.savez(\n   127\t            extra_file,\n   128\t            centers=centers,\n   129\t            radii=radii,\n   130\t            reported_sum=reported_sum,\n   131\t        )\n   132\t        print(f\"Detailed packing data saved to {extra_file}\")\n   133\t    except Exception as e:\n   134\t        print(f\"Error saving extra.npz: {e}\")\n   135\t        metrics[\"extra_npz_save_error\"] = str(e)\n   136\t\n   137\t    return metrics\n   138\t\n   139\t\n   140\tdef main(program_path: str, results_dir: str):\n   141\t    \"\"\"Runs the circle packing evaluation using shinka.eval.\"\"\"\n   142\t    print(f\"Evaluating program: {program_path}\")\n   143\t    print(f\"Saving results to: {results_dir}\")\n   144\t    os.makedirs(results_dir, exist_ok=True)\n   145\t\n   146\t    num_experiment_runs = 1\n   147\t\n   148\t    # Define a nested function to pass results_dir to the aggregator\n   149\t    def _aggregator_with_context(\n   150\t        r: List[Tuple[np.ndarray, np.ndarray, float]],\n   151\t    ) -> Dict[str, Any]:\n   152\t        return aggregate_circle_packing_metrics(r, results_dir)\n   153\t\n   154\t    metrics, correct, error_msg = run_shinka_eval(\n   155\t        program_path=program_path,\n   156\t        results_dir=results_dir,\n   157\t        experiment_fn_name=\"run_packing\",\n   158\t        num_runs=num_experiment_runs,\n   159\t        get_experiment_kwargs=get_circle_packing_kwargs,\n   160\t        validate_fn=adapted_validate_packing,\n   161\t        aggregate_metrics_fn=_aggregator_with_context,\n   162\t    )\n   163\t\n   164\t    if correct:\n   165\t        print(\"Evaluation and Validation completed successfully.\")\n   166\t    else:\n   167\t        print(f\"Evaluation or Validation failed: {error_msg}\")\n   168\t\n   169\t    print(\"Metrics:\")\n   170\t    for key, value in metrics.items():\n   171\t        if isinstance(value, str) and len(value) > 100:\n   172\t            print(f\"  {key}: <string_too_long_to_display>\")\n   173\t        else:\n   174\t            print(f\"  {key}: {value}\")\n   175\t\n   176\t\n   177\tif __name__ == \"__main__\":\n   178\t    parser = argparse.ArgumentParser(\n   179\t        description=\"Circle packing evaluator using shinka.eval\"\n   180\t    )\n   181\t    parser.add_argument(\n   182\t        \"--program_path\",\n   183\t        type=str,\n   184\t        default=\"tasks/circle_packing/initial.py\",\n   185\t        help=\"Path to program to evaluate (must contain 'run_packing')\",\n   186\t    )\n   187\t    parser.add_argument(\n   188\t        \"--results_dir\",\n   189\t        type=str,\n   190\t        default=\"tasks/circle_packing/results/debug_eval\",\n   191\t        help=\"Dir to save results (metrics.json, correct.json, extra.npz)\",\n   192\t    )\n   193\t    parsed_args = parser.parse_args()\n   194\t    main(parsed_args.program_path, parsed_args.results_dir)\n   195\t\n"}], "role": "tool", "tool_call_id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking. I'm looking at `evaluate_ori.py` and immediately see the core problem: packing 26 circles within a unit square. The goal, as judged by the `combined_score`, is to maximize the sum of the radii.\n\nThe key constraints are no negative radii, all circles inside the square, and no overlaps. The `adapted_validate_packing` function is the gatekeeper here, ensuring these constraints are met. A problem in Gen 4 has become apparent, with a dramatic score drop from Gen 3. The `combined_score` has significantly decreased, indicating something is seriously wrong with the circle packing in Gen 4.\n\nI need to understand why. My initial hypothesis is that either the constraints are being violated (overlaps or boundary violations) or the radii of the circles have shrunk dramatically, causing a lower total. I'll need to dig into this by analyzing the `primary_result` and `main.py` files for Gen 4. It will be helpful to find out where the `extra.npz` files are being saved, since that's where the critical `centers` and `radii` data are stored. The `results_dir` is the place to start.\n\nTo help diagnose this, I need to create some auxiliary metrics. I need to define them in `EVAL_AGENTS.md` since it's currently empty, and then write the code in `auxiliary_metrics.py`.\n\nThe key will be to confirm constraint violations:\n\n*   I'll calculate `aux_total_overlap_distance` and `aux_max_overlap_distance` to quantify the overlap problem.\n*   `aux_boundary_violation_distance` to measure how much circles are going outside the square.\n*   `aux_min_radius` to see if the radii are becoming suspiciously small.\n*   `aux_num_violated_circles`, a simple count of constraint violations.\n\nEssentially, I'm now in a diagnosis phase – either `EXPLORATION` or `OPTIMIZATION` -- where I am trying to figure out why the packing quality has regressed so significantly. The next step is to update `auxiliary_metrics.py` based on this analysis.\n", "signature": null}], "tool_calls": [{"id": "call_93797a325ec240039ed95cf3c33e__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"file_text\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"create\"}"}}]}, {"content": [{"type": "text", "text": "[An error occurred during execution.]\n"}, {"type": "text", "text": "Invalid `path` parameter: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. File already exists at: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. Cannot overwrite files using command `create`."}], "role": "tool", "tool_call_id": "call_93797a325ec240039ed95cf3c33e__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Right, a slip of the mind, completely my fault. I got ahead of myself and forgot that the system's already laid the groundwork with `EVAL_AGENTS.md`, even if it's currently a blank slate. Silly me, I should have been using `str_replace` or something similar for the update, not a fresh `create`. \n\nOkay, no problem. I'll utilize `str_replace` now. That's the correct way to inject the relevant registry and generation log into that pre-existing file. Time to make sure the initial content is correctly appended.\n", "signature": null}], "tool_calls": [{"id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"str_replace\", \"old_str\": \"# EV2 Agent Memory\\n\\n- Initialized by eval service.\\n- Use this file as compact cross-generation memory.\\n\", \"new_str\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, now that I've meticulously updated `EVAL_AGENTS.md`, ensuring all the agent evaluation criteria are up to snuff, it's time to dive into the code. My next task is to implement the `evaluate_aux` function within `auxiliary_metrics.py`. This is where the real analytical work begins. I'll need to crack open `extra.npz` to extract the crucial `centers` and `radii` data. These parameters are fundamental; they'll serve as the foundation for the upcoming analysis. It’s all about the details; they underpin the whole operation.\n", "signature": null}], "tool_calls": [{"id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"command\": \"str_replace\", \"old_str\": \"def evaluate_aux(results_dir, primary_result=None):\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    return {}\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"new_str\": \"import numpy as np\\nimport os\\nfrom typing import Dict, Any\\n\\ndef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    metrics = {}\\n    try:\\n        extra_file_path = os.path.join(results_dir, \\\"extra.npz\\\")\\n        if not os.path.exists(extra_file_path):\\n            print(f\\\"Warning: extra.npz not found at {extra_file_path}\\\")\\n            return metrics\\n\\n        data = np.load(extra_file_path)\\n        centers = data[\\\"centers\\\"]\\n        radii = data[\\\"radii\\\"]\\n\\n        num_circles = centers.shape[0]\\n        \\n        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\\n        total_overlap_distance = 0.0\\n        max_overlap_distance = 0.0\\n        num_overlapping_pairs = 0\\n\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    overlap = min_dist_for_no_overlap - dist\\n                    total_overlap_distance += overlap\\n                    if overlap > max_overlap_distance:\\n                        max_overlap_distance = overlap\\n                    num_overlapping_pairs += 1\\n\\n        metrics[\\\"aux_total_overlap_distance\\\"] = float(total_overlap_distance)\\n        metrics[\\\"aux_max_overlap_distance\\\"] = float(max_overlap_distance)\\n        \\n        # --- aux_boundary_violation_distance ---\\n        boundary_violation_distance = 0.0\\n        \\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n\\n            # Check x-bounds\\n            if x - r < 0:\\n                boundary_violation_distance += abs(x - r)\\n            if x + r > 1:\\n                boundary_violation_distance += abs(x + r - 1)\\n            \\n            # Check y-bounds\\n            if y - r < 0:\\n                boundary_violation_distance += abs(y - r)\\n            if y + r > 1:\\n                boundary_violation_distance += abs(y + r - 1)\\n                \\n        metrics[\\\"aux_boundary_violation_distance\\\"] = float(boundary_violation_distance)\\n\\n        # --- aux_min_radius ---\\n        metrics[\\\"aux_min_radius\\\"] = float(np.min(radii)) if radii.size > 0 else 0.0\\n        \\n        # --- aux_num_violated_circles ---\\n        num_violated_circles = 0\\n        violated_circles_indices = set()\\n\\n        # Check for overlaps (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    violated_circles_indices.add(i)\\n                    violated_circles_indices.add(j)\\n\\n        # Check for boundary violations (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\\n                violated_circles_indices.add(i)\\n                \\n        metrics[\\\"aux_num_violated_circles\\\"] = len(violated_circles_indices)\\n\\n\\n    except Exception as e:\\n        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        # Return default 0.0 for all metrics in case of failure\\n        metrics[\\\"aux_total_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_max_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_boundary_violation_distance\\\"] = 0.0\\n        metrics[\\\"aux_min_radius\\\"] = 0.0\\n        metrics[\\\"aux_num_violated_circles\\\"] = 0\\n        \\n    return metrics\\n\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"]\n    17\t\n    18\t        num_circles = centers.shape[0]\n    19\t        \n    20\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    21\t        total_overlap_distance = 0.0\n    22\t        max_overlap_distance = 0.0\n    23\t        num_overlapping_pairs = 0\n    24\t\n    25\t        for i in range(num_circles):\n    26\t            for j in range(i + 1, num_circles):\n    27\t                dist = np.linalg.norm(centers[i] - centers[j])\n    28\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    29\t                if dist < min_dist_for_no_overlap:\n    30\t                    overlap = min_dist_for_no_overlap - dist\n    31\t                    total_overlap_distance += overlap\n    32\t                    if overlap > max_overlap_distance:\n    33\t                        max_overlap_distance = overlap\n    34\t                    num_overlapping_pairs += 1\n    35\t\n    36\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    37\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    38\t        \n    39\t        # --- aux_boundary_violation_distance ---\n    40\t        boundary_violation_distance = 0.0\n    41\t        \n    42\t        for i in range(num_circles):\n    43\t            x, y = centers[i]\n    44\t            r = radii[i]\n    45\t\n    46\t            # Check x-bounds\n    47\t            if x - r < 0:\n    48\t                boundary_violation_distance += abs(x - r)\n    49\t            if x + r > 1:\n    50\t                boundary_violation_distance += abs(x + r - 1)\n    51\t            \n    52\t            # Check y-bounds\n    53\t            if y - r < 0:\n    54\t                boundary_violation_distance += abs(y - r)\n    55\t            if y + r > 1:\n    56\t                boundary_violation_distance += abs(y + r - 1)\n    57\t                \n    58\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    59\t\n    60\t        # --- aux_min_radius ---\n    61\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    62\t        \n    63\t        # --- aux_num_violated_circles ---\n    64\t        num_violated_circles = 0\n    65\t        violated_circles_indices = set()\n    66\t\n    67\t        # Check for overlaps (re-do from above to get specific circles)\n    68\t        for i in range(num_circles):\n    69\t            for j in range(i + 1, num_circles):\n    70\t                dist = np.linalg.norm(centers[i] - centers[j])\n    71\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    72\t                if dist < min_dist_for_no_overlap:\n    73\t                    violated_circles_indices.add(i)\n    74\t                    violated_circles_indices.add(j)\n    75\t\n    76\t        # Check for boundary violations (re-do from above to get specific circles)\n    77\t        for i in range(num_circles):\n    78\t            x, y = centers[i]\n    79\t            r = radii[i]\n    80\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    81\t                violated_circles_indices.add(i)\n    82\t                \n    83\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    84\t\n    85\t\n    86\t    except Exception as e:\n    87\t        print(f\"Error in evaluate_aux: {e}\")\n    88\t        # Return default 0.0 for all metrics in case of failure\n    89\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    90\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    91\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    92\t        metrics[\"aux_min_radius\"] = 0.0\n    93\t        metrics[\"aux_num_violated_circles\"] = 0\n    94\t        \n    95\t    return metrics\n    96\t\n    97\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "name": "file_editor"}, {"content": [], "role": "assistant"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "AbOfaZUUh5bi8Q_-1uShCQ", "created": 1772073728, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv"}, "function": {"arguments": "{\"security_risk\": \"LOW\", \"command\": \"python3 -c 'from eval_agent.tool_box.api import call_tool; import os; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))'\", \"summary\": \"Smoke test auxiliary_metrics.py for gen_4.\"}", "name": "terminal"}, "id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "type": "function"}], "images": [], "reasoning_content": "Okay, here's what I'm thinking:\n\nAlright, that `auxiliary_metrics.py` script is finally updated with the new metrics. Now, the real work begins. I need to verify that it's robust and actually *works* with the current data. I can't just assume it'll run flawlessly, especially after adding new calculations.\n\nSo, I'll leverage that `call_tool` function to trigger `evaluate_aux`. That's the most efficient way to get it running. First things first though; I need to get the `results_dir` for `gen_4`. That's where the data for this generation of the project lives. Then, I'll be able to run `evaluate_aux` and confirm that everything is behaving as expected. No errors, and, more importantly, the output needs to be valid. The integrity of the results is paramount.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking:\n\nAlright, that `auxiliary_metrics.py` script is finally updated with the new metrics. Now, the real work begins. I need to verify that it's robust and actually *works* with the current data. I can't just assume it'll run flawlessly, especially after adding new calculations.\n\nSo, I'll leverage that `call_tool` function to trigger `evaluate_aux`. That's the most efficient way to get it running. First things first though; I need to get the `results_dir` for `gen_4`. That's where the data for this generation of the project lives. Then, I'll be able to run `evaluate_aux` and confirm that everything is behaving as expected. No errors, and, more importantly, the output needs to be valid. The integrity of the results is paramount.\n"}], "provider_specific_fields": {"thought_signatures": ["CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv"]}}}], "usage": {"completion_tokens": 206, "prompt_tokens": 14940, "total_tokens": 15146, "completion_tokens_details": {"reasoning_tokens": 76, "text_tokens": 130}, "prompt_tokens_details": {"text_tokens": 14940}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.004997, "timestamp": 1772073732.6245155, "latency_sec": 3.6693854331970215, "usage_summary": {"prompt_tokens": 14940, "completion_tokens": 206, "reasoning_tokens": 76, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073737.873-d10d.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": "call_ad172c287e414c36a7999f9b4215__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "signature": null}], "tool_calls": [{"id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\tdef evaluate_aux(results_dir, primary_result=None):\n     2\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     3\t    return {}\n     4\t\n"}], "role": "tool", "tool_call_id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n", "signature": null}], "tool_calls": [{"id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"summary\": \"View evaluate_ori.py to understand the primary evaluation metric.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py:\n     1\t\"\"\"\n     2\tEvaluator for circle packing example (n=26) with improved timeout handling\n     3\t\"\"\"\n     4\t\n     5\timport os\n     6\timport argparse\n     7\timport sys\n     8\timport numpy as np\n     9\tfrom typing import Tuple, Optional, List, Dict, Any\n    10\tfrom pathlib import Path\n    11\t\n    12\t# Add project root to import path for direct file execution.\n    13\tsys.path.insert(0, str(Path(__file__).resolve().parents[2]))\n    14\t\n    15\tfrom shinka.core import run_shinka_eval\n    16\t\n    17\t\n    18\tdef format_centers_string(centers: np.ndarray) -> str:\n    19\t    \"\"\"Formats circle centers into a multi-line string for display.\"\"\"\n    20\t    return \"\\n\".join(\n    21\t        [\n    22\t            f\"  centers[{i}] = ({x_coord:.4f}, {y_coord:.4f})\"\n    23\t            for i, (x_coord, y_coord) in enumerate(centers)\n    24\t        ]\n    25\t    )\n    26\t\n    27\t\n    28\tdef adapted_validate_packing(\n    29\t    run_output: Tuple[np.ndarray, np.ndarray, float],\n    30\t    atol=1e-6,\n    31\t) -> Tuple[bool, Optional[str]]:\n    32\t    \"\"\"\n    33\t    Validates circle packing results based on the output of 'run_packing'.\n    34\t\n    35\t    Args:\n    36\t        run_output: Tuple (centers, radii, reported_sum) from run_packing.\n    37\t\n    38\t    Returns:\n    39\t        (is_valid: bool, error_message: Optional[str])\n    40\t    \"\"\"\n    41\t    centers, radii, reported_sum = run_output\n    42\t    msg = \"The circles are placed correctly. There are no overlaps or any circles outside the unit square.\"\n    43\t    if not isinstance(centers, np.ndarray):\n    44\t        centers = np.array(centers)\n    45\t    if not isinstance(radii, np.ndarray):\n    46\t        radii = np.array(radii)\n    47\t\n    48\t    n_expected = 26\n    49\t    if centers.shape != (n_expected, 2):\n    50\t        msg = (\n    51\t            f\"Centers shape incorrect. Expected ({n_expected}, 2), got {centers.shape}\"\n    52\t        )\n    53\t        return False, msg\n    54\t    if radii.shape != (n_expected,):\n    55\t        msg = f\"Radii shape incorrect. Expected ({n_expected},), got {radii.shape}\"\n    56\t        return False, msg\n    57\t\n    58\t    if np.any(radii < 0):\n    59\t        negative_indices = np.where(radii < 0)[0]\n    60\t        msg = f\"Negative radii found for circles at indices: {negative_indices}\"\n    61\t        return False, msg\n    62\t\n    63\t    if not np.isclose(np.sum(radii), reported_sum, atol=atol):\n    64\t        msg = (\n    65\t            f\"Sum of radii ({np.sum(radii):.6f}) does not match \"\n    66\t            f\"reported ({reported_sum:.6f})\"\n    67\t        )\n    68\t        return False, msg\n    69\t\n    70\t    for i in range(n_expected):\n    71\t        x, y = centers[i]\n    72\t        r = radii[i]\n    73\t        is_outside = (\n    74\t            x - r < -atol or x + r > 1 + atol or y - r < -atol or y + r > 1 + atol\n    75\t        )\n    76\t        if is_outside:\n    77\t            msg = (\n    78\t                f\"Circle {i} (x={x:.4f}, y={y:.4f}, r={r:.4f}) is outside unit square.\"\n    79\t            )\n    80\t            return False, msg\n    81\t\n    82\t    for i in range(n_expected):\n    83\t        for j in range(i + 1, n_expected):\n    84\t            dist = np.sqrt(np.sum((centers[i] - centers[j]) ** 2))\n    85\t            if dist < radii[i] + radii[j] - atol:\n    86\t                msg = (\n    87\t                    f\"Circles {i} & {j} overlap. Dist: {dist:.4f}, \"\n    88\t                    f\"Sum Radii: {(radii[i] + radii[j]):.4f}\"\n    89\t                )\n    90\t                return False, msg\n    91\t    return True, msg\n    92\t\n    93\t\n    94\tdef get_circle_packing_kwargs(run_index: int) -> Dict[str, Any]:\n    95\t    \"\"\"Provides keyword arguments for circle packing runs (none needed).\"\"\"\n    96\t    return {}\n    97\t\n    98\t\n    99\tdef aggregate_circle_packing_metrics(\n   100\t    results: List[Tuple[np.ndarray, np.ndarray, float]], results_dir: str\n   101\t) -> Dict[str, Any]:\n   102\t    \"\"\"\n   103\t    Aggregates metrics for circle packing. Assumes num_runs=1.\n   104\t    Saves extra.npz with detailed packing information.\n   105\t    \"\"\"\n   106\t    if not results:\n   107\t        return {\"combined_score\": 0.0, \"error\": \"No results to aggregate\"}\n   108\t\n   109\t    centers, radii, reported_sum = results[0]\n   110\t\n   111\t    public_metrics = {\n   112\t        \"centers_str\": format_centers_string(centers),\n   113\t        \"num_circles\": centers.shape[0],\n   114\t    }\n   115\t    private_metrics = {\n   116\t        \"reported_sum_of_radii\": float(reported_sum),\n   117\t    }\n   118\t    metrics = {\n   119\t        \"combined_score\": float(reported_sum),\n   120\t        \"public\": public_metrics,\n   121\t        \"private\": private_metrics,\n   122\t    }\n   123\t\n   124\t    extra_file = os.path.join(results_dir, \"extra.npz\")\n   125\t    try:\n   126\t        np.savez(\n   127\t            extra_file,\n   128\t            centers=centers,\n   129\t            radii=radii,\n   130\t            reported_sum=reported_sum,\n   131\t        )\n   132\t        print(f\"Detailed packing data saved to {extra_file}\")\n   133\t    except Exception as e:\n   134\t        print(f\"Error saving extra.npz: {e}\")\n   135\t        metrics[\"extra_npz_save_error\"] = str(e)\n   136\t\n   137\t    return metrics\n   138\t\n   139\t\n   140\tdef main(program_path: str, results_dir: str):\n   141\t    \"\"\"Runs the circle packing evaluation using shinka.eval.\"\"\"\n   142\t    print(f\"Evaluating program: {program_path}\")\n   143\t    print(f\"Saving results to: {results_dir}\")\n   144\t    os.makedirs(results_dir, exist_ok=True)\n   145\t\n   146\t    num_experiment_runs = 1\n   147\t\n   148\t    # Define a nested function to pass results_dir to the aggregator\n   149\t    def _aggregator_with_context(\n   150\t        r: List[Tuple[np.ndarray, np.ndarray, float]],\n   151\t    ) -> Dict[str, Any]:\n   152\t        return aggregate_circle_packing_metrics(r, results_dir)\n   153\t\n   154\t    metrics, correct, error_msg = run_shinka_eval(\n   155\t        program_path=program_path,\n   156\t        results_dir=results_dir,\n   157\t        experiment_fn_name=\"run_packing\",\n   158\t        num_runs=num_experiment_runs,\n   159\t        get_experiment_kwargs=get_circle_packing_kwargs,\n   160\t        validate_fn=adapted_validate_packing,\n   161\t        aggregate_metrics_fn=_aggregator_with_context,\n   162\t    )\n   163\t\n   164\t    if correct:\n   165\t        print(\"Evaluation and Validation completed successfully.\")\n   166\t    else:\n   167\t        print(f\"Evaluation or Validation failed: {error_msg}\")\n   168\t\n   169\t    print(\"Metrics:\")\n   170\t    for key, value in metrics.items():\n   171\t        if isinstance(value, str) and len(value) > 100:\n   172\t            print(f\"  {key}: <string_too_long_to_display>\")\n   173\t        else:\n   174\t            print(f\"  {key}: {value}\")\n   175\t\n   176\t\n   177\tif __name__ == \"__main__\":\n   178\t    parser = argparse.ArgumentParser(\n   179\t        description=\"Circle packing evaluator using shinka.eval\"\n   180\t    )\n   181\t    parser.add_argument(\n   182\t        \"--program_path\",\n   183\t        type=str,\n   184\t        default=\"tasks/circle_packing/initial.py\",\n   185\t        help=\"Path to program to evaluate (must contain 'run_packing')\",\n   186\t    )\n   187\t    parser.add_argument(\n   188\t        \"--results_dir\",\n   189\t        type=str,\n   190\t        default=\"tasks/circle_packing/results/debug_eval\",\n   191\t        help=\"Dir to save results (metrics.json, correct.json, extra.npz)\",\n   192\t    )\n   193\t    parsed_args = parser.parse_args()\n   194\t    main(parsed_args.program_path, parsed_args.results_dir)\n   195\t\n"}], "role": "tool", "tool_call_id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking. I'm looking at `evaluate_ori.py` and immediately see the core problem: packing 26 circles within a unit square. The goal, as judged by the `combined_score`, is to maximize the sum of the radii.\n\nThe key constraints are no negative radii, all circles inside the square, and no overlaps. The `adapted_validate_packing` function is the gatekeeper here, ensuring these constraints are met. A problem in Gen 4 has become apparent, with a dramatic score drop from Gen 3. The `combined_score` has significantly decreased, indicating something is seriously wrong with the circle packing in Gen 4.\n\nI need to understand why. My initial hypothesis is that either the constraints are being violated (overlaps or boundary violations) or the radii of the circles have shrunk dramatically, causing a lower total. I'll need to dig into this by analyzing the `primary_result` and `main.py` files for Gen 4. It will be helpful to find out where the `extra.npz` files are being saved, since that's where the critical `centers` and `radii` data are stored. The `results_dir` is the place to start.\n\nTo help diagnose this, I need to create some auxiliary metrics. I need to define them in `EVAL_AGENTS.md` since it's currently empty, and then write the code in `auxiliary_metrics.py`.\n\nThe key will be to confirm constraint violations:\n\n*   I'll calculate `aux_total_overlap_distance` and `aux_max_overlap_distance` to quantify the overlap problem.\n*   `aux_boundary_violation_distance` to measure how much circles are going outside the square.\n*   `aux_min_radius` to see if the radii are becoming suspiciously small.\n*   `aux_num_violated_circles`, a simple count of constraint violations.\n\nEssentially, I'm now in a diagnosis phase – either `EXPLORATION` or `OPTIMIZATION` -- where I am trying to figure out why the packing quality has regressed so significantly. The next step is to update `auxiliary_metrics.py` based on this analysis.\n", "signature": null}], "tool_calls": [{"id": "call_93797a325ec240039ed95cf3c33e__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"file_text\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"create\"}"}}]}, {"content": [{"type": "text", "text": "[An error occurred during execution.]\n"}, {"type": "text", "text": "Invalid `path` parameter: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. File already exists at: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. Cannot overwrite files using command `create`."}], "role": "tool", "tool_call_id": "call_93797a325ec240039ed95cf3c33e__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Right, a slip of the mind, completely my fault. I got ahead of myself and forgot that the system's already laid the groundwork with `EVAL_AGENTS.md`, even if it's currently a blank slate. Silly me, I should have been using `str_replace` or something similar for the update, not a fresh `create`. \n\nOkay, no problem. I'll utilize `str_replace` now. That's the correct way to inject the relevant registry and generation log into that pre-existing file. Time to make sure the initial content is correctly appended.\n", "signature": null}], "tool_calls": [{"id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"str_replace\", \"old_str\": \"# EV2 Agent Memory\\n\\n- Initialized by eval service.\\n- Use this file as compact cross-generation memory.\\n\", \"new_str\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, now that I've meticulously updated `EVAL_AGENTS.md`, ensuring all the agent evaluation criteria are up to snuff, it's time to dive into the code. My next task is to implement the `evaluate_aux` function within `auxiliary_metrics.py`. This is where the real analytical work begins. I'll need to crack open `extra.npz` to extract the crucial `centers` and `radii` data. These parameters are fundamental; they'll serve as the foundation for the upcoming analysis. It’s all about the details; they underpin the whole operation.\n", "signature": null}], "tool_calls": [{"id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"command\": \"str_replace\", \"old_str\": \"def evaluate_aux(results_dir, primary_result=None):\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    return {}\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"new_str\": \"import numpy as np\\nimport os\\nfrom typing import Dict, Any\\n\\ndef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    metrics = {}\\n    try:\\n        extra_file_path = os.path.join(results_dir, \\\"extra.npz\\\")\\n        if not os.path.exists(extra_file_path):\\n            print(f\\\"Warning: extra.npz not found at {extra_file_path}\\\")\\n            return metrics\\n\\n        data = np.load(extra_file_path)\\n        centers = data[\\\"centers\\\"]\\n        radii = data[\\\"radii\\\"]\\n\\n        num_circles = centers.shape[0]\\n        \\n        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\\n        total_overlap_distance = 0.0\\n        max_overlap_distance = 0.0\\n        num_overlapping_pairs = 0\\n\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    overlap = min_dist_for_no_overlap - dist\\n                    total_overlap_distance += overlap\\n                    if overlap > max_overlap_distance:\\n                        max_overlap_distance = overlap\\n                    num_overlapping_pairs += 1\\n\\n        metrics[\\\"aux_total_overlap_distance\\\"] = float(total_overlap_distance)\\n        metrics[\\\"aux_max_overlap_distance\\\"] = float(max_overlap_distance)\\n        \\n        # --- aux_boundary_violation_distance ---\\n        boundary_violation_distance = 0.0\\n        \\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n\\n            # Check x-bounds\\n            if x - r < 0:\\n                boundary_violation_distance += abs(x - r)\\n            if x + r > 1:\\n                boundary_violation_distance += abs(x + r - 1)\\n            \\n            # Check y-bounds\\n            if y - r < 0:\\n                boundary_violation_distance += abs(y - r)\\n            if y + r > 1:\\n                boundary_violation_distance += abs(y + r - 1)\\n                \\n        metrics[\\\"aux_boundary_violation_distance\\\"] = float(boundary_violation_distance)\\n\\n        # --- aux_min_radius ---\\n        metrics[\\\"aux_min_radius\\\"] = float(np.min(radii)) if radii.size > 0 else 0.0\\n        \\n        # --- aux_num_violated_circles ---\\n        num_violated_circles = 0\\n        violated_circles_indices = set()\\n\\n        # Check for overlaps (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    violated_circles_indices.add(i)\\n                    violated_circles_indices.add(j)\\n\\n        # Check for boundary violations (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\\n                violated_circles_indices.add(i)\\n                \\n        metrics[\\\"aux_num_violated_circles\\\"] = len(violated_circles_indices)\\n\\n\\n    except Exception as e:\\n        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        # Return default 0.0 for all metrics in case of failure\\n        metrics[\\\"aux_total_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_max_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_boundary_violation_distance\\\"] = 0.0\\n        metrics[\\\"aux_min_radius\\\"] = 0.0\\n        metrics[\\\"aux_num_violated_circles\\\"] = 0\\n        \\n    return metrics\\n\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"]\n    17\t\n    18\t        num_circles = centers.shape[0]\n    19\t        \n    20\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    21\t        total_overlap_distance = 0.0\n    22\t        max_overlap_distance = 0.0\n    23\t        num_overlapping_pairs = 0\n    24\t\n    25\t        for i in range(num_circles):\n    26\t            for j in range(i + 1, num_circles):\n    27\t                dist = np.linalg.norm(centers[i] - centers[j])\n    28\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    29\t                if dist < min_dist_for_no_overlap:\n    30\t                    overlap = min_dist_for_no_overlap - dist\n    31\t                    total_overlap_distance += overlap\n    32\t                    if overlap > max_overlap_distance:\n    33\t                        max_overlap_distance = overlap\n    34\t                    num_overlapping_pairs += 1\n    35\t\n    36\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    37\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    38\t        \n    39\t        # --- aux_boundary_violation_distance ---\n    40\t        boundary_violation_distance = 0.0\n    41\t        \n    42\t        for i in range(num_circles):\n    43\t            x, y = centers[i]\n    44\t            r = radii[i]\n    45\t\n    46\t            # Check x-bounds\n    47\t            if x - r < 0:\n    48\t                boundary_violation_distance += abs(x - r)\n    49\t            if x + r > 1:\n    50\t                boundary_violation_distance += abs(x + r - 1)\n    51\t            \n    52\t            # Check y-bounds\n    53\t            if y - r < 0:\n    54\t                boundary_violation_distance += abs(y - r)\n    55\t            if y + r > 1:\n    56\t                boundary_violation_distance += abs(y + r - 1)\n    57\t                \n    58\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    59\t\n    60\t        # --- aux_min_radius ---\n    61\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    62\t        \n    63\t        # --- aux_num_violated_circles ---\n    64\t        num_violated_circles = 0\n    65\t        violated_circles_indices = set()\n    66\t\n    67\t        # Check for overlaps (re-do from above to get specific circles)\n    68\t        for i in range(num_circles):\n    69\t            for j in range(i + 1, num_circles):\n    70\t                dist = np.linalg.norm(centers[i] - centers[j])\n    71\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    72\t                if dist < min_dist_for_no_overlap:\n    73\t                    violated_circles_indices.add(i)\n    74\t                    violated_circles_indices.add(j)\n    75\t\n    76\t        # Check for boundary violations (re-do from above to get specific circles)\n    77\t        for i in range(num_circles):\n    78\t            x, y = centers[i]\n    79\t            r = radii[i]\n    80\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    81\t                violated_circles_indices.add(i)\n    82\t                \n    83\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    84\t\n    85\t\n    86\t    except Exception as e:\n    87\t        print(f\"Error in evaluate_aux: {e}\")\n    88\t        # Return default 0.0 for all metrics in case of failure\n    89\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    90\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    91\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    92\t        metrics[\"aux_min_radius\"] = 0.0\n    93\t        metrics[\"aux_num_violated_circles\"] = 0\n    94\t        \n    95\t    return metrics\n    96\t\n    97\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "name": "file_editor"}, {"content": [], "role": "assistant"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking:\n\nAlright, that `auxiliary_metrics.py` script is finally updated with the new metrics. Now, the real work begins. I need to verify that it's robust and actually *works* with the current data. I can't just assume it'll run flawlessly, especially after adding new calculations.\n\nSo, I'll leverage that `call_tool` function to trigger `evaluate_aux`. That's the most efficient way to get it running. First things first though; I need to get the `results_dir` for `gen_4`. That's where the data for this generation of the project lives. Then, I'll be able to run `evaluate_aux` and confirm that everything is behaving as expected. No errors, and, more importantly, the output needs to be valid. The integrity of the results is paramount.\n", "signature": null}], "tool_calls": [{"id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"python3 -c 'from eval_agent.tool_box.api import call_tool; import os; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))'\", \"summary\": \"Smoke test auxiliary_metrics.py for gen_4.\"}"}}]}, {"content": [{"type": "text", "text": "Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    from eval_agent.tool_box.api import call_tool; import os; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'eval_agent'\n[The command completed with exit code 1.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 1]"}], "role": "tool", "tool_call_id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "name": "terminal"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "BbOfabvWEvHE694P3OSx-A0", "created": 1772073733, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"summary\": \"Smoke test auxiliary_metrics.py for gen_4 by adding current directory to sys.path.\", \"command\": \"python3 -c 'import os; import sys; sys.path.insert(0, \\\".\\\"); from auxiliary_metrics import evaluate_aux; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; print(evaluate_aux(results_dir))'; \", \"security_risk\": \"LOW\"}", "name": "terminal"}, "id": "call_c6dad52b16544ff6ad6d15fac601__thought__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", "type": "function"}], "images": [], "reasoning_content": "Okay, here's what's going through my mind as I analyze this failure.\n\nUgh, the smoke test just bombed. I got a `ModuleNotFoundError: No module named 'eval_agent'`. Right, that makes sense. I was trying to import `eval_agent.tool_box.api` in that direct `python3 -c` call. The problem is, the Python interpreter spawned by that command probably doesn't have the `eval_agent` package in its search path. That's a classic gotcha.\n\nOn top of that, I think I messed up the command itself. I'm calling `evaluate_aux`, which is actually defined in `auxiliary_metrics.py`.  That file is *right here* in the current directory, inside `eval_agent_memory`, so I should be able to import it directly. I also realize now that the `call_tool` function is totally unnecessary in that `python3 -c` snippet for `evaluate_aux`.\n\nAlright, let's fix these issues. I'll need to adjust the import path to properly reference `auxiliary_metrics.py`. I have to be absolutely sure that the import works correctly. I'll focus on ensuring that this local import works. It's crucial.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as I analyze this failure.\n\nUgh, the smoke test just bombed. I got a `ModuleNotFoundError: No module named 'eval_agent'`. Right, that makes sense. I was trying to import `eval_agent.tool_box.api` in that direct `python3 -c` call. The problem is, the Python interpreter spawned by that command probably doesn't have the `eval_agent` package in its search path. That's a classic gotcha.\n\nOn top of that, I think I messed up the command itself. I'm calling `evaluate_aux`, which is actually defined in `auxiliary_metrics.py`.  That file is *right here* in the current directory, inside `eval_agent_memory`, so I should be able to import it directly. I also realize now that the `call_tool` function is totally unnecessary in that `python3 -c` snippet for `evaluate_aux`.\n\nAlright, let's fix these issues. I'll need to adjust the import path to properly reference `auxiliary_metrics.py`. I have to be absolutely sure that the import works correctly. I'll focus on ensuring that this local import works. It's crucial.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 315, "prompt_tokens": 15415, "total_tokens": 15730, "completion_tokens_details": {"reasoning_tokens": 178, "text_tokens": 137}, "prompt_tokens_details": {"text_tokens": 15415}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.005412, "timestamp": 1772073737.872669, "latency_sec": 4.580437421798706, "usage_summary": {"prompt_tokens": 15415, "completion_tokens": 315, "reasoning_tokens": 178, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073742.239-a1a8.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": "call_ad172c287e414c36a7999f9b4215__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "signature": null}], "tool_calls": [{"id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\tdef evaluate_aux(results_dir, primary_result=None):\n     2\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     3\t    return {}\n     4\t\n"}], "role": "tool", "tool_call_id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n", "signature": null}], "tool_calls": [{"id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"summary\": \"View evaluate_ori.py to understand the primary evaluation metric.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py:\n     1\t\"\"\"\n     2\tEvaluator for circle packing example (n=26) with improved timeout handling\n     3\t\"\"\"\n     4\t\n     5\timport os\n     6\timport argparse\n     7\timport sys\n     8\timport numpy as np\n     9\tfrom typing import Tuple, Optional, List, Dict, Any\n    10\tfrom pathlib import Path\n    11\t\n    12\t# Add project root to import path for direct file execution.\n    13\tsys.path.insert(0, str(Path(__file__).resolve().parents[2]))\n    14\t\n    15\tfrom shinka.core import run_shinka_eval\n    16\t\n    17\t\n    18\tdef format_centers_string(centers: np.ndarray) -> str:\n    19\t    \"\"\"Formats circle centers into a multi-line string for display.\"\"\"\n    20\t    return \"\\n\".join(\n    21\t        [\n    22\t            f\"  centers[{i}] = ({x_coord:.4f}, {y_coord:.4f})\"\n    23\t            for i, (x_coord, y_coord) in enumerate(centers)\n    24\t        ]\n    25\t    )\n    26\t\n    27\t\n    28\tdef adapted_validate_packing(\n    29\t    run_output: Tuple[np.ndarray, np.ndarray, float],\n    30\t    atol=1e-6,\n    31\t) -> Tuple[bool, Optional[str]]:\n    32\t    \"\"\"\n    33\t    Validates circle packing results based on the output of 'run_packing'.\n    34\t\n    35\t    Args:\n    36\t        run_output: Tuple (centers, radii, reported_sum) from run_packing.\n    37\t\n    38\t    Returns:\n    39\t        (is_valid: bool, error_message: Optional[str])\n    40\t    \"\"\"\n    41\t    centers, radii, reported_sum = run_output\n    42\t    msg = \"The circles are placed correctly. There are no overlaps or any circles outside the unit square.\"\n    43\t    if not isinstance(centers, np.ndarray):\n    44\t        centers = np.array(centers)\n    45\t    if not isinstance(radii, np.ndarray):\n    46\t        radii = np.array(radii)\n    47\t\n    48\t    n_expected = 26\n    49\t    if centers.shape != (n_expected, 2):\n    50\t        msg = (\n    51\t            f\"Centers shape incorrect. Expected ({n_expected}, 2), got {centers.shape}\"\n    52\t        )\n    53\t        return False, msg\n    54\t    if radii.shape != (n_expected,):\n    55\t        msg = f\"Radii shape incorrect. Expected ({n_expected},), got {radii.shape}\"\n    56\t        return False, msg\n    57\t\n    58\t    if np.any(radii < 0):\n    59\t        negative_indices = np.where(radii < 0)[0]\n    60\t        msg = f\"Negative radii found for circles at indices: {negative_indices}\"\n    61\t        return False, msg\n    62\t\n    63\t    if not np.isclose(np.sum(radii), reported_sum, atol=atol):\n    64\t        msg = (\n    65\t            f\"Sum of radii ({np.sum(radii):.6f}) does not match \"\n    66\t            f\"reported ({reported_sum:.6f})\"\n    67\t        )\n    68\t        return False, msg\n    69\t\n    70\t    for i in range(n_expected):\n    71\t        x, y = centers[i]\n    72\t        r = radii[i]\n    73\t        is_outside = (\n    74\t            x - r < -atol or x + r > 1 + atol or y - r < -atol or y + r > 1 + atol\n    75\t        )\n    76\t        if is_outside:\n    77\t            msg = (\n    78\t                f\"Circle {i} (x={x:.4f}, y={y:.4f}, r={r:.4f}) is outside unit square.\"\n    79\t            )\n    80\t            return False, msg\n    81\t\n    82\t    for i in range(n_expected):\n    83\t        for j in range(i + 1, n_expected):\n    84\t            dist = np.sqrt(np.sum((centers[i] - centers[j]) ** 2))\n    85\t            if dist < radii[i] + radii[j] - atol:\n    86\t                msg = (\n    87\t                    f\"Circles {i} & {j} overlap. Dist: {dist:.4f}, \"\n    88\t                    f\"Sum Radii: {(radii[i] + radii[j]):.4f}\"\n    89\t                )\n    90\t                return False, msg\n    91\t    return True, msg\n    92\t\n    93\t\n    94\tdef get_circle_packing_kwargs(run_index: int) -> Dict[str, Any]:\n    95\t    \"\"\"Provides keyword arguments for circle packing runs (none needed).\"\"\"\n    96\t    return {}\n    97\t\n    98\t\n    99\tdef aggregate_circle_packing_metrics(\n   100\t    results: List[Tuple[np.ndarray, np.ndarray, float]], results_dir: str\n   101\t) -> Dict[str, Any]:\n   102\t    \"\"\"\n   103\t    Aggregates metrics for circle packing. Assumes num_runs=1.\n   104\t    Saves extra.npz with detailed packing information.\n   105\t    \"\"\"\n   106\t    if not results:\n   107\t        return {\"combined_score\": 0.0, \"error\": \"No results to aggregate\"}\n   108\t\n   109\t    centers, radii, reported_sum = results[0]\n   110\t\n   111\t    public_metrics = {\n   112\t        \"centers_str\": format_centers_string(centers),\n   113\t        \"num_circles\": centers.shape[0],\n   114\t    }\n   115\t    private_metrics = {\n   116\t        \"reported_sum_of_radii\": float(reported_sum),\n   117\t    }\n   118\t    metrics = {\n   119\t        \"combined_score\": float(reported_sum),\n   120\t        \"public\": public_metrics,\n   121\t        \"private\": private_metrics,\n   122\t    }\n   123\t\n   124\t    extra_file = os.path.join(results_dir, \"extra.npz\")\n   125\t    try:\n   126\t        np.savez(\n   127\t            extra_file,\n   128\t            centers=centers,\n   129\t            radii=radii,\n   130\t            reported_sum=reported_sum,\n   131\t        )\n   132\t        print(f\"Detailed packing data saved to {extra_file}\")\n   133\t    except Exception as e:\n   134\t        print(f\"Error saving extra.npz: {e}\")\n   135\t        metrics[\"extra_npz_save_error\"] = str(e)\n   136\t\n   137\t    return metrics\n   138\t\n   139\t\n   140\tdef main(program_path: str, results_dir: str):\n   141\t    \"\"\"Runs the circle packing evaluation using shinka.eval.\"\"\"\n   142\t    print(f\"Evaluating program: {program_path}\")\n   143\t    print(f\"Saving results to: {results_dir}\")\n   144\t    os.makedirs(results_dir, exist_ok=True)\n   145\t\n   146\t    num_experiment_runs = 1\n   147\t\n   148\t    # Define a nested function to pass results_dir to the aggregator\n   149\t    def _aggregator_with_context(\n   150\t        r: List[Tuple[np.ndarray, np.ndarray, float]],\n   151\t    ) -> Dict[str, Any]:\n   152\t        return aggregate_circle_packing_metrics(r, results_dir)\n   153\t\n   154\t    metrics, correct, error_msg = run_shinka_eval(\n   155\t        program_path=program_path,\n   156\t        results_dir=results_dir,\n   157\t        experiment_fn_name=\"run_packing\",\n   158\t        num_runs=num_experiment_runs,\n   159\t        get_experiment_kwargs=get_circle_packing_kwargs,\n   160\t        validate_fn=adapted_validate_packing,\n   161\t        aggregate_metrics_fn=_aggregator_with_context,\n   162\t    )\n   163\t\n   164\t    if correct:\n   165\t        print(\"Evaluation and Validation completed successfully.\")\n   166\t    else:\n   167\t        print(f\"Evaluation or Validation failed: {error_msg}\")\n   168\t\n   169\t    print(\"Metrics:\")\n   170\t    for key, value in metrics.items():\n   171\t        if isinstance(value, str) and len(value) > 100:\n   172\t            print(f\"  {key}: <string_too_long_to_display>\")\n   173\t        else:\n   174\t            print(f\"  {key}: {value}\")\n   175\t\n   176\t\n   177\tif __name__ == \"__main__\":\n   178\t    parser = argparse.ArgumentParser(\n   179\t        description=\"Circle packing evaluator using shinka.eval\"\n   180\t    )\n   181\t    parser.add_argument(\n   182\t        \"--program_path\",\n   183\t        type=str,\n   184\t        default=\"tasks/circle_packing/initial.py\",\n   185\t        help=\"Path to program to evaluate (must contain 'run_packing')\",\n   186\t    )\n   187\t    parser.add_argument(\n   188\t        \"--results_dir\",\n   189\t        type=str,\n   190\t        default=\"tasks/circle_packing/results/debug_eval\",\n   191\t        help=\"Dir to save results (metrics.json, correct.json, extra.npz)\",\n   192\t    )\n   193\t    parsed_args = parser.parse_args()\n   194\t    main(parsed_args.program_path, parsed_args.results_dir)\n   195\t\n"}], "role": "tool", "tool_call_id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking. I'm looking at `evaluate_ori.py` and immediately see the core problem: packing 26 circles within a unit square. The goal, as judged by the `combined_score`, is to maximize the sum of the radii.\n\nThe key constraints are no negative radii, all circles inside the square, and no overlaps. The `adapted_validate_packing` function is the gatekeeper here, ensuring these constraints are met. A problem in Gen 4 has become apparent, with a dramatic score drop from Gen 3. The `combined_score` has significantly decreased, indicating something is seriously wrong with the circle packing in Gen 4.\n\nI need to understand why. My initial hypothesis is that either the constraints are being violated (overlaps or boundary violations) or the radii of the circles have shrunk dramatically, causing a lower total. I'll need to dig into this by analyzing the `primary_result` and `main.py` files for Gen 4. It will be helpful to find out where the `extra.npz` files are being saved, since that's where the critical `centers` and `radii` data are stored. The `results_dir` is the place to start.\n\nTo help diagnose this, I need to create some auxiliary metrics. I need to define them in `EVAL_AGENTS.md` since it's currently empty, and then write the code in `auxiliary_metrics.py`.\n\nThe key will be to confirm constraint violations:\n\n*   I'll calculate `aux_total_overlap_distance` and `aux_max_overlap_distance` to quantify the overlap problem.\n*   `aux_boundary_violation_distance` to measure how much circles are going outside the square.\n*   `aux_min_radius` to see if the radii are becoming suspiciously small.\n*   `aux_num_violated_circles`, a simple count of constraint violations.\n\nEssentially, I'm now in a diagnosis phase – either `EXPLORATION` or `OPTIMIZATION` -- where I am trying to figure out why the packing quality has regressed so significantly. The next step is to update `auxiliary_metrics.py` based on this analysis.\n", "signature": null}], "tool_calls": [{"id": "call_93797a325ec240039ed95cf3c33e__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"file_text\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"create\"}"}}]}, {"content": [{"type": "text", "text": "[An error occurred during execution.]\n"}, {"type": "text", "text": "Invalid `path` parameter: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. File already exists at: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. Cannot overwrite files using command `create`."}], "role": "tool", "tool_call_id": "call_93797a325ec240039ed95cf3c33e__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Right, a slip of the mind, completely my fault. I got ahead of myself and forgot that the system's already laid the groundwork with `EVAL_AGENTS.md`, even if it's currently a blank slate. Silly me, I should have been using `str_replace` or something similar for the update, not a fresh `create`. \n\nOkay, no problem. I'll utilize `str_replace` now. That's the correct way to inject the relevant registry and generation log into that pre-existing file. Time to make sure the initial content is correctly appended.\n", "signature": null}], "tool_calls": [{"id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"str_replace\", \"old_str\": \"# EV2 Agent Memory\\n\\n- Initialized by eval service.\\n- Use this file as compact cross-generation memory.\\n\", \"new_str\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, now that I've meticulously updated `EVAL_AGENTS.md`, ensuring all the agent evaluation criteria are up to snuff, it's time to dive into the code. My next task is to implement the `evaluate_aux` function within `auxiliary_metrics.py`. This is where the real analytical work begins. I'll need to crack open `extra.npz` to extract the crucial `centers` and `radii` data. These parameters are fundamental; they'll serve as the foundation for the upcoming analysis. It’s all about the details; they underpin the whole operation.\n", "signature": null}], "tool_calls": [{"id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"command\": \"str_replace\", \"old_str\": \"def evaluate_aux(results_dir, primary_result=None):\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    return {}\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"new_str\": \"import numpy as np\\nimport os\\nfrom typing import Dict, Any\\n\\ndef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    metrics = {}\\n    try:\\n        extra_file_path = os.path.join(results_dir, \\\"extra.npz\\\")\\n        if not os.path.exists(extra_file_path):\\n            print(f\\\"Warning: extra.npz not found at {extra_file_path}\\\")\\n            return metrics\\n\\n        data = np.load(extra_file_path)\\n        centers = data[\\\"centers\\\"]\\n        radii = data[\\\"radii\\\"]\\n\\n        num_circles = centers.shape[0]\\n        \\n        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\\n        total_overlap_distance = 0.0\\n        max_overlap_distance = 0.0\\n        num_overlapping_pairs = 0\\n\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    overlap = min_dist_for_no_overlap - dist\\n                    total_overlap_distance += overlap\\n                    if overlap > max_overlap_distance:\\n                        max_overlap_distance = overlap\\n                    num_overlapping_pairs += 1\\n\\n        metrics[\\\"aux_total_overlap_distance\\\"] = float(total_overlap_distance)\\n        metrics[\\\"aux_max_overlap_distance\\\"] = float(max_overlap_distance)\\n        \\n        # --- aux_boundary_violation_distance ---\\n        boundary_violation_distance = 0.0\\n        \\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n\\n            # Check x-bounds\\n            if x - r < 0:\\n                boundary_violation_distance += abs(x - r)\\n            if x + r > 1:\\n                boundary_violation_distance += abs(x + r - 1)\\n            \\n            # Check y-bounds\\n            if y - r < 0:\\n                boundary_violation_distance += abs(y - r)\\n            if y + r > 1:\\n                boundary_violation_distance += abs(y + r - 1)\\n                \\n        metrics[\\\"aux_boundary_violation_distance\\\"] = float(boundary_violation_distance)\\n\\n        # --- aux_min_radius ---\\n        metrics[\\\"aux_min_radius\\\"] = float(np.min(radii)) if radii.size > 0 else 0.0\\n        \\n        # --- aux_num_violated_circles ---\\n        num_violated_circles = 0\\n        violated_circles_indices = set()\\n\\n        # Check for overlaps (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    violated_circles_indices.add(i)\\n                    violated_circles_indices.add(j)\\n\\n        # Check for boundary violations (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\\n                violated_circles_indices.add(i)\\n                \\n        metrics[\\\"aux_num_violated_circles\\\"] = len(violated_circles_indices)\\n\\n\\n    except Exception as e:\\n        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        # Return default 0.0 for all metrics in case of failure\\n        metrics[\\\"aux_total_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_max_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_boundary_violation_distance\\\"] = 0.0\\n        metrics[\\\"aux_min_radius\\\"] = 0.0\\n        metrics[\\\"aux_num_violated_circles\\\"] = 0\\n        \\n    return metrics\\n\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"]\n    17\t\n    18\t        num_circles = centers.shape[0]\n    19\t        \n    20\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    21\t        total_overlap_distance = 0.0\n    22\t        max_overlap_distance = 0.0\n    23\t        num_overlapping_pairs = 0\n    24\t\n    25\t        for i in range(num_circles):\n    26\t            for j in range(i + 1, num_circles):\n    27\t                dist = np.linalg.norm(centers[i] - centers[j])\n    28\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    29\t                if dist < min_dist_for_no_overlap:\n    30\t                    overlap = min_dist_for_no_overlap - dist\n    31\t                    total_overlap_distance += overlap\n    32\t                    if overlap > max_overlap_distance:\n    33\t                        max_overlap_distance = overlap\n    34\t                    num_overlapping_pairs += 1\n    35\t\n    36\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    37\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    38\t        \n    39\t        # --- aux_boundary_violation_distance ---\n    40\t        boundary_violation_distance = 0.0\n    41\t        \n    42\t        for i in range(num_circles):\n    43\t            x, y = centers[i]\n    44\t            r = radii[i]\n    45\t\n    46\t            # Check x-bounds\n    47\t            if x - r < 0:\n    48\t                boundary_violation_distance += abs(x - r)\n    49\t            if x + r > 1:\n    50\t                boundary_violation_distance += abs(x + r - 1)\n    51\t            \n    52\t            # Check y-bounds\n    53\t            if y - r < 0:\n    54\t                boundary_violation_distance += abs(y - r)\n    55\t            if y + r > 1:\n    56\t                boundary_violation_distance += abs(y + r - 1)\n    57\t                \n    58\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    59\t\n    60\t        # --- aux_min_radius ---\n    61\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    62\t        \n    63\t        # --- aux_num_violated_circles ---\n    64\t        num_violated_circles = 0\n    65\t        violated_circles_indices = set()\n    66\t\n    67\t        # Check for overlaps (re-do from above to get specific circles)\n    68\t        for i in range(num_circles):\n    69\t            for j in range(i + 1, num_circles):\n    70\t                dist = np.linalg.norm(centers[i] - centers[j])\n    71\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    72\t                if dist < min_dist_for_no_overlap:\n    73\t                    violated_circles_indices.add(i)\n    74\t                    violated_circles_indices.add(j)\n    75\t\n    76\t        # Check for boundary violations (re-do from above to get specific circles)\n    77\t        for i in range(num_circles):\n    78\t            x, y = centers[i]\n    79\t            r = radii[i]\n    80\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    81\t                violated_circles_indices.add(i)\n    82\t                \n    83\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    84\t\n    85\t\n    86\t    except Exception as e:\n    87\t        print(f\"Error in evaluate_aux: {e}\")\n    88\t        # Return default 0.0 for all metrics in case of failure\n    89\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    90\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    91\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    92\t        metrics[\"aux_min_radius\"] = 0.0\n    93\t        metrics[\"aux_num_violated_circles\"] = 0\n    94\t        \n    95\t    return metrics\n    96\t\n    97\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "name": "file_editor"}, {"content": [], "role": "assistant"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking:\n\nAlright, that `auxiliary_metrics.py` script is finally updated with the new metrics. Now, the real work begins. I need to verify that it's robust and actually *works* with the current data. I can't just assume it'll run flawlessly, especially after adding new calculations.\n\nSo, I'll leverage that `call_tool` function to trigger `evaluate_aux`. That's the most efficient way to get it running. First things first though; I need to get the `results_dir` for `gen_4`. That's where the data for this generation of the project lives. Then, I'll be able to run `evaluate_aux` and confirm that everything is behaving as expected. No errors, and, more importantly, the output needs to be valid. The integrity of the results is paramount.\n", "signature": null}], "tool_calls": [{"id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"python3 -c 'from eval_agent.tool_box.api import call_tool; import os; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))'\", \"summary\": \"Smoke test auxiliary_metrics.py for gen_4.\"}"}}]}, {"content": [{"type": "text", "text": "Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    from eval_agent.tool_box.api import call_tool; import os; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'eval_agent'\n[The command completed with exit code 1.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 1]"}], "role": "tool", "tool_call_id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as I analyze this failure.\n\nUgh, the smoke test just bombed. I got a `ModuleNotFoundError: No module named 'eval_agent'`. Right, that makes sense. I was trying to import `eval_agent.tool_box.api` in that direct `python3 -c` call. The problem is, the Python interpreter spawned by that command probably doesn't have the `eval_agent` package in its search path. That's a classic gotcha.\n\nOn top of that, I think I messed up the command itself. I'm calling `evaluate_aux`, which is actually defined in `auxiliary_metrics.py`.  That file is *right here* in the current directory, inside `eval_agent_memory`, so I should be able to import it directly. I also realize now that the `call_tool` function is totally unnecessary in that `python3 -c` snippet for `evaluate_aux`.\n\nAlright, let's fix these issues. I'll need to adjust the import path to properly reference `auxiliary_metrics.py`. I have to be absolutely sure that the import works correctly. I'll focus on ensuring that this local import works. It's crucial.\n", "signature": null}], "tool_calls": [{"id": "call_c6dad52b16544ff6ad6d15fac601__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"summary\": \"Smoke test auxiliary_metrics.py for gen_4 by adding current directory to sys.path.\", \"command\": \"python3 -c 'import os; import sys; sys.path.insert(0, \\\".\\\"); from auxiliary_metrics import evaluate_aux; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; print(evaluate_aux(results_dir))'; \", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "python3 -c 'import os; import sys; sys.path.insert(0, \".\"); from auxiliary_metrics import evaluate_aux; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; print(evaluate_aux(results_dir))'\n{'aux_total_overlap_distance': 0.0, 'aux_max_overlap_distance': 0.0, 'aux_boundary_violation_distance': 0.0, 'aux_min_radius': 0.0, 'aux_num_violated_circles': 0}\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_c6dad52b16544ff6ad6d15fac601__thought__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", "name": "terminal"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "CrOfaYfsNv2R694Pl4bWwA4", "created": 1772073738, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"summary\": \"Check contents of gen_4 results directory.\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results/\", \"security_risk\": \"LOW\"}", "name": "terminal"}, "id": "call_319dcfe7a9ef42db87cdefdb77fe__thought__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", "type": "function"}], "images": [], "reasoning_content": "Okay, the smoke test just finished, and though it ran without errors, all the evaluation metrics are reporting zero. That immediately flags a problem with the data. My gut tells me something's up with the `extra.npz` file, specifically within the `gen_4/results` directory of the `gen_100` run. It seems like either the file isn't present, or more likely, it's empty or got corrupted during the process. The results are being pulled from the `results_circle_packing_v2_1_gen100_periodic5_20260226_023318` run, so I need to check the file system. Right, let me go check if that `extra.npz` file actually exists, and if it does, what its contents look like within the `results` directory. Need to dive in there and see what's what.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, the smoke test just finished, and though it ran without errors, all the evaluation metrics are reporting zero. That immediately flags a problem with the data. My gut tells me something's up with the `extra.npz` file, specifically within the `gen_4/results` directory of the `gen_100` run. It seems like either the file isn't present, or more likely, it's empty or got corrupted during the process. The results are being pulled from the `results_circle_packing_v2_1_gen100_periodic5_20260226_023318` run, so I need to check the file system. Right, let me go check if that `extra.npz` file actually exists, and if it does, what its contents look like within the `results` directory. Need to dive in there and see what's what.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 205, "prompt_tokens": 16022, "total_tokens": 16227, "completion_tokens_details": {"reasoning_tokens": 122, "text_tokens": 83}, "prompt_tokens_details": {"text_tokens": 16022}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.005319099999999999, "timestamp": 1772073742.2394392, "latency_sec": 3.382028579711914, "usage_summary": {"prompt_tokens": 16022, "completion_tokens": 205, "reasoning_tokens": 122, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073746.838-1a59.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": "call_ad172c287e414c36a7999f9b4215__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "signature": null}], "tool_calls": [{"id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\tdef evaluate_aux(results_dir, primary_result=None):\n     2\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     3\t    return {}\n     4\t\n"}], "role": "tool", "tool_call_id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n", "signature": null}], "tool_calls": [{"id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"summary\": \"View evaluate_ori.py to understand the primary evaluation metric.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py:\n     1\t\"\"\"\n     2\tEvaluator for circle packing example (n=26) with improved timeout handling\n     3\t\"\"\"\n     4\t\n     5\timport os\n     6\timport argparse\n     7\timport sys\n     8\timport numpy as np\n     9\tfrom typing import Tuple, Optional, List, Dict, Any\n    10\tfrom pathlib import Path\n    11\t\n    12\t# Add project root to import path for direct file execution.\n    13\tsys.path.insert(0, str(Path(__file__).resolve().parents[2]))\n    14\t\n    15\tfrom shinka.core import run_shinka_eval\n    16\t\n    17\t\n    18\tdef format_centers_string(centers: np.ndarray) -> str:\n    19\t    \"\"\"Formats circle centers into a multi-line string for display.\"\"\"\n    20\t    return \"\\n\".join(\n    21\t        [\n    22\t            f\"  centers[{i}] = ({x_coord:.4f}, {y_coord:.4f})\"\n    23\t            for i, (x_coord, y_coord) in enumerate(centers)\n    24\t        ]\n    25\t    )\n    26\t\n    27\t\n    28\tdef adapted_validate_packing(\n    29\t    run_output: Tuple[np.ndarray, np.ndarray, float],\n    30\t    atol=1e-6,\n    31\t) -> Tuple[bool, Optional[str]]:\n    32\t    \"\"\"\n    33\t    Validates circle packing results based on the output of 'run_packing'.\n    34\t\n    35\t    Args:\n    36\t        run_output: Tuple (centers, radii, reported_sum) from run_packing.\n    37\t\n    38\t    Returns:\n    39\t        (is_valid: bool, error_message: Optional[str])\n    40\t    \"\"\"\n    41\t    centers, radii, reported_sum = run_output\n    42\t    msg = \"The circles are placed correctly. There are no overlaps or any circles outside the unit square.\"\n    43\t    if not isinstance(centers, np.ndarray):\n    44\t        centers = np.array(centers)\n    45\t    if not isinstance(radii, np.ndarray):\n    46\t        radii = np.array(radii)\n    47\t\n    48\t    n_expected = 26\n    49\t    if centers.shape != (n_expected, 2):\n    50\t        msg = (\n    51\t            f\"Centers shape incorrect. Expected ({n_expected}, 2), got {centers.shape}\"\n    52\t        )\n    53\t        return False, msg\n    54\t    if radii.shape != (n_expected,):\n    55\t        msg = f\"Radii shape incorrect. Expected ({n_expected},), got {radii.shape}\"\n    56\t        return False, msg\n    57\t\n    58\t    if np.any(radii < 0):\n    59\t        negative_indices = np.where(radii < 0)[0]\n    60\t        msg = f\"Negative radii found for circles at indices: {negative_indices}\"\n    61\t        return False, msg\n    62\t\n    63\t    if not np.isclose(np.sum(radii), reported_sum, atol=atol):\n    64\t        msg = (\n    65\t            f\"Sum of radii ({np.sum(radii):.6f}) does not match \"\n    66\t            f\"reported ({reported_sum:.6f})\"\n    67\t        )\n    68\t        return False, msg\n    69\t\n    70\t    for i in range(n_expected):\n    71\t        x, y = centers[i]\n    72\t        r = radii[i]\n    73\t        is_outside = (\n    74\t            x - r < -atol or x + r > 1 + atol or y - r < -atol or y + r > 1 + atol\n    75\t        )\n    76\t        if is_outside:\n    77\t            msg = (\n    78\t                f\"Circle {i} (x={x:.4f}, y={y:.4f}, r={r:.4f}) is outside unit square.\"\n    79\t            )\n    80\t            return False, msg\n    81\t\n    82\t    for i in range(n_expected):\n    83\t        for j in range(i + 1, n_expected):\n    84\t            dist = np.sqrt(np.sum((centers[i] - centers[j]) ** 2))\n    85\t            if dist < radii[i] + radii[j] - atol:\n    86\t                msg = (\n    87\t                    f\"Circles {i} & {j} overlap. Dist: {dist:.4f}, \"\n    88\t                    f\"Sum Radii: {(radii[i] + radii[j]):.4f}\"\n    89\t                )\n    90\t                return False, msg\n    91\t    return True, msg\n    92\t\n    93\t\n    94\tdef get_circle_packing_kwargs(run_index: int) -> Dict[str, Any]:\n    95\t    \"\"\"Provides keyword arguments for circle packing runs (none needed).\"\"\"\n    96\t    return {}\n    97\t\n    98\t\n    99\tdef aggregate_circle_packing_metrics(\n   100\t    results: List[Tuple[np.ndarray, np.ndarray, float]], results_dir: str\n   101\t) -> Dict[str, Any]:\n   102\t    \"\"\"\n   103\t    Aggregates metrics for circle packing. Assumes num_runs=1.\n   104\t    Saves extra.npz with detailed packing information.\n   105\t    \"\"\"\n   106\t    if not results:\n   107\t        return {\"combined_score\": 0.0, \"error\": \"No results to aggregate\"}\n   108\t\n   109\t    centers, radii, reported_sum = results[0]\n   110\t\n   111\t    public_metrics = {\n   112\t        \"centers_str\": format_centers_string(centers),\n   113\t        \"num_circles\": centers.shape[0],\n   114\t    }\n   115\t    private_metrics = {\n   116\t        \"reported_sum_of_radii\": float(reported_sum),\n   117\t    }\n   118\t    metrics = {\n   119\t        \"combined_score\": float(reported_sum),\n   120\t        \"public\": public_metrics,\n   121\t        \"private\": private_metrics,\n   122\t    }\n   123\t\n   124\t    extra_file = os.path.join(results_dir, \"extra.npz\")\n   125\t    try:\n   126\t        np.savez(\n   127\t            extra_file,\n   128\t            centers=centers,\n   129\t            radii=radii,\n   130\t            reported_sum=reported_sum,\n   131\t        )\n   132\t        print(f\"Detailed packing data saved to {extra_file}\")\n   133\t    except Exception as e:\n   134\t        print(f\"Error saving extra.npz: {e}\")\n   135\t        metrics[\"extra_npz_save_error\"] = str(e)\n   136\t\n   137\t    return metrics\n   138\t\n   139\t\n   140\tdef main(program_path: str, results_dir: str):\n   141\t    \"\"\"Runs the circle packing evaluation using shinka.eval.\"\"\"\n   142\t    print(f\"Evaluating program: {program_path}\")\n   143\t    print(f\"Saving results to: {results_dir}\")\n   144\t    os.makedirs(results_dir, exist_ok=True)\n   145\t\n   146\t    num_experiment_runs = 1\n   147\t\n   148\t    # Define a nested function to pass results_dir to the aggregator\n   149\t    def _aggregator_with_context(\n   150\t        r: List[Tuple[np.ndarray, np.ndarray, float]],\n   151\t    ) -> Dict[str, Any]:\n   152\t        return aggregate_circle_packing_metrics(r, results_dir)\n   153\t\n   154\t    metrics, correct, error_msg = run_shinka_eval(\n   155\t        program_path=program_path,\n   156\t        results_dir=results_dir,\n   157\t        experiment_fn_name=\"run_packing\",\n   158\t        num_runs=num_experiment_runs,\n   159\t        get_experiment_kwargs=get_circle_packing_kwargs,\n   160\t        validate_fn=adapted_validate_packing,\n   161\t        aggregate_metrics_fn=_aggregator_with_context,\n   162\t    )\n   163\t\n   164\t    if correct:\n   165\t        print(\"Evaluation and Validation completed successfully.\")\n   166\t    else:\n   167\t        print(f\"Evaluation or Validation failed: {error_msg}\")\n   168\t\n   169\t    print(\"Metrics:\")\n   170\t    for key, value in metrics.items():\n   171\t        if isinstance(value, str) and len(value) > 100:\n   172\t            print(f\"  {key}: <string_too_long_to_display>\")\n   173\t        else:\n   174\t            print(f\"  {key}: {value}\")\n   175\t\n   176\t\n   177\tif __name__ == \"__main__\":\n   178\t    parser = argparse.ArgumentParser(\n   179\t        description=\"Circle packing evaluator using shinka.eval\"\n   180\t    )\n   181\t    parser.add_argument(\n   182\t        \"--program_path\",\n   183\t        type=str,\n   184\t        default=\"tasks/circle_packing/initial.py\",\n   185\t        help=\"Path to program to evaluate (must contain 'run_packing')\",\n   186\t    )\n   187\t    parser.add_argument(\n   188\t        \"--results_dir\",\n   189\t        type=str,\n   190\t        default=\"tasks/circle_packing/results/debug_eval\",\n   191\t        help=\"Dir to save results (metrics.json, correct.json, extra.npz)\",\n   192\t    )\n   193\t    parsed_args = parser.parse_args()\n   194\t    main(parsed_args.program_path, parsed_args.results_dir)\n   195\t\n"}], "role": "tool", "tool_call_id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking. I'm looking at `evaluate_ori.py` and immediately see the core problem: packing 26 circles within a unit square. The goal, as judged by the `combined_score`, is to maximize the sum of the radii.\n\nThe key constraints are no negative radii, all circles inside the square, and no overlaps. The `adapted_validate_packing` function is the gatekeeper here, ensuring these constraints are met. A problem in Gen 4 has become apparent, with a dramatic score drop from Gen 3. The `combined_score` has significantly decreased, indicating something is seriously wrong with the circle packing in Gen 4.\n\nI need to understand why. My initial hypothesis is that either the constraints are being violated (overlaps or boundary violations) or the radii of the circles have shrunk dramatically, causing a lower total. I'll need to dig into this by analyzing the `primary_result` and `main.py` files for Gen 4. It will be helpful to find out where the `extra.npz` files are being saved, since that's where the critical `centers` and `radii` data are stored. The `results_dir` is the place to start.\n\nTo help diagnose this, I need to create some auxiliary metrics. I need to define them in `EVAL_AGENTS.md` since it's currently empty, and then write the code in `auxiliary_metrics.py`.\n\nThe key will be to confirm constraint violations:\n\n*   I'll calculate `aux_total_overlap_distance` and `aux_max_overlap_distance` to quantify the overlap problem.\n*   `aux_boundary_violation_distance` to measure how much circles are going outside the square.\n*   `aux_min_radius` to see if the radii are becoming suspiciously small.\n*   `aux_num_violated_circles`, a simple count of constraint violations.\n\nEssentially, I'm now in a diagnosis phase – either `EXPLORATION` or `OPTIMIZATION` -- where I am trying to figure out why the packing quality has regressed so significantly. The next step is to update `auxiliary_metrics.py` based on this analysis.\n", "signature": null}], "tool_calls": [{"id": "call_93797a325ec240039ed95cf3c33e__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"file_text\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"create\"}"}}]}, {"content": [{"type": "text", "text": "[An error occurred during execution.]\n"}, {"type": "text", "text": "Invalid `path` parameter: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. File already exists at: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. Cannot overwrite files using command `create`."}], "role": "tool", "tool_call_id": "call_93797a325ec240039ed95cf3c33e__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Right, a slip of the mind, completely my fault. I got ahead of myself and forgot that the system's already laid the groundwork with `EVAL_AGENTS.md`, even if it's currently a blank slate. Silly me, I should have been using `str_replace` or something similar for the update, not a fresh `create`. \n\nOkay, no problem. I'll utilize `str_replace` now. That's the correct way to inject the relevant registry and generation log into that pre-existing file. Time to make sure the initial content is correctly appended.\n", "signature": null}], "tool_calls": [{"id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"str_replace\", \"old_str\": \"# EV2 Agent Memory\\n\\n- Initialized by eval service.\\n- Use this file as compact cross-generation memory.\\n\", \"new_str\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, now that I've meticulously updated `EVAL_AGENTS.md`, ensuring all the agent evaluation criteria are up to snuff, it's time to dive into the code. My next task is to implement the `evaluate_aux` function within `auxiliary_metrics.py`. This is where the real analytical work begins. I'll need to crack open `extra.npz` to extract the crucial `centers` and `radii` data. These parameters are fundamental; they'll serve as the foundation for the upcoming analysis. It’s all about the details; they underpin the whole operation.\n", "signature": null}], "tool_calls": [{"id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"command\": \"str_replace\", \"old_str\": \"def evaluate_aux(results_dir, primary_result=None):\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    return {}\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"new_str\": \"import numpy as np\\nimport os\\nfrom typing import Dict, Any\\n\\ndef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    metrics = {}\\n    try:\\n        extra_file_path = os.path.join(results_dir, \\\"extra.npz\\\")\\n        if not os.path.exists(extra_file_path):\\n            print(f\\\"Warning: extra.npz not found at {extra_file_path}\\\")\\n            return metrics\\n\\n        data = np.load(extra_file_path)\\n        centers = data[\\\"centers\\\"]\\n        radii = data[\\\"radii\\\"]\\n\\n        num_circles = centers.shape[0]\\n        \\n        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\\n        total_overlap_distance = 0.0\\n        max_overlap_distance = 0.0\\n        num_overlapping_pairs = 0\\n\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    overlap = min_dist_for_no_overlap - dist\\n                    total_overlap_distance += overlap\\n                    if overlap > max_overlap_distance:\\n                        max_overlap_distance = overlap\\n                    num_overlapping_pairs += 1\\n\\n        metrics[\\\"aux_total_overlap_distance\\\"] = float(total_overlap_distance)\\n        metrics[\\\"aux_max_overlap_distance\\\"] = float(max_overlap_distance)\\n        \\n        # --- aux_boundary_violation_distance ---\\n        boundary_violation_distance = 0.0\\n        \\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n\\n            # Check x-bounds\\n            if x - r < 0:\\n                boundary_violation_distance += abs(x - r)\\n            if x + r > 1:\\n                boundary_violation_distance += abs(x + r - 1)\\n            \\n            # Check y-bounds\\n            if y - r < 0:\\n                boundary_violation_distance += abs(y - r)\\n            if y + r > 1:\\n                boundary_violation_distance += abs(y + r - 1)\\n                \\n        metrics[\\\"aux_boundary_violation_distance\\\"] = float(boundary_violation_distance)\\n\\n        # --- aux_min_radius ---\\n        metrics[\\\"aux_min_radius\\\"] = float(np.min(radii)) if radii.size > 0 else 0.0\\n        \\n        # --- aux_num_violated_circles ---\\n        num_violated_circles = 0\\n        violated_circles_indices = set()\\n\\n        # Check for overlaps (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    violated_circles_indices.add(i)\\n                    violated_circles_indices.add(j)\\n\\n        # Check for boundary violations (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\\n                violated_circles_indices.add(i)\\n                \\n        metrics[\\\"aux_num_violated_circles\\\"] = len(violated_circles_indices)\\n\\n\\n    except Exception as e:\\n        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        # Return default 0.0 for all metrics in case of failure\\n        metrics[\\\"aux_total_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_max_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_boundary_violation_distance\\\"] = 0.0\\n        metrics[\\\"aux_min_radius\\\"] = 0.0\\n        metrics[\\\"aux_num_violated_circles\\\"] = 0\\n        \\n    return metrics\\n\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"]\n    17\t\n    18\t        num_circles = centers.shape[0]\n    19\t        \n    20\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    21\t        total_overlap_distance = 0.0\n    22\t        max_overlap_distance = 0.0\n    23\t        num_overlapping_pairs = 0\n    24\t\n    25\t        for i in range(num_circles):\n    26\t            for j in range(i + 1, num_circles):\n    27\t                dist = np.linalg.norm(centers[i] - centers[j])\n    28\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    29\t                if dist < min_dist_for_no_overlap:\n    30\t                    overlap = min_dist_for_no_overlap - dist\n    31\t                    total_overlap_distance += overlap\n    32\t                    if overlap > max_overlap_distance:\n    33\t                        max_overlap_distance = overlap\n    34\t                    num_overlapping_pairs += 1\n    35\t\n    36\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    37\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    38\t        \n    39\t        # --- aux_boundary_violation_distance ---\n    40\t        boundary_violation_distance = 0.0\n    41\t        \n    42\t        for i in range(num_circles):\n    43\t            x, y = centers[i]\n    44\t            r = radii[i]\n    45\t\n    46\t            # Check x-bounds\n    47\t            if x - r < 0:\n    48\t                boundary_violation_distance += abs(x - r)\n    49\t            if x + r > 1:\n    50\t                boundary_violation_distance += abs(x + r - 1)\n    51\t            \n    52\t            # Check y-bounds\n    53\t            if y - r < 0:\n    54\t                boundary_violation_distance += abs(y - r)\n    55\t            if y + r > 1:\n    56\t                boundary_violation_distance += abs(y + r - 1)\n    57\t                \n    58\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    59\t\n    60\t        # --- aux_min_radius ---\n    61\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    62\t        \n    63\t        # --- aux_num_violated_circles ---\n    64\t        num_violated_circles = 0\n    65\t        violated_circles_indices = set()\n    66\t\n    67\t        # Check for overlaps (re-do from above to get specific circles)\n    68\t        for i in range(num_circles):\n    69\t            for j in range(i + 1, num_circles):\n    70\t                dist = np.linalg.norm(centers[i] - centers[j])\n    71\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    72\t                if dist < min_dist_for_no_overlap:\n    73\t                    violated_circles_indices.add(i)\n    74\t                    violated_circles_indices.add(j)\n    75\t\n    76\t        # Check for boundary violations (re-do from above to get specific circles)\n    77\t        for i in range(num_circles):\n    78\t            x, y = centers[i]\n    79\t            r = radii[i]\n    80\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    81\t                violated_circles_indices.add(i)\n    82\t                \n    83\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    84\t\n    85\t\n    86\t    except Exception as e:\n    87\t        print(f\"Error in evaluate_aux: {e}\")\n    88\t        # Return default 0.0 for all metrics in case of failure\n    89\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    90\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    91\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    92\t        metrics[\"aux_min_radius\"] = 0.0\n    93\t        metrics[\"aux_num_violated_circles\"] = 0\n    94\t        \n    95\t    return metrics\n    96\t\n    97\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "name": "file_editor"}, {"content": [], "role": "assistant"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking:\n\nAlright, that `auxiliary_metrics.py` script is finally updated with the new metrics. Now, the real work begins. I need to verify that it's robust and actually *works* with the current data. I can't just assume it'll run flawlessly, especially after adding new calculations.\n\nSo, I'll leverage that `call_tool` function to trigger `evaluate_aux`. That's the most efficient way to get it running. First things first though; I need to get the `results_dir` for `gen_4`. That's where the data for this generation of the project lives. Then, I'll be able to run `evaluate_aux` and confirm that everything is behaving as expected. No errors, and, more importantly, the output needs to be valid. The integrity of the results is paramount.\n", "signature": null}], "tool_calls": [{"id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"python3 -c 'from eval_agent.tool_box.api import call_tool; import os; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))'\", \"summary\": \"Smoke test auxiliary_metrics.py for gen_4.\"}"}}]}, {"content": [{"type": "text", "text": "Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    from eval_agent.tool_box.api import call_tool; import os; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'eval_agent'\n[The command completed with exit code 1.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 1]"}], "role": "tool", "tool_call_id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as I analyze this failure.\n\nUgh, the smoke test just bombed. I got a `ModuleNotFoundError: No module named 'eval_agent'`. Right, that makes sense. I was trying to import `eval_agent.tool_box.api` in that direct `python3 -c` call. The problem is, the Python interpreter spawned by that command probably doesn't have the `eval_agent` package in its search path. That's a classic gotcha.\n\nOn top of that, I think I messed up the command itself. I'm calling `evaluate_aux`, which is actually defined in `auxiliary_metrics.py`.  That file is *right here* in the current directory, inside `eval_agent_memory`, so I should be able to import it directly. I also realize now that the `call_tool` function is totally unnecessary in that `python3 -c` snippet for `evaluate_aux`.\n\nAlright, let's fix these issues. I'll need to adjust the import path to properly reference `auxiliary_metrics.py`. I have to be absolutely sure that the import works correctly. I'll focus on ensuring that this local import works. It's crucial.\n", "signature": null}], "tool_calls": [{"id": "call_c6dad52b16544ff6ad6d15fac601__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"summary\": \"Smoke test auxiliary_metrics.py for gen_4 by adding current directory to sys.path.\", \"command\": \"python3 -c 'import os; import sys; sys.path.insert(0, \\\".\\\"); from auxiliary_metrics import evaluate_aux; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; print(evaluate_aux(results_dir))'; \", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "python3 -c 'import os; import sys; sys.path.insert(0, \".\"); from auxiliary_metrics import evaluate_aux; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; print(evaluate_aux(results_dir))'\n{'aux_total_overlap_distance': 0.0, 'aux_max_overlap_distance': 0.0, 'aux_boundary_violation_distance': 0.0, 'aux_min_radius': 0.0, 'aux_num_violated_circles': 0}\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_c6dad52b16544ff6ad6d15fac601__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, the smoke test just finished, and though it ran without errors, all the evaluation metrics are reporting zero. That immediately flags a problem with the data. My gut tells me something's up with the `extra.npz` file, specifically within the `gen_4/results` directory of the `gen_100` run. It seems like either the file isn't present, or more likely, it's empty or got corrupted during the process. The results are being pulled from the `results_circle_packing_v2_1_gen100_periodic5_20260226_023318` run, so I need to check the file system. Right, let me go check if that `extra.npz` file actually exists, and if it does, what its contents look like within the `results` directory. Need to dive in there and see what's what.\n", "signature": null}], "tool_calls": [{"id": "call_319dcfe7a9ef42db87cdefdb77fe__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"summary\": \"Check contents of gen_4 results directory.\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results/\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "total 16\n-rw-r--r-- 1 tengxiao tengxiao  112 Feb 26 02:41 auxiliary_metrics_snapshot.py\n-rw-r--r-- 1 tengxiao tengxiao   42 Feb 26 02:41 correct.json\n-rw-r--r-- 1 tengxiao tengxiao 1398 Feb 26 02:41 extra.npz\n-rw-r--r-- 1 tengxiao tengxiao 1666 Feb 26 02:41 metrics.json\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_319dcfe7a9ef42db87cdefdb77fe__thought__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", "name": "terminal"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "DrOfaYOGOd-G694Po_7k8Qs", "created": 1772073742, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"old_str\": \"        print(f\\\"Error in evaluate_aux: {e}\\\")\", \"new_str\": \"        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        import traceback\\n        traceback.print_exc()\", \"command\": \"str_replace\"}", "name": "file_editor"}, "id": "call_0bd8712946544dd8ae3779039a57__thought__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", "type": "function"}], "images": [], "reasoning_content": "Okay, here's what I'm thinking:\n\nAlright, that `extra.npz` file, I know for a fact it's sitting right there in `gen_4/results`. So the issue *isn't* the file's absence. That eliminates a whole class of potential problems.  My gut says `np.load` is the culprit. Either it choked on the format, or the data inside wasn't structured how I anticipated. That's what's triggering the `except` block and causing all the metrics to zero out.\n\nRight, time to dive deeper. I'll need to see the full stack trace to pinpoint exactly *where* and *why* `np.load` is failing.  I'll modify `auxiliary_metrics.py` to spit out all the gory details of the exception. That should give me the diagnostic clues I need to fix this quickly. \n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking:\n\nAlright, that `extra.npz` file, I know for a fact it's sitting right there in `gen_4/results`. So the issue *isn't* the file's absence. That eliminates a whole class of potential problems.  My gut says `np.load` is the culprit. Either it choked on the format, or the data inside wasn't structured how I anticipated. That's what's triggering the `except` block and causing all the metrics to zero out.\n\nRight, time to dive deeper. I'll need to see the full stack trace to pinpoint exactly *where* and *why* `np.load` is failing.  I'll modify `auxiliary_metrics.py` to spit out all the gory details of the exception. That should give me the diagnostic clues I need to fix this quickly. \n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 219, "prompt_tokens": 16483, "total_tokens": 16702, "completion_tokens_details": {"reasoning_tokens": 90, "text_tokens": 129}, "prompt_tokens_details": {"text_tokens": 16483}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0054924, "timestamp": 1772073746.8377523, "latency_sec": 3.9283411502838135, "usage_summary": {"prompt_tokens": 16483, "completion_tokens": 219, "reasoning_tokens": 90, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073749.206-813f.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": "call_ad172c287e414c36a7999f9b4215__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "signature": null}], "tool_calls": [{"id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\tdef evaluate_aux(results_dir, primary_result=None):\n     2\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     3\t    return {}\n     4\t\n"}], "role": "tool", "tool_call_id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n", "signature": null}], "tool_calls": [{"id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"summary\": \"View evaluate_ori.py to understand the primary evaluation metric.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py:\n     1\t\"\"\"\n     2\tEvaluator for circle packing example (n=26) with improved timeout handling\n     3\t\"\"\"\n     4\t\n     5\timport os\n     6\timport argparse\n     7\timport sys\n     8\timport numpy as np\n     9\tfrom typing import Tuple, Optional, List, Dict, Any\n    10\tfrom pathlib import Path\n    11\t\n    12\t# Add project root to import path for direct file execution.\n    13\tsys.path.insert(0, str(Path(__file__).resolve().parents[2]))\n    14\t\n    15\tfrom shinka.core import run_shinka_eval\n    16\t\n    17\t\n    18\tdef format_centers_string(centers: np.ndarray) -> str:\n    19\t    \"\"\"Formats circle centers into a multi-line string for display.\"\"\"\n    20\t    return \"\\n\".join(\n    21\t        [\n    22\t            f\"  centers[{i}] = ({x_coord:.4f}, {y_coord:.4f})\"\n    23\t            for i, (x_coord, y_coord) in enumerate(centers)\n    24\t        ]\n    25\t    )\n    26\t\n    27\t\n    28\tdef adapted_validate_packing(\n    29\t    run_output: Tuple[np.ndarray, np.ndarray, float],\n    30\t    atol=1e-6,\n    31\t) -> Tuple[bool, Optional[str]]:\n    32\t    \"\"\"\n    33\t    Validates circle packing results based on the output of 'run_packing'.\n    34\t\n    35\t    Args:\n    36\t        run_output: Tuple (centers, radii, reported_sum) from run_packing.\n    37\t\n    38\t    Returns:\n    39\t        (is_valid: bool, error_message: Optional[str])\n    40\t    \"\"\"\n    41\t    centers, radii, reported_sum = run_output\n    42\t    msg = \"The circles are placed correctly. There are no overlaps or any circles outside the unit square.\"\n    43\t    if not isinstance(centers, np.ndarray):\n    44\t        centers = np.array(centers)\n    45\t    if not isinstance(radii, np.ndarray):\n    46\t        radii = np.array(radii)\n    47\t\n    48\t    n_expected = 26\n    49\t    if centers.shape != (n_expected, 2):\n    50\t        msg = (\n    51\t            f\"Centers shape incorrect. Expected ({n_expected}, 2), got {centers.shape}\"\n    52\t        )\n    53\t        return False, msg\n    54\t    if radii.shape != (n_expected,):\n    55\t        msg = f\"Radii shape incorrect. Expected ({n_expected},), got {radii.shape}\"\n    56\t        return False, msg\n    57\t\n    58\t    if np.any(radii < 0):\n    59\t        negative_indices = np.where(radii < 0)[0]\n    60\t        msg = f\"Negative radii found for circles at indices: {negative_indices}\"\n    61\t        return False, msg\n    62\t\n    63\t    if not np.isclose(np.sum(radii), reported_sum, atol=atol):\n    64\t        msg = (\n    65\t            f\"Sum of radii ({np.sum(radii):.6f}) does not match \"\n    66\t            f\"reported ({reported_sum:.6f})\"\n    67\t        )\n    68\t        return False, msg\n    69\t\n    70\t    for i in range(n_expected):\n    71\t        x, y = centers[i]\n    72\t        r = radii[i]\n    73\t        is_outside = (\n    74\t            x - r < -atol or x + r > 1 + atol or y - r < -atol or y + r > 1 + atol\n    75\t        )\n    76\t        if is_outside:\n    77\t            msg = (\n    78\t                f\"Circle {i} (x={x:.4f}, y={y:.4f}, r={r:.4f}) is outside unit square.\"\n    79\t            )\n    80\t            return False, msg\n    81\t\n    82\t    for i in range(n_expected):\n    83\t        for j in range(i + 1, n_expected):\n    84\t            dist = np.sqrt(np.sum((centers[i] - centers[j]) ** 2))\n    85\t            if dist < radii[i] + radii[j] - atol:\n    86\t                msg = (\n    87\t                    f\"Circles {i} & {j} overlap. Dist: {dist:.4f}, \"\n    88\t                    f\"Sum Radii: {(radii[i] + radii[j]):.4f}\"\n    89\t                )\n    90\t                return False, msg\n    91\t    return True, msg\n    92\t\n    93\t\n    94\tdef get_circle_packing_kwargs(run_index: int) -> Dict[str, Any]:\n    95\t    \"\"\"Provides keyword arguments for circle packing runs (none needed).\"\"\"\n    96\t    return {}\n    97\t\n    98\t\n    99\tdef aggregate_circle_packing_metrics(\n   100\t    results: List[Tuple[np.ndarray, np.ndarray, float]], results_dir: str\n   101\t) -> Dict[str, Any]:\n   102\t    \"\"\"\n   103\t    Aggregates metrics for circle packing. Assumes num_runs=1.\n   104\t    Saves extra.npz with detailed packing information.\n   105\t    \"\"\"\n   106\t    if not results:\n   107\t        return {\"combined_score\": 0.0, \"error\": \"No results to aggregate\"}\n   108\t\n   109\t    centers, radii, reported_sum = results[0]\n   110\t\n   111\t    public_metrics = {\n   112\t        \"centers_str\": format_centers_string(centers),\n   113\t        \"num_circles\": centers.shape[0],\n   114\t    }\n   115\t    private_metrics = {\n   116\t        \"reported_sum_of_radii\": float(reported_sum),\n   117\t    }\n   118\t    metrics = {\n   119\t        \"combined_score\": float(reported_sum),\n   120\t        \"public\": public_metrics,\n   121\t        \"private\": private_metrics,\n   122\t    }\n   123\t\n   124\t    extra_file = os.path.join(results_dir, \"extra.npz\")\n   125\t    try:\n   126\t        np.savez(\n   127\t            extra_file,\n   128\t            centers=centers,\n   129\t            radii=radii,\n   130\t            reported_sum=reported_sum,\n   131\t        )\n   132\t        print(f\"Detailed packing data saved to {extra_file}\")\n   133\t    except Exception as e:\n   134\t        print(f\"Error saving extra.npz: {e}\")\n   135\t        metrics[\"extra_npz_save_error\"] = str(e)\n   136\t\n   137\t    return metrics\n   138\t\n   139\t\n   140\tdef main(program_path: str, results_dir: str):\n   141\t    \"\"\"Runs the circle packing evaluation using shinka.eval.\"\"\"\n   142\t    print(f\"Evaluating program: {program_path}\")\n   143\t    print(f\"Saving results to: {results_dir}\")\n   144\t    os.makedirs(results_dir, exist_ok=True)\n   145\t\n   146\t    num_experiment_runs = 1\n   147\t\n   148\t    # Define a nested function to pass results_dir to the aggregator\n   149\t    def _aggregator_with_context(\n   150\t        r: List[Tuple[np.ndarray, np.ndarray, float]],\n   151\t    ) -> Dict[str, Any]:\n   152\t        return aggregate_circle_packing_metrics(r, results_dir)\n   153\t\n   154\t    metrics, correct, error_msg = run_shinka_eval(\n   155\t        program_path=program_path,\n   156\t        results_dir=results_dir,\n   157\t        experiment_fn_name=\"run_packing\",\n   158\t        num_runs=num_experiment_runs,\n   159\t        get_experiment_kwargs=get_circle_packing_kwargs,\n   160\t        validate_fn=adapted_validate_packing,\n   161\t        aggregate_metrics_fn=_aggregator_with_context,\n   162\t    )\n   163\t\n   164\t    if correct:\n   165\t        print(\"Evaluation and Validation completed successfully.\")\n   166\t    else:\n   167\t        print(f\"Evaluation or Validation failed: {error_msg}\")\n   168\t\n   169\t    print(\"Metrics:\")\n   170\t    for key, value in metrics.items():\n   171\t        if isinstance(value, str) and len(value) > 100:\n   172\t            print(f\"  {key}: <string_too_long_to_display>\")\n   173\t        else:\n   174\t            print(f\"  {key}: {value}\")\n   175\t\n   176\t\n   177\tif __name__ == \"__main__\":\n   178\t    parser = argparse.ArgumentParser(\n   179\t        description=\"Circle packing evaluator using shinka.eval\"\n   180\t    )\n   181\t    parser.add_argument(\n   182\t        \"--program_path\",\n   183\t        type=str,\n   184\t        default=\"tasks/circle_packing/initial.py\",\n   185\t        help=\"Path to program to evaluate (must contain 'run_packing')\",\n   186\t    )\n   187\t    parser.add_argument(\n   188\t        \"--results_dir\",\n   189\t        type=str,\n   190\t        default=\"tasks/circle_packing/results/debug_eval\",\n   191\t        help=\"Dir to save results (metrics.json, correct.json, extra.npz)\",\n   192\t    )\n   193\t    parsed_args = parser.parse_args()\n   194\t    main(parsed_args.program_path, parsed_args.results_dir)\n   195\t\n"}], "role": "tool", "tool_call_id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking. I'm looking at `evaluate_ori.py` and immediately see the core problem: packing 26 circles within a unit square. The goal, as judged by the `combined_score`, is to maximize the sum of the radii.\n\nThe key constraints are no negative radii, all circles inside the square, and no overlaps. The `adapted_validate_packing` function is the gatekeeper here, ensuring these constraints are met. A problem in Gen 4 has become apparent, with a dramatic score drop from Gen 3. The `combined_score` has significantly decreased, indicating something is seriously wrong with the circle packing in Gen 4.\n\nI need to understand why. My initial hypothesis is that either the constraints are being violated (overlaps or boundary violations) or the radii of the circles have shrunk dramatically, causing a lower total. I'll need to dig into this by analyzing the `primary_result` and `main.py` files for Gen 4. It will be helpful to find out where the `extra.npz` files are being saved, since that's where the critical `centers` and `radii` data are stored. The `results_dir` is the place to start.\n\nTo help diagnose this, I need to create some auxiliary metrics. I need to define them in `EVAL_AGENTS.md` since it's currently empty, and then write the code in `auxiliary_metrics.py`.\n\nThe key will be to confirm constraint violations:\n\n*   I'll calculate `aux_total_overlap_distance` and `aux_max_overlap_distance` to quantify the overlap problem.\n*   `aux_boundary_violation_distance` to measure how much circles are going outside the square.\n*   `aux_min_radius` to see if the radii are becoming suspiciously small.\n*   `aux_num_violated_circles`, a simple count of constraint violations.\n\nEssentially, I'm now in a diagnosis phase – either `EXPLORATION` or `OPTIMIZATION` -- where I am trying to figure out why the packing quality has regressed so significantly. The next step is to update `auxiliary_metrics.py` based on this analysis.\n", "signature": null}], "tool_calls": [{"id": "call_93797a325ec240039ed95cf3c33e__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"file_text\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"create\"}"}}]}, {"content": [{"type": "text", "text": "[An error occurred during execution.]\n"}, {"type": "text", "text": "Invalid `path` parameter: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. File already exists at: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. Cannot overwrite files using command `create`."}], "role": "tool", "tool_call_id": "call_93797a325ec240039ed95cf3c33e__thought__CscVAY89a1/dplaQ7YwJJxuinJp8t0U0SJO9M2VVIZWXHQyLhSwUv1IUxiB9F2bxHswczG754lCBorpFW5ea7/2TY5qNleHC0ZKxs2eetjgh6PLRXoGcGyxtaN6hivEmQ/ctWqbqkyYRHkR4/JrFwiNyqPClFDSF19bSVdVgVKwFsylr+cHS+Nk72dv/Po7yH8+MOQhQ3t1ZDZSUaBPd4OgVsKmtLpBzFQxfEPYvYB2mpu+AqglkqmBFZkw40VuR/hPqBxG/xhqehVkIgTm+QR6QtZgK8XGJbDXa+ihsXwR9ZZeoF1gYcN63MZuEIqBZXRX0ht9MaTca/gQtmjmohdtGa8rx/1d7OtwMUFVIT5Mzt3yzwmHvLjjpyb3mAdImlrqfqhBZCEP1MsD9zKgjBQ5GQIZVGGCwLORcsF1tmWYRwmrgVktuKWXXUHbwD3P0zcU7+jfMuafvA+54dCkjJ9FfVt6pf5IszoOdv0hiEhVj8vL7S850E5WdUt/ouklhSOv9oK2/iUFS3+d6SrjHvKzNIVYrihOnABs0enpeNDfCFLV7kIALmwSsjACfsewhK7kvsl3YY7G57mDuEm0telL8LoZS3mGRexjN/0h2Mljv1aF4G/p3WNnvbIlZzCcZAuGbG3uWaZ6De5yXWyerF6XxtxLG6pvlX9qI9i2A7ZKoRQIy8crZ11H7m1Z2c3xsJN3F2w8G90kUlVEt29RCHP3X1dpWoXQMZOFB5LVSLMay/xWD/UPJFZuhbYRr+f46CC3LU0R/DIeWgMGDo5Q9XnfZH5skJxXC2NEMKGTw4ab0gvyovzGBviHgSwb1eQ7IeYwij1mF5OvoofP2E7FEwMiGmk1RhDWq7tFs6n3JMGHRVLDovE6j+2RFZ8i1yF++rwvHdbt/hCSBSGVokvml1Ds6WSvL0FADwf1TDBjbXij+6K3DvVWPf4ebiGqeSuAiIpdHcN8H05L1iflfTAqQy8WTRw4jFQsj/D/ysOtMPg2FBDC4kFDzN2IOvOTZsF1rJ0Mk1BCVxGoj+tGeQx6jVZYV/+Tl4MCFCdF/mQGKcmYGDZXX6jcbiyM1Squu/WG1y+aBJkfbs17ncCNbw/6ld+8v4fLE1SOmje/eVewR1jfb+Y/HpfeVgi7BgbMur6TdrCAnU5pGFsZUMlwq/OZLEbynpMJ4EjbJeMAirXXunk4KH35Y/X9aaFGox3RU8g/N5m16Vd66058kGJAPtKBgxvT6zpZxsM1HSXa1e8xC5r78RFtw6fRP3zZ665HJS0Gv+3Pr8ph+jSPHfLiQVr87mLCQonP+/0VnN/xCHlXCuk4sVIPjIhxEwFZDshbJxiqYwuLndbKgoEsmTlyHyiUAvUvEEvba7Mjql0iU9DFUhqkqheyvdd/V5sYLgwsR8j4rJgIW21lwH6MFxd+7wr13tk+VzbFXowhDWI+XT7bYPh9GbfM8etMcq6BZ3lG0W6hdFsnHBd71HFhY27kexUkNmXeKM1DHINW+vmR++3kW4GulsG+m8REaPPttG0+M8MWiRz2uDB/3D8pKhRviW6g98WaAPlZEwsc3yqxeTp67+gZK9VWZQNZhaDOcU0CePPzc3nkQ2Hbwq+umDhd5FLouzs0jvTcZisMn34oi6MhsorsM6ZFAx7Qth0sw6XxxrARFnyuzROV27K+o80YWkJVCgLdaxmapuXIAtcX4jjTvxmBDJnOdy93241PiwLl4Y4J5xOTjoBG7QbrI7vdYBny8CFzSGN4Li+VG6QMSrxaLg7KgqLgXpWN4PrL/VCYmmnZjQl9xePWDsTTuPFQakBZlgYTuQUrb97gU1j5opY5xkU5EL8uLZaihsIm5Y7JTzve6z+2lGsI3gyjmz1DJ7ciZ1Sh/f3ej/K2CsSAji+JXgjgv4uPZ0xC8RgQE8WMhvHVG/HJuEhmlXgyBDjZ5I5QImUQkFSQG+aNr7NfTA6dFSlB8ri1PwfRHAQWyuZcBNSFB5zdBcRzi6rslYbgo8b1es3cURJ7wgbUhioZynzbcqeKdCLdC8Xq3NgF0gjypt+Ijev+7GyCW22D6VYy4BotxTg17Du3bhI7oBpfpLAogn0oLKAZRfD/+sU38hIF9HdXA8kIjeyZF5R8AnReXxcbFDcmFlsjF1Jy/FYJu9PbzQ5LLOm67edQI5cmQhKwqgTlsIkVYdEP12RPuWXcqT4vDGrfaY9nJHp4x1M4yCnULKhbYW0Z2M/dXqGV7zUuTnSS4v9F1pL3xzOe0qbwl8uRfmmPYmIgPCKZtSVezhl353VEr3Aktsy/2ruH+diIR8os0oJWCwAm94alYKWcxSnCVWKRmZHS4d1VKshAmsDFyBh4UQOwAL6AtvvWW4yz9EQSNELkWUBmrFNg4EnkZ+LxewiT9SkE+kFTtYkgNTIW6wIR4PGOajGIttKcQbWp2uTm/23Hn6QL3RzVxLBOtSGMrRwor2GZXB0bWKRDEejRwdUXrMdlHxF8y7ocfn254aRwB7u8lyEMuAI8ofkgo2igEPbXRYTFLGt4TcI+E1igt6npbWbF9suPqXNompjvdJiCUf1EyXmraIHEr4/wfbhGke1h75KasWQ9eF1bgK2dBTtE1tRbrfDfaI0CkfYm8kOeRNRxoC6f90YG12u+ScRBxbnnoitpvgedtb+XBvkNluonqABYGLU/JDSAJ/XWMYeUmsq7+JVxIx4gSJJrweyQfl2FeRfjYMp6aDpou4mxidMOFicz8UQWYJaUPbcS5PfvgEzAwVtPXzhpRLZgSrOKLH1DbvF95RghtEPSPx5D2Kl/l33LJ3bBE8S//DFCJ18rDHziXxS4q6oeGIPoVXPO7OzS7n2hEkrJxGmnm4XfwgCp4mSrgvh3lnSZ6vLy8XvtXB0GMdZQc9Mw7VzZq6rEAsijQmzdfCOLfwYqxPenyKlxaW1RbOFnqS4nIxNbSd1lxww5ksNBjOQtXJAFB83qHocmqbrPhVEiVJwHnLwj48OGhKAWPGFf2bCOYFmu3YzVzaNGtKs9XlDxqQKHbrYlMzPuUOP34tGDy9LMKomTcZpxhgBphkH/RQffEDQBtLHQtTLP0D0OUS5sgmHQcSwFnClDjTcu1FVR1gZBPL1cUAIwDdepq1NOp+CjBUh1imTlqL3vUdlNueNHLzIkmhK8ARndJY+JlLXnDheeCIAfjhyGa4TsCT5PrWWwWjSzXMFqk4azx6bt/CYhhAKrKX2E1DeY1aaEdunMoeBDyHjrgS6dLQ8yV5gkYASi4+EewiHCiEJV7HY3KYuP+Zq3OOPQdZ3tZbWEMBe7WrWt7Q3/kAQdCpS9HSx0Em5uXb75Re7/SZhWkdhYekIeSo1wJ3cxKid7n7N9LBtKm+LlbNmdoCDxf9shYsArcU0hdkz4Bvt8wK82TtQd+jWCBiluVGrngQWbm+pGmCwTOAAzTuX+kfGN78k218yB3/2DShqG3MW8g2ik9t7ZYn+ilRzx4Q7XWHc+VaIXiLKlDlekHKQkT90haP1oM4VG8f1b77VdIUg9kZ2/MizYqeX26b/Io9m73XCWGIBpt8iO+u8GZv1wxH8wvlwuVZNY8+l3JZKID6eRKg2QQANtwWHZo/Fg71qgMdfdW40bzGR58JQ3gZvjjGbWqd3vd1Es9uX/ubY/i5mfI4UySrKOiuwypEaTUIS4wSkL9vKHGwYGVqX8=", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Right, a slip of the mind, completely my fault. I got ahead of myself and forgot that the system's already laid the groundwork with `EVAL_AGENTS.md`, even if it's currently a blank slate. Silly me, I should have been using `str_replace` or something similar for the update, not a fresh `create`. \n\nOkay, no problem. I'll utilize `str_replace` now. That's the correct way to inject the relevant registry and generation log into that pre-existing file. Time to make sure the initial content is correctly appended.\n", "signature": null}], "tool_calls": [{"id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"str_replace\", \"old_str\": \"# EV2 Agent Memory\\n\\n- Initialized by eval service.\\n- Use this file as compact cross-generation memory.\\n\", \"new_str\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, now that I've meticulously updated `EVAL_AGENTS.md`, ensuring all the agent evaluation criteria are up to snuff, it's time to dive into the code. My next task is to implement the `evaluate_aux` function within `auxiliary_metrics.py`. This is where the real analytical work begins. I'll need to crack open `extra.npz` to extract the crucial `centers` and `radii` data. These parameters are fundamental; they'll serve as the foundation for the upcoming analysis. It’s all about the details; they underpin the whole operation.\n", "signature": null}], "tool_calls": [{"id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"command\": \"str_replace\", \"old_str\": \"def evaluate_aux(results_dir, primary_result=None):\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    return {}\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"new_str\": \"import numpy as np\\nimport os\\nfrom typing import Dict, Any\\n\\ndef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    metrics = {}\\n    try:\\n        extra_file_path = os.path.join(results_dir, \\\"extra.npz\\\")\\n        if not os.path.exists(extra_file_path):\\n            print(f\\\"Warning: extra.npz not found at {extra_file_path}\\\")\\n            return metrics\\n\\n        data = np.load(extra_file_path)\\n        centers = data[\\\"centers\\\"]\\n        radii = data[\\\"radii\\\"]\\n\\n        num_circles = centers.shape[0]\\n        \\n        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\\n        total_overlap_distance = 0.0\\n        max_overlap_distance = 0.0\\n        num_overlapping_pairs = 0\\n\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    overlap = min_dist_for_no_overlap - dist\\n                    total_overlap_distance += overlap\\n                    if overlap > max_overlap_distance:\\n                        max_overlap_distance = overlap\\n                    num_overlapping_pairs += 1\\n\\n        metrics[\\\"aux_total_overlap_distance\\\"] = float(total_overlap_distance)\\n        metrics[\\\"aux_max_overlap_distance\\\"] = float(max_overlap_distance)\\n        \\n        # --- aux_boundary_violation_distance ---\\n        boundary_violation_distance = 0.0\\n        \\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n\\n            # Check x-bounds\\n            if x - r < 0:\\n                boundary_violation_distance += abs(x - r)\\n            if x + r > 1:\\n                boundary_violation_distance += abs(x + r - 1)\\n            \\n            # Check y-bounds\\n            if y - r < 0:\\n                boundary_violation_distance += abs(y - r)\\n            if y + r > 1:\\n                boundary_violation_distance += abs(y + r - 1)\\n                \\n        metrics[\\\"aux_boundary_violation_distance\\\"] = float(boundary_violation_distance)\\n\\n        # --- aux_min_radius ---\\n        metrics[\\\"aux_min_radius\\\"] = float(np.min(radii)) if radii.size > 0 else 0.0\\n        \\n        # --- aux_num_violated_circles ---\\n        num_violated_circles = 0\\n        violated_circles_indices = set()\\n\\n        # Check for overlaps (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    violated_circles_indices.add(i)\\n                    violated_circles_indices.add(j)\\n\\n        # Check for boundary violations (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\\n                violated_circles_indices.add(i)\\n                \\n        metrics[\\\"aux_num_violated_circles\\\"] = len(violated_circles_indices)\\n\\n\\n    except Exception as e:\\n        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        # Return default 0.0 for all metrics in case of failure\\n        metrics[\\\"aux_total_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_max_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_boundary_violation_distance\\\"] = 0.0\\n        metrics[\\\"aux_min_radius\\\"] = 0.0\\n        metrics[\\\"aux_num_violated_circles\\\"] = 0\\n        \\n    return metrics\\n\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"]\n    17\t\n    18\t        num_circles = centers.shape[0]\n    19\t        \n    20\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    21\t        total_overlap_distance = 0.0\n    22\t        max_overlap_distance = 0.0\n    23\t        num_overlapping_pairs = 0\n    24\t\n    25\t        for i in range(num_circles):\n    26\t            for j in range(i + 1, num_circles):\n    27\t                dist = np.linalg.norm(centers[i] - centers[j])\n    28\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    29\t                if dist < min_dist_for_no_overlap:\n    30\t                    overlap = min_dist_for_no_overlap - dist\n    31\t                    total_overlap_distance += overlap\n    32\t                    if overlap > max_overlap_distance:\n    33\t                        max_overlap_distance = overlap\n    34\t                    num_overlapping_pairs += 1\n    35\t\n    36\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    37\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    38\t        \n    39\t        # --- aux_boundary_violation_distance ---\n    40\t        boundary_violation_distance = 0.0\n    41\t        \n    42\t        for i in range(num_circles):\n    43\t            x, y = centers[i]\n    44\t            r = radii[i]\n    45\t\n    46\t            # Check x-bounds\n    47\t            if x - r < 0:\n    48\t                boundary_violation_distance += abs(x - r)\n    49\t            if x + r > 1:\n    50\t                boundary_violation_distance += abs(x + r - 1)\n    51\t            \n    52\t            # Check y-bounds\n    53\t            if y - r < 0:\n    54\t                boundary_violation_distance += abs(y - r)\n    55\t            if y + r > 1:\n    56\t                boundary_violation_distance += abs(y + r - 1)\n    57\t                \n    58\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    59\t\n    60\t        # --- aux_min_radius ---\n    61\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    62\t        \n    63\t        # --- aux_num_violated_circles ---\n    64\t        num_violated_circles = 0\n    65\t        violated_circles_indices = set()\n    66\t\n    67\t        # Check for overlaps (re-do from above to get specific circles)\n    68\t        for i in range(num_circles):\n    69\t            for j in range(i + 1, num_circles):\n    70\t                dist = np.linalg.norm(centers[i] - centers[j])\n    71\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    72\t                if dist < min_dist_for_no_overlap:\n    73\t                    violated_circles_indices.add(i)\n    74\t                    violated_circles_indices.add(j)\n    75\t\n    76\t        # Check for boundary violations (re-do from above to get specific circles)\n    77\t        for i in range(num_circles):\n    78\t            x, y = centers[i]\n    79\t            r = radii[i]\n    80\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    81\t                violated_circles_indices.add(i)\n    82\t                \n    83\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    84\t\n    85\t\n    86\t    except Exception as e:\n    87\t        print(f\"Error in evaluate_aux: {e}\")\n    88\t        # Return default 0.0 for all metrics in case of failure\n    89\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    90\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    91\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    92\t        metrics[\"aux_min_radius\"] = 0.0\n    93\t        metrics[\"aux_num_violated_circles\"] = 0\n    94\t        \n    95\t    return metrics\n    96\t\n    97\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "name": "file_editor"}, {"content": [], "role": "assistant"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking:\n\nAlright, that `auxiliary_metrics.py` script is finally updated with the new metrics. Now, the real work begins. I need to verify that it's robust and actually *works* with the current data. I can't just assume it'll run flawlessly, especially after adding new calculations.\n\nSo, I'll leverage that `call_tool` function to trigger `evaluate_aux`. That's the most efficient way to get it running. First things first though; I need to get the `results_dir` for `gen_4`. That's where the data for this generation of the project lives. Then, I'll be able to run `evaluate_aux` and confirm that everything is behaving as expected. No errors, and, more importantly, the output needs to be valid. The integrity of the results is paramount.\n", "signature": null}], "tool_calls": [{"id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"python3 -c 'from eval_agent.tool_box.api import call_tool; import os; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))'\", \"summary\": \"Smoke test auxiliary_metrics.py for gen_4.\"}"}}]}, {"content": [{"type": "text", "text": "Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    from eval_agent.tool_box.api import call_tool; import os; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'eval_agent'\n[The command completed with exit code 1.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 1]"}], "role": "tool", "tool_call_id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as I analyze this failure.\n\nUgh, the smoke test just bombed. I got a `ModuleNotFoundError: No module named 'eval_agent'`. Right, that makes sense. I was trying to import `eval_agent.tool_box.api` in that direct `python3 -c` call. The problem is, the Python interpreter spawned by that command probably doesn't have the `eval_agent` package in its search path. That's a classic gotcha.\n\nOn top of that, I think I messed up the command itself. I'm calling `evaluate_aux`, which is actually defined in `auxiliary_metrics.py`.  That file is *right here* in the current directory, inside `eval_agent_memory`, so I should be able to import it directly. I also realize now that the `call_tool` function is totally unnecessary in that `python3 -c` snippet for `evaluate_aux`.\n\nAlright, let's fix these issues. I'll need to adjust the import path to properly reference `auxiliary_metrics.py`. I have to be absolutely sure that the import works correctly. I'll focus on ensuring that this local import works. It's crucial.\n", "signature": null}], "tool_calls": [{"id": "call_c6dad52b16544ff6ad6d15fac601__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"summary\": \"Smoke test auxiliary_metrics.py for gen_4 by adding current directory to sys.path.\", \"command\": \"python3 -c 'import os; import sys; sys.path.insert(0, \\\".\\\"); from auxiliary_metrics import evaluate_aux; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; print(evaluate_aux(results_dir))'; \", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "python3 -c 'import os; import sys; sys.path.insert(0, \".\"); from auxiliary_metrics import evaluate_aux; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; print(evaluate_aux(results_dir))'\n{'aux_total_overlap_distance': 0.0, 'aux_max_overlap_distance': 0.0, 'aux_boundary_violation_distance': 0.0, 'aux_min_radius': 0.0, 'aux_num_violated_circles': 0}\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_c6dad52b16544ff6ad6d15fac601__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, the smoke test just finished, and though it ran without errors, all the evaluation metrics are reporting zero. That immediately flags a problem with the data. My gut tells me something's up with the `extra.npz` file, specifically within the `gen_4/results` directory of the `gen_100` run. It seems like either the file isn't present, or more likely, it's empty or got corrupted during the process. The results are being pulled from the `results_circle_packing_v2_1_gen100_periodic5_20260226_023318` run, so I need to check the file system. Right, let me go check if that `extra.npz` file actually exists, and if it does, what its contents look like within the `results` directory. Need to dive in there and see what's what.\n", "signature": null}], "tool_calls": [{"id": "call_319dcfe7a9ef42db87cdefdb77fe__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"summary\": \"Check contents of gen_4 results directory.\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results/\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "total 16\n-rw-r--r-- 1 tengxiao tengxiao  112 Feb 26 02:41 auxiliary_metrics_snapshot.py\n-rw-r--r-- 1 tengxiao tengxiao   42 Feb 26 02:41 correct.json\n-rw-r--r-- 1 tengxiao tengxiao 1398 Feb 26 02:41 extra.npz\n-rw-r--r-- 1 tengxiao tengxiao 1666 Feb 26 02:41 metrics.json\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_319dcfe7a9ef42db87cdefdb77fe__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking:\n\nAlright, that `extra.npz` file, I know for a fact it's sitting right there in `gen_4/results`. So the issue *isn't* the file's absence. That eliminates a whole class of potential problems.  My gut says `np.load` is the culprit. Either it choked on the format, or the data inside wasn't structured how I anticipated. That's what's triggering the `except` block and causing all the metrics to zero out.\n\nRight, time to dive deeper. I'll need to see the full stack trace to pinpoint exactly *where* and *why* `np.load` is failing.  I'll modify `auxiliary_metrics.py` to spit out all the gory details of the exception. That should give me the diagnostic clues I need to fix this quickly. \n", "signature": null}], "tool_calls": [{"id": "call_0bd8712946544dd8ae3779039a57__thought__CosDAY89a19ZPkn33fmv6iOyiJufWYrA76svHM0eOaaxEWHBwh+sQcEO8l8F3pdhRUyVG2gZG9zUYBuUCPO6Atu48i4x69+2dalINDxElGpwGiR3gerL42bNw5h14qhYSizP10fDxli4K3JTlEuUu5ozAIeM1ue4Fpo0F3TzL+J/8FjoZ9sR4fz5+grTVZWPP1LeYYg1VfUHtOtNT8xZUFWIzxaBj2LdDRJiEQMJ8a1rrEB4anHksvvrM4pUWvnpDhWdycAMVbtZoeP0PwEJxVmSVzsclzuGS+ecnn6QFs0mcY4vE/5jdV/fLiWKyArIg2CfPslGYASgE8QDwK1kmb5/V1+neDZRwN1USBLL6i4eOhGoiYJqhKX/codZm6MVThBSyT2GhUUJ5IQTMUfrWXwDRelV//X6Z2g/pm/EXEyRrrgsDkP1alb/hH55FH16Y6XBCMvyM0reK3sij1vmyhcdP5wwosLgtR01MJ9bXRH1xprBJwiEDjSDg7/iVrvsfDaK2btGOl2sTNYPvPs=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"old_str\": \"        print(f\\\"Error in evaluate_aux: {e}\\\")\", \"new_str\": \"        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        import traceback\\n        traceback.print_exc()\", \"command\": \"str_replace\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n    84\t\n    85\t\n    86\t    except Exception as e:\n    87\t        print(f\"Error in evaluate_aux: {e}\")\n    88\t        import traceback\n    89\t        traceback.print_exc()\n    90\t        # Return default 0.0 for all metrics in case of failure\n    91\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    92\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    93\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    94\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_0bd8712946544dd8ae3779039a57__thought__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", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "ErOfaYeVN53F694PuZW8wQw", "created": 1772073746, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "ClcBjz1rX4++5gq0qCJQvQrvUdLpvXeOcV+0eJzfBXBAxfBVj+CmobKNuGCu7Ci5A5Kmek0PcmjBC7LnktL1ZkGbxdyKMbjXkenkrdZWiXeevC1W8oTZ0hg="}, "function": {"arguments": "{\"command\": \"python3 -c 'import os; import sys; sys.path.insert(0, \\\".\\\"); from auxiliary_metrics import evaluate_aux; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; print(evaluate_aux(results_dir))'; \", \"summary\": \"Smoke test auxiliary_metrics.py for gen_4 with traceback enabled.\", \"security_risk\": \"LOW\"}", "name": "terminal"}, "id": "call_85022261e90b42d380002ce4ec52__thought__ClcBjz1rX4++5gq0qCJQvQrvUdLpvXeOcV+0eJzfBXBAxfBVj+CmobKNuGCu7Ci5A5Kmek0PcmjBC7LnktL1ZkGbxdyKMbjXkenkrdZWiXeevC1W8oTZ0hg=", "type": "function"}], "images": [], "reasoning_content": "Alright, time to get down to brass tacks. I need to reproduce the error, and the best way to do that, in this situation, is a fresh smoke test. Let's see that traceback again. I need to pinpoint the exact line or sequence of operations that's causing this problem. It’s the only way to effectively diagnose and resolve this.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, time to get down to brass tacks. I need to reproduce the error, and the best way to do that, in this situation, is a fresh smoke test. Let's see that traceback again. I need to pinpoint the exact line or sequence of operations that's causing this problem. It’s the only way to effectively diagnose and resolve this.\n"}], "provider_specific_fields": {"thought_signatures": ["ClcBjz1rX4++5gq0qCJQvQrvUdLpvXeOcV+0eJzfBXBAxfBVj+CmobKNuGCu7Ci5A5Kmek0PcmjBC7LnktL1ZkGbxdyKMbjXkenkrdZWiXeevC1W8oTZ0hg="]}}}], "usage": {"completion_tokens": 148, "prompt_tokens": 17038, "total_tokens": 17186, "completion_tokens_details": {"reasoning_tokens": 16, "text_tokens": 132}, "prompt_tokens_details": {"text_tokens": 17038}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.005481399999999999, "timestamp": 1772073749.2062411, "latency_sec": 2.321730852127075, "usage_summary": {"prompt_tokens": 17038, "completion_tokens": 148, "reasoning_tokens": 16, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073755.604-7421.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": "call_ad172c287e414c36a7999f9b4215__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "signature": null}], "tool_calls": [{"id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\tdef evaluate_aux(results_dir, primary_result=None):\n     2\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     3\t    return {}\n     4\t\n"}], "role": "tool", "tool_call_id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n", "signature": null}], "tool_calls": [{"id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"summary\": \"View evaluate_ori.py to understand the primary evaluation metric.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py:\n     1\t\"\"\"\n     2\tEvaluator for circle packing example (n=26) with improved timeout handling\n     3\t\"\"\"\n     4\t\n     5\timport os\n     6\timport argparse\n     7\timport sys\n     8\timport numpy as np\n     9\tfrom typing import Tuple, Optional, List, Dict, Any\n    10\tfrom pathlib import Path\n    11\t\n    12\t# Add project root to import path for direct file execution.\n    13\tsys.path.insert(0, str(Path(__file__).resolve().parents[2]))\n    14\t\n    15\tfrom shinka.core import run_shinka_eval\n    16\t\n    17\t\n    18\tdef format_centers_string(centers: np.ndarray) -> str:\n    19\t    \"\"\"Formats circle centers into a multi-line string for display.\"\"\"\n    20\t    return \"\\n\".join(\n    21\t        [\n    22\t            f\"  centers[{i}] = ({x_coord:.4f}, {y_coord:.4f})\"\n    23\t            for i, (x_coord, y_coord) in enumerate(centers)\n    24\t        ]\n    25\t    )\n    26\t\n    27\t\n    28\tdef adapted_validate_packing(\n    29\t    run_output: Tuple[np.ndarray, np.ndarray, float],\n    30\t    atol=1e-6,\n    31\t) -> Tuple[bool, Optional[str]]:\n    32\t    \"\"\"\n    33\t    Validates circle packing results based on the output of 'run_packing'.\n    34\t\n    35\t    Args:\n    36\t        run_output: Tuple (centers, radii, reported_sum) from run_packing.\n    37\t\n    38\t    Returns:\n    39\t        (is_valid: bool, error_message: Optional[str])\n    40\t    \"\"\"\n    41\t    centers, radii, reported_sum = run_output\n    42\t    msg = \"The circles are placed correctly. There are no overlaps or any circles outside the unit square.\"\n    43\t    if not isinstance(centers, np.ndarray):\n    44\t        centers = np.array(centers)\n    45\t    if not isinstance(radii, np.ndarray):\n    46\t        radii = np.array(radii)\n    47\t\n    48\t    n_expected = 26\n    49\t    if centers.shape != (n_expected, 2):\n    50\t        msg = (\n    51\t            f\"Centers shape incorrect. Expected ({n_expected}, 2), got {centers.shape}\"\n    52\t        )\n    53\t        return False, msg\n    54\t    if radii.shape != (n_expected,):\n    55\t        msg = f\"Radii shape incorrect. Expected ({n_expected},), got {radii.shape}\"\n    56\t        return False, msg\n    57\t\n    58\t    if np.any(radii < 0):\n    59\t        negative_indices = np.where(radii < 0)[0]\n    60\t        msg = f\"Negative radii found for circles at indices: {negative_indices}\"\n    61\t        return False, msg\n    62\t\n    63\t    if not np.isclose(np.sum(radii), reported_sum, atol=atol):\n    64\t        msg = (\n    65\t            f\"Sum of radii ({np.sum(radii):.6f}) does not match \"\n    66\t            f\"reported ({reported_sum:.6f})\"\n    67\t        )\n    68\t        return False, msg\n    69\t\n    70\t    for i in range(n_expected):\n    71\t        x, y = centers[i]\n    72\t        r = radii[i]\n    73\t        is_outside = (\n    74\t            x - r < -atol or x + r > 1 + atol or y - r < -atol or y + r > 1 + atol\n    75\t        )\n    76\t        if is_outside:\n    77\t            msg = (\n    78\t                f\"Circle {i} (x={x:.4f}, y={y:.4f}, r={r:.4f}) is outside unit square.\"\n    79\t            )\n    80\t            return False, msg\n    81\t\n    82\t    for i in range(n_expected):\n    83\t        for j in range(i + 1, n_expected):\n    84\t            dist = np.sqrt(np.sum((centers[i] - centers[j]) ** 2))\n    85\t            if dist < radii[i] + radii[j] - atol:\n    86\t                msg = (\n    87\t                    f\"Circles {i} & {j} overlap. Dist: {dist:.4f}, \"\n    88\t                    f\"Sum Radii: {(radii[i] + radii[j]):.4f}\"\n    89\t                )\n    90\t                return False, msg\n    91\t    return True, msg\n    92\t\n    93\t\n    94\tdef get_circle_packing_kwargs(run_index: int) -> Dict[str, Any]:\n    95\t    \"\"\"Provides keyword arguments for circle packing runs (none needed).\"\"\"\n    96\t    return {}\n    97\t\n    98\t\n    99\tdef aggregate_circle_packing_metrics(\n   100\t    results: List[Tuple[np.ndarray, np.ndarray, float]], results_dir: str\n   101\t) -> Dict[str, Any]:\n   102\t    \"\"\"\n   103\t    Aggregates metrics for circle packing. Assumes num_runs=1.\n   104\t    Saves extra.npz with detailed packing information.\n   105\t    \"\"\"\n   106\t    if not results:\n   107\t        return {\"combined_score\": 0.0, \"error\": \"No results to aggregate\"}\n   108\t\n   109\t    centers, radii, reported_sum = results[0]\n   110\t\n   111\t    public_metrics = {\n   112\t        \"centers_str\": format_centers_string(centers),\n   113\t        \"num_circles\": centers.shape[0],\n   114\t    }\n   115\t    private_metrics = {\n   116\t        \"reported_sum_of_radii\": float(reported_sum),\n   117\t    }\n   118\t    metrics = {\n   119\t        \"combined_score\": float(reported_sum),\n   120\t        \"public\": public_metrics,\n   121\t        \"private\": private_metrics,\n   122\t    }\n   123\t\n   124\t    extra_file = os.path.join(results_dir, \"extra.npz\")\n   125\t    try:\n   126\t        np.savez(\n   127\t            extra_file,\n   128\t            centers=centers,\n   129\t            radii=radii,\n   130\t            reported_sum=reported_sum,\n   131\t        )\n   132\t        print(f\"Detailed packing data saved to {extra_file}\")\n   133\t    except Exception as e:\n   134\t        print(f\"Error saving extra.npz: {e}\")\n   135\t        metrics[\"extra_npz_save_error\"] = str(e)\n   136\t\n   137\t    return metrics\n   138\t\n   139\t\n   140\tdef main(program_path: str, results_dir: str):\n   141\t    \"\"\"Runs the circle packing evaluation using shinka.eval.\"\"\"\n   142\t    print(f\"Evaluating program: {program_path}\")\n   143\t    print(f\"Saving results to: {results_dir}\")\n   144\t    os.makedirs(results_dir, exist_ok=True)\n   145\t\n   146\t    num_experiment_runs = 1\n   147\t\n   148\t    # Define a nested function to pass results_dir to the aggregator\n   149\t    def _aggregator_with_context(\n   150\t        r: List[Tuple[np.ndarray, np.ndarray, float]],\n   151\t    ) -> Dict[str, Any]:\n   152\t        return aggregate_circle_packing_metrics(r, results_dir)\n   153\t\n   154\t    metrics, correct, error_msg = run_shinka_eval(\n   155\t        program_path=program_path,\n   156\t        results_dir=results_dir,\n   157\t        experiment_fn_name=\"run_packing\",\n   158\t        num_runs=num_experiment_runs,\n   159\t        get_experiment_kwargs=get_circle_packing_kwargs,\n   160\t        validate_fn=adapted_validate_packing,\n   161\t        aggregate_metrics_fn=_aggregator_with_context,\n   162\t    )\n   163\t\n   164\t    if correct:\n   165\t        print(\"Evaluation and Validation completed successfully.\")\n   166\t    else:\n   167\t        print(f\"Evaluation or Validation failed: {error_msg}\")\n   168\t\n   169\t    print(\"Metrics:\")\n   170\t    for key, value in metrics.items():\n   171\t        if isinstance(value, str) and len(value) > 100:\n   172\t            print(f\"  {key}: <string_too_long_to_display>\")\n   173\t        else:\n   174\t            print(f\"  {key}: {value}\")\n   175\t\n   176\t\n   177\tif __name__ == \"__main__\":\n   178\t    parser = argparse.ArgumentParser(\n   179\t        description=\"Circle packing evaluator using shinka.eval\"\n   180\t    )\n   181\t    parser.add_argument(\n   182\t        \"--program_path\",\n   183\t        type=str,\n   184\t        default=\"tasks/circle_packing/initial.py\",\n   185\t        help=\"Path to program to evaluate (must contain 'run_packing')\",\n   186\t    )\n   187\t    parser.add_argument(\n   188\t        \"--results_dir\",\n   189\t        type=str,\n   190\t        default=\"tasks/circle_packing/results/debug_eval\",\n   191\t        help=\"Dir to save results (metrics.json, correct.json, extra.npz)\",\n   192\t    )\n   193\t    parsed_args = parser.parse_args()\n   194\t    main(parsed_args.program_path, parsed_args.results_dir)\n   195\t\n"}], "role": "tool", "tool_call_id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking. I'm looking at `evaluate_ori.py` and immediately see the core problem: packing 26 circles within a unit square. The goal, as judged by the `combined_score`, is to maximize the sum of the radii.\n\nThe key constraints are no negative radii, all circles inside the square, and no overlaps. The `adapted_validate_packing` function is the gatekeeper here, ensuring these constraints are met. A problem in Gen 4 has become apparent, with a dramatic score drop from Gen 3. The `combined_score` has significantly decreased, indicating something is seriously wrong with the circle packing in Gen 4.\n\nI need to understand why. My initial hypothesis is that either the constraints are being violated (overlaps or boundary violations) or the radii of the circles have shrunk dramatically, causing a lower total. I'll need to dig into this by analyzing the `primary_result` and `main.py` files for Gen 4. It will be helpful to find out where the `extra.npz` files are being saved, since that's where the critical `centers` and `radii` data are stored. The `results_dir` is the place to start.\n\nTo help diagnose this, I need to create some auxiliary metrics. I need to define them in `EVAL_AGENTS.md` since it's currently empty, and then write the code in `auxiliary_metrics.py`.\n\nThe key will be to confirm constraint violations:\n\n*   I'll calculate `aux_total_overlap_distance` and `aux_max_overlap_distance` to quantify the overlap problem.\n*   `aux_boundary_violation_distance` to measure how much circles are going outside the square.\n*   `aux_min_radius` to see if the radii are becoming suspiciously small.\n*   `aux_num_violated_circles`, a simple count of constraint violations.\n\nEssentially, I'm now in a diagnosis phase – either `EXPLORATION` or `OPTIMIZATION` -- where I am trying to figure out why the packing quality has regressed so significantly. The next step is to update `auxiliary_metrics.py` based on this analysis.\n", "signature": null}], "tool_calls": [{"id": "call_93797a325ec240039ed95cf3c33e__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"file_text\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"create\"}"}}]}, {"content": [{"type": "text", "text": "[An error occurred during execution.]\n"}, {"type": "text", "text": "Invalid `path` parameter: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. File already exists at: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. Cannot overwrite files using command `create`."}], "role": "tool", "tool_call_id": "call_93797a325ec240039ed95cf3c33e__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Right, a slip of the mind, completely my fault. I got ahead of myself and forgot that the system's already laid the groundwork with `EVAL_AGENTS.md`, even if it's currently a blank slate. Silly me, I should have been using `str_replace` or something similar for the update, not a fresh `create`. \n\nOkay, no problem. I'll utilize `str_replace` now. That's the correct way to inject the relevant registry and generation log into that pre-existing file. Time to make sure the initial content is correctly appended.\n", "signature": null}], "tool_calls": [{"id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"str_replace\", \"old_str\": \"# EV2 Agent Memory\\n\\n- Initialized by eval service.\\n- Use this file as compact cross-generation memory.\\n\", \"new_str\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, now that I've meticulously updated `EVAL_AGENTS.md`, ensuring all the agent evaluation criteria are up to snuff, it's time to dive into the code. My next task is to implement the `evaluate_aux` function within `auxiliary_metrics.py`. This is where the real analytical work begins. I'll need to crack open `extra.npz` to extract the crucial `centers` and `radii` data. These parameters are fundamental; they'll serve as the foundation for the upcoming analysis. It’s all about the details; they underpin the whole operation.\n", "signature": null}], "tool_calls": [{"id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"command\": \"str_replace\", \"old_str\": \"def evaluate_aux(results_dir, primary_result=None):\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    return {}\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"new_str\": \"import numpy as np\\nimport os\\nfrom typing import Dict, Any\\n\\ndef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    metrics = {}\\n    try:\\n        extra_file_path = os.path.join(results_dir, \\\"extra.npz\\\")\\n        if not os.path.exists(extra_file_path):\\n            print(f\\\"Warning: extra.npz not found at {extra_file_path}\\\")\\n            return metrics\\n\\n        data = np.load(extra_file_path)\\n        centers = data[\\\"centers\\\"]\\n        radii = data[\\\"radii\\\"]\\n\\n        num_circles = centers.shape[0]\\n        \\n        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\\n        total_overlap_distance = 0.0\\n        max_overlap_distance = 0.0\\n        num_overlapping_pairs = 0\\n\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    overlap = min_dist_for_no_overlap - dist\\n                    total_overlap_distance += overlap\\n                    if overlap > max_overlap_distance:\\n                        max_overlap_distance = overlap\\n                    num_overlapping_pairs += 1\\n\\n        metrics[\\\"aux_total_overlap_distance\\\"] = float(total_overlap_distance)\\n        metrics[\\\"aux_max_overlap_distance\\\"] = float(max_overlap_distance)\\n        \\n        # --- aux_boundary_violation_distance ---\\n        boundary_violation_distance = 0.0\\n        \\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n\\n            # Check x-bounds\\n            if x - r < 0:\\n                boundary_violation_distance += abs(x - r)\\n            if x + r > 1:\\n                boundary_violation_distance += abs(x + r - 1)\\n            \\n            # Check y-bounds\\n            if y - r < 0:\\n                boundary_violation_distance += abs(y - r)\\n            if y + r > 1:\\n                boundary_violation_distance += abs(y + r - 1)\\n                \\n        metrics[\\\"aux_boundary_violation_distance\\\"] = float(boundary_violation_distance)\\n\\n        # --- aux_min_radius ---\\n        metrics[\\\"aux_min_radius\\\"] = float(np.min(radii)) if radii.size > 0 else 0.0\\n        \\n        # --- aux_num_violated_circles ---\\n        num_violated_circles = 0\\n        violated_circles_indices = set()\\n\\n        # Check for overlaps (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    violated_circles_indices.add(i)\\n                    violated_circles_indices.add(j)\\n\\n        # Check for boundary violations (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\\n                violated_circles_indices.add(i)\\n                \\n        metrics[\\\"aux_num_violated_circles\\\"] = len(violated_circles_indices)\\n\\n\\n    except Exception as e:\\n        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        # Return default 0.0 for all metrics in case of failure\\n        metrics[\\\"aux_total_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_max_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_boundary_violation_distance\\\"] = 0.0\\n        metrics[\\\"aux_min_radius\\\"] = 0.0\\n        metrics[\\\"aux_num_violated_circles\\\"] = 0\\n        \\n    return metrics\\n\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"]\n    17\t\n    18\t        num_circles = centers.shape[0]\n    19\t        \n    20\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    21\t        total_overlap_distance = 0.0\n    22\t        max_overlap_distance = 0.0\n    23\t        num_overlapping_pairs = 0\n    24\t\n    25\t        for i in range(num_circles):\n    26\t            for j in range(i + 1, num_circles):\n    27\t                dist = np.linalg.norm(centers[i] - centers[j])\n    28\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    29\t                if dist < min_dist_for_no_overlap:\n    30\t                    overlap = min_dist_for_no_overlap - dist\n    31\t                    total_overlap_distance += overlap\n    32\t                    if overlap > max_overlap_distance:\n    33\t                        max_overlap_distance = overlap\n    34\t                    num_overlapping_pairs += 1\n    35\t\n    36\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    37\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    38\t        \n    39\t        # --- aux_boundary_violation_distance ---\n    40\t        boundary_violation_distance = 0.0\n    41\t        \n    42\t        for i in range(num_circles):\n    43\t            x, y = centers[i]\n    44\t            r = radii[i]\n    45\t\n    46\t            # Check x-bounds\n    47\t            if x - r < 0:\n    48\t                boundary_violation_distance += abs(x - r)\n    49\t            if x + r > 1:\n    50\t                boundary_violation_distance += abs(x + r - 1)\n    51\t            \n    52\t            # Check y-bounds\n    53\t            if y - r < 0:\n    54\t                boundary_violation_distance += abs(y - r)\n    55\t            if y + r > 1:\n    56\t                boundary_violation_distance += abs(y + r - 1)\n    57\t                \n    58\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    59\t\n    60\t        # --- aux_min_radius ---\n    61\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    62\t        \n    63\t        # --- aux_num_violated_circles ---\n    64\t        num_violated_circles = 0\n    65\t        violated_circles_indices = set()\n    66\t\n    67\t        # Check for overlaps (re-do from above to get specific circles)\n    68\t        for i in range(num_circles):\n    69\t            for j in range(i + 1, num_circles):\n    70\t                dist = np.linalg.norm(centers[i] - centers[j])\n    71\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    72\t                if dist < min_dist_for_no_overlap:\n    73\t                    violated_circles_indices.add(i)\n    74\t                    violated_circles_indices.add(j)\n    75\t\n    76\t        # Check for boundary violations (re-do from above to get specific circles)\n    77\t        for i in range(num_circles):\n    78\t            x, y = centers[i]\n    79\t            r = radii[i]\n    80\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    81\t                violated_circles_indices.add(i)\n    82\t                \n    83\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    84\t\n    85\t\n    86\t    except Exception as e:\n    87\t        print(f\"Error in evaluate_aux: {e}\")\n    88\t        # Return default 0.0 for all metrics in case of failure\n    89\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    90\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    91\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    92\t        metrics[\"aux_min_radius\"] = 0.0\n    93\t        metrics[\"aux_num_violated_circles\"] = 0\n    94\t        \n    95\t    return metrics\n    96\t\n    97\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "name": "file_editor"}, {"content": [], "role": "assistant"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking:\n\nAlright, that `auxiliary_metrics.py` script is finally updated with the new metrics. Now, the real work begins. I need to verify that it's robust and actually *works* with the current data. I can't just assume it'll run flawlessly, especially after adding new calculations.\n\nSo, I'll leverage that `call_tool` function to trigger `evaluate_aux`. That's the most efficient way to get it running. First things first though; I need to get the `results_dir` for `gen_4`. That's where the data for this generation of the project lives. Then, I'll be able to run `evaluate_aux` and confirm that everything is behaving as expected. No errors, and, more importantly, the output needs to be valid. The integrity of the results is paramount.\n", "signature": null}], "tool_calls": [{"id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"python3 -c 'from eval_agent.tool_box.api import call_tool; import os; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))'\", \"summary\": \"Smoke test auxiliary_metrics.py for gen_4.\"}"}}]}, {"content": [{"type": "text", "text": "Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    from eval_agent.tool_box.api import call_tool; import os; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'eval_agent'\n[The command completed with exit code 1.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 1]"}], "role": "tool", "tool_call_id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as I analyze this failure.\n\nUgh, the smoke test just bombed. I got a `ModuleNotFoundError: No module named 'eval_agent'`. Right, that makes sense. I was trying to import `eval_agent.tool_box.api` in that direct `python3 -c` call. The problem is, the Python interpreter spawned by that command probably doesn't have the `eval_agent` package in its search path. That's a classic gotcha.\n\nOn top of that, I think I messed up the command itself. I'm calling `evaluate_aux`, which is actually defined in `auxiliary_metrics.py`.  That file is *right here* in the current directory, inside `eval_agent_memory`, so I should be able to import it directly. I also realize now that the `call_tool` function is totally unnecessary in that `python3 -c` snippet for `evaluate_aux`.\n\nAlright, let's fix these issues. I'll need to adjust the import path to properly reference `auxiliary_metrics.py`. I have to be absolutely sure that the import works correctly. I'll focus on ensuring that this local import works. It's crucial.\n", "signature": null}], "tool_calls": [{"id": "call_c6dad52b16544ff6ad6d15fac601__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"summary\": \"Smoke test auxiliary_metrics.py for gen_4 by adding current directory to sys.path.\", \"command\": \"python3 -c 'import os; import sys; sys.path.insert(0, \\\".\\\"); from auxiliary_metrics import evaluate_aux; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; print(evaluate_aux(results_dir))'; \", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "python3 -c 'import os; import sys; sys.path.insert(0, \".\"); from auxiliary_metrics import evaluate_aux; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; print(evaluate_aux(results_dir))'\n{'aux_total_overlap_distance': 0.0, 'aux_max_overlap_distance': 0.0, 'aux_boundary_violation_distance': 0.0, 'aux_min_radius': 0.0, 'aux_num_violated_circles': 0}\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_c6dad52b16544ff6ad6d15fac601__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, the smoke test just finished, and though it ran without errors, all the evaluation metrics are reporting zero. That immediately flags a problem with the data. My gut tells me something's up with the `extra.npz` file, specifically within the `gen_4/results` directory of the `gen_100` run. It seems like either the file isn't present, or more likely, it's empty or got corrupted during the process. The results are being pulled from the `results_circle_packing_v2_1_gen100_periodic5_20260226_023318` run, so I need to check the file system. Right, let me go check if that `extra.npz` file actually exists, and if it does, what its contents look like within the `results` directory. Need to dive in there and see what's what.\n", "signature": null}], "tool_calls": [{"id": "call_319dcfe7a9ef42db87cdefdb77fe__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"summary\": \"Check contents of gen_4 results directory.\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results/\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "total 16\n-rw-r--r-- 1 tengxiao tengxiao  112 Feb 26 02:41 auxiliary_metrics_snapshot.py\n-rw-r--r-- 1 tengxiao tengxiao   42 Feb 26 02:41 correct.json\n-rw-r--r-- 1 tengxiao tengxiao 1398 Feb 26 02:41 extra.npz\n-rw-r--r-- 1 tengxiao tengxiao 1666 Feb 26 02:41 metrics.json\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_319dcfe7a9ef42db87cdefdb77fe__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking:\n\nAlright, that `extra.npz` file, I know for a fact it's sitting right there in `gen_4/results`. So the issue *isn't* the file's absence. That eliminates a whole class of potential problems.  My gut says `np.load` is the culprit. Either it choked on the format, or the data inside wasn't structured how I anticipated. That's what's triggering the `except` block and causing all the metrics to zero out.\n\nRight, time to dive deeper. I'll need to see the full stack trace to pinpoint exactly *where* and *why* `np.load` is failing.  I'll modify `auxiliary_metrics.py` to spit out all the gory details of the exception. That should give me the diagnostic clues I need to fix this quickly. \n", "signature": null}], "tool_calls": [{"id": "call_0bd8712946544dd8ae3779039a57__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"old_str\": \"        print(f\\\"Error in evaluate_aux: {e}\\\")\", \"new_str\": \"        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        import traceback\\n        traceback.print_exc()\", \"command\": \"str_replace\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n    84\t\n    85\t\n    86\t    except Exception as e:\n    87\t        print(f\"Error in evaluate_aux: {e}\")\n    88\t        import traceback\n    89\t        traceback.print_exc()\n    90\t        # Return default 0.0 for all metrics in case of failure\n    91\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    92\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    93\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    94\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_0bd8712946544dd8ae3779039a57__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, time to get down to brass tacks. I need to reproduce the error, and the best way to do that, in this situation, is a fresh smoke test. Let's see that traceback again. I need to pinpoint the exact line or sequence of operations that's causing this problem. It’s the only way to effectively diagnose and resolve this.\n", "signature": null}], "tool_calls": [{"id": "call_85022261e90b42d380002ce4ec52__thought__ClcBjz1rX4++5gq0qCJQvQrvUdLpvXeOcV+0eJzfBXBAxfBVj+CmobKNuGCu7Ci5A5Kmek0PcmjBC7LnktL1ZkGbxdyKMbjXkenkrdZWiXeevC1W8oTZ0hg=", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python3 -c 'import os; import sys; sys.path.insert(0, \\\".\\\"); from auxiliary_metrics import evaluate_aux; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; print(evaluate_aux(results_dir))'; \", \"summary\": \"Smoke test auxiliary_metrics.py for gen_4 with traceback enabled.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "python3 -c 'import os; import sys; sys.path.insert(0, \".\"); from auxiliary_metrics import evaluate_aux; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; print(evaluate_aux(results_dir))'\n{'aux_total_overlap_distance': 0.0, 'aux_max_overlap_distance': 0.0, 'aux_boundary_violation_distance': 0.0, 'aux_min_radius': 0.0, 'aux_num_violated_circles': 0}\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_85022261e90b42d380002ce4ec52__thought__ClcBjz1rX4++5gq0qCJQvQrvUdLpvXeOcV+0eJzfBXBAxfBVj+CmobKNuGCu7Ci5A5Kmek0PcmjBC7LnktL1ZkGbxdyKMbjXkenkrdZWiXeevC1W8oTZ0hg=", "name": "terminal"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "FbOfaazuNor0694P6Y_ImAo", "created": 1772073749, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"summary\": \"Inspect keys in extra.npz for gen_4.\", \"command\": \"python3 -c 'import numpy as np; import os; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; extra_file_path = os.path.join(results_dir, \\\"extra.npz\\\"); data = np.load(extra_file_path); print(list(data.keys())); data.close()'\", \"security_risk\": \"LOW\"}", "name": "terminal"}, "id": "call_92781713072d4b658e5b48d04564__thought__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", "type": "function"}], "images": [], "reasoning_content": "Okay, let's break this down. The frustrating part is, the traceback isn't showing up where it should.  It should be hitting `stderr`, but it's not being captured by the terminal's default output when I'm just using `print`. That tells me the exception is probably being dealt with upstream, *before* the `print_exc()` call, or the issue isn't even an exception to begin with.\n\nI know my code has a crucial check: `if not os.path.exists(extra_file_path): print(f\"Warning: extra.npz not found at {extra_file_path}\") return metrics`. But `ls -l` confirmed that file's there. So, that can't be it.\n\nThat leaves a few possibilities for it to silently return those zero-valued `metrics` without raising an exception and hitting the warning. Either the `np.load(extra_file_path)` call is somehow succeeding, but then something goes wrong accessing either `data[\"centers\"]` or `data[\"radii\"]` within it. Or, and this is another possibility, `radii.size` is somehow zero after loading the file. \n\nRight now, the best path forward is to dig directly into `extra.npz`. I could install `h5py` since `np.load` often relies on it for `.npz` files, but for a quick check, I'll just load the file and print out the keys to see what's really inside. This should give me a good handle on where the problem lies.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's break this down. The frustrating part is, the traceback isn't showing up where it should.  It should be hitting `stderr`, but it's not being captured by the terminal's default output when I'm just using `print`. That tells me the exception is probably being dealt with upstream, *before* the `print_exc()` call, or the issue isn't even an exception to begin with.\n\nI know my code has a crucial check: `if not os.path.exists(extra_file_path): print(f\"Warning: extra.npz not found at {extra_file_path}\") return metrics`. But `ls -l` confirmed that file's there. So, that can't be it.\n\nThat leaves a few possibilities for it to silently return those zero-valued `metrics` without raising an exception and hitting the warning. Either the `np.load(extra_file_path)` call is somehow succeeding, but then something goes wrong accessing either `data[\"centers\"]` or `data[\"radii\"]` within it. Or, and this is another possibility, `radii.size` is somehow zero after loading the file. \n\nRight now, the best path forward is to dig directly into `extra.npz`. I could install `h5py` since `np.load` often relies on it for `.npz` files, but for a quick check, I'll just load the file and print out the keys to see what's really inside. This should give me a good handle on where the problem lies.\n"}], "provider_specific_fields": {"thought_signatures": ["CrMHAY89a1/VeekiZYKT2pV/q3M6paXKGyQ5UJdeiUP3uGj7lST6F+d+E1hxVXneYRMy01KvDDgw4YO3k0Ar5JbWphaKJ+Qxz4/GmOO8kx+cVVQLQHLLzh7EPHEXIuCAWsYNtQivHtg8cxKNNFaBdGKfu49I8xjSraEaX4r4ZxR3mdaPf3jRituJ7e3p+aEuEscd4jGexikWCGFfebRwh9UFV1e0Ct6WtSMyUGsSRX23ZDdd11CoUlPOS2am5biF97lv8C51b2pWGnyze1Mg7lHhVX3c/PkAvnhbC3f1a8U3IBEeV8aqeEaR9zlcuq+DVd7doHhmQkP8zD6P5aj+R/JOPzsA0cetpBEww8dh/P5OuYxJLqkrYLmfpUoxERqPbe1fcheDvIwSm7SblR9xRhY9rTqfVIqgARiLezr6aBSTn1KDAG+DSe5tboi5L0rILzl39N2t9tAZSn4ouUx4JSxQ7QjIxsLVChRXBskNKRdnvVwTBoiwHbRRppKyQcyDSaT8jtDF/oorz4moD08e6IpWXfz/stUOjYfV22b0+9zSYWAl/vwkfsy9LzqHFbogSeAXk2EcpD5iivPSf+KBIWtdA9uePI7+3k2nV9XAcpBQIx2XZkvKHo/HypaXp7XiUyhmoao2Fg8hWc9FdqQpiLYEePZV6Z0Z+QM3BfjNUTJmB90MPIDIuHXZxfuMcjSudBVK1cq2Al+/5hdTSFW+9v/dQya3IrEG9UjDYYJHyzij9qVbMdw13uH1YeaC5OUk3h9FOzGCT4oZe/95GJ9AY7NhFPJJ56pDuwhYflDh/LBthi628cgGQ9PC1AGqX6HwKRug6cKFT7rDGuDAfuARt9JxJc/P+ySl38Q6Qcpj+N5vLvyAAY1ZF5XmeziaFmkRJEUsSw7UFBeSMpZ11ZS36BnSZlsEwjQkk+L8x8yib6hum9vCAf7P9mV8wJe1/SrsFUM9mVkni9zf+4/b5xGKWSr46twwy/k2pl5hkN7SAZ2JiItT1Y1kpK2BxHtyX7C1huHe+dUOMBXRRKjwuJn7+39sIcACu5imvtqK7uiKACh9/z80ZL5rdtMPH9VaVfwtXVWVVNp8cuIOFnm7uCY11Gx9DT7q2CeBMbSUIHShHOFADg8Bgfg77494sd4zT4fRTg7P/r8zN/whzpq5inp4UJXqh2ilxMF+hLKAaLRwi8Vn80IFU9WCOG6fzCCkz0pP3pQBOrQ9wcZ2wNiS0RSbdc6ZyIG4WQLCdwQHJNGgGDYOziI6mlc="]}}}], "usage": {"completion_tokens": 396, "prompt_tokens": 17478, "total_tokens": 17874, "completion_tokens_details": {"reasoning_tokens": 250, "text_tokens": 146}, "prompt_tokens_details": {"text_tokens": 17478}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0062334, "timestamp": 1772073755.603929, "latency_sec": 5.719409465789795, "usage_summary": {"prompt_tokens": 17478, "completion_tokens": 396, "reasoning_tokens": 250, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073760.204-93f8.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 4 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_4/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_4/main.py\n- Current metrics: $RESULTS_DIR/gen_4/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 1.6221\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 4 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530\n- Net change (gen_0 -> gen_3): +1.3932"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. The user, a very demanding client, wants me, a seasoned numerical analyst and optimization architect, to step in and fix a problem. My job is to craft some helpful metrics to guide this evolutionary loop. My immediate concern is that a large drop in the primary score from generation 3 to 4. That has to be the focus.\n\nFirst things first, I've got to gather the lay of the land. I need to get my hands on `EVAL_AGENTS.md` and see what the active metrics are and what the history looks like. Then, I’ll examine the primary score trend, which is the immediate issue. Okay, generation zero to one saw a big improvement, followed by a plateau and now a crash in generation four, with a primary score of 1.6221. That's a red flag! \n\nI'll then dive into `main.py` for generation 4, and look at the `results/metrics.json` file for that generation, to see if there's any pre-existing auxiliary metrics that can help me understand what's gone wrong. And finally, I'll need to understand the problem itself: circle packing, from `evaluate_ori.py`, to get a feel for the ground truth.\n\nNow the real work begins: Diagnosing the problem. This sudden drop is the key. Is there a bad mutation in the code? Is the solution unstable? Are there constraint violations? Circle packing is all about fitting circles in a space without overlaps, so I need to assess overlap, boundary violations, packing density and the size of the circles. \n\nBased on my understanding of the problem and the current issues, I know that I need to add some new auxiliary metrics. They will need to identify *constraint violations* and any *solution instability*. I'll focus on these. Depending on what I find, I might even need to *replace* some of the existing metrics if they're not pulling their weight or are already saturated.\n\nThen it's time to get down to the code: `auxiliary_metrics.py`. I'll make sure the imports are correct, then implement the `evaluate_aux` function. Defensive coding is key here; a default return of 0.0 on failure is non-negotiable.\n\nFinally, document everything by updating `EVAL_AGENTS.md` with the generation, stage, primary score, hypothesis, and the actions I took.\n\nOkay, let's get started. I'll need to look at `EVAL_AGENTS.md` and `auxiliary_metrics.py` now. \n", "signature": null}], "tool_calls": [{"id": "call_ad172c287e414c36a7999f9b4215__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"command\": \"view\", \"summary\": \"View EVAL_AGENTS.md to check active metrics and past generations.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# EV2 Agent Memory\n     2\t\n     3\t- Initialized by eval service.\n     4\t- Use this file as compact cross-generation memory.\n     5\t\n"}], "role": "tool", "tool_call_id": "call_ad172c287e414c36a7999f9b4215__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, so, *EVAL_AGENTS.md* is completely blank. Seriously, nothing. That means this is the virgin run, the starting point for all my auxiliary metric management. I need to get this initialized pronto; this is where I’ll be tracking and assessing everything.\n\nAlright, time to dive into *auxiliary_metrics.py*. Let's see what we're working with here. I need to understand the architecture before anything else.\n", "signature": null}], "tool_calls": [{"id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py to see current auxiliary metrics.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\tdef evaluate_aux(results_dir, primary_result=None):\n     2\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     3\t    return {}\n     4\t\n"}], "role": "tool", "tool_call_id": "call_d9b93833f7ad483d9a301bba9cf1__thought__CvABAY89a1+5MH5Q++0fZuOOHzw9f3GTRhay1G7PV5tUxNo0NOF84eA8BYKFoA1ceRWjIqBgrBs3EuV+fKMABXYNJdwKV2jIlkkaxkyUCmod8upRHhcbrVDIv029rri5OTxMwmsNZzbOT29RuQWFs3tznDCzfrYx32Qunks3e0soo7ftYYZt8cv77/R6VW9GIHO/pEVSJ0EKEmiaAADPw6j1zznaQKVepwe7hwGHa2qLUsrThiIbP6HPinz95mx4q3CtjwvEykVL5kgbYBj33sD0VAlVgb1N1thlD0JYXDH7wxLiaolXOqECQF2Xac2juGAG", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's where I'm at. I'm staring at a clean slate. My `auxiliary_metrics.py` is, predictably, a barren wasteland, currently returning nothing. No pre-existing baggage to deal with, no legacy code to untangle. This clean start is actually pretty refreshing. \n\nNow, time to get a handle on the core of this circle packing problem. I need to understand what constitutes a \"correct\" or \"optimal\" solution. My next stop is definitely `evaluate_ori.py`. That file should contain the ground truth definitions, the metrics that define success. That's where I'll find the gold standard, the target I'm aiming for. I'll need to dissect its logic, understand how it quantifies the packing density, the overlap, and everything else that makes for a good circle packing. Right, let's dive in and see how we're scoring these things.\n", "signature": null}], "tool_calls": [{"id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"summary\": \"View evaluate_ori.py to understand the primary evaluation metric.\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py:\n     1\t\"\"\"\n     2\tEvaluator for circle packing example (n=26) with improved timeout handling\n     3\t\"\"\"\n     4\t\n     5\timport os\n     6\timport argparse\n     7\timport sys\n     8\timport numpy as np\n     9\tfrom typing import Tuple, Optional, List, Dict, Any\n    10\tfrom pathlib import Path\n    11\t\n    12\t# Add project root to import path for direct file execution.\n    13\tsys.path.insert(0, str(Path(__file__).resolve().parents[2]))\n    14\t\n    15\tfrom shinka.core import run_shinka_eval\n    16\t\n    17\t\n    18\tdef format_centers_string(centers: np.ndarray) -> str:\n    19\t    \"\"\"Formats circle centers into a multi-line string for display.\"\"\"\n    20\t    return \"\\n\".join(\n    21\t        [\n    22\t            f\"  centers[{i}] = ({x_coord:.4f}, {y_coord:.4f})\"\n    23\t            for i, (x_coord, y_coord) in enumerate(centers)\n    24\t        ]\n    25\t    )\n    26\t\n    27\t\n    28\tdef adapted_validate_packing(\n    29\t    run_output: Tuple[np.ndarray, np.ndarray, float],\n    30\t    atol=1e-6,\n    31\t) -> Tuple[bool, Optional[str]]:\n    32\t    \"\"\"\n    33\t    Validates circle packing results based on the output of 'run_packing'.\n    34\t\n    35\t    Args:\n    36\t        run_output: Tuple (centers, radii, reported_sum) from run_packing.\n    37\t\n    38\t    Returns:\n    39\t        (is_valid: bool, error_message: Optional[str])\n    40\t    \"\"\"\n    41\t    centers, radii, reported_sum = run_output\n    42\t    msg = \"The circles are placed correctly. There are no overlaps or any circles outside the unit square.\"\n    43\t    if not isinstance(centers, np.ndarray):\n    44\t        centers = np.array(centers)\n    45\t    if not isinstance(radii, np.ndarray):\n    46\t        radii = np.array(radii)\n    47\t\n    48\t    n_expected = 26\n    49\t    if centers.shape != (n_expected, 2):\n    50\t        msg = (\n    51\t            f\"Centers shape incorrect. Expected ({n_expected}, 2), got {centers.shape}\"\n    52\t        )\n    53\t        return False, msg\n    54\t    if radii.shape != (n_expected,):\n    55\t        msg = f\"Radii shape incorrect. Expected ({n_expected},), got {radii.shape}\"\n    56\t        return False, msg\n    57\t\n    58\t    if np.any(radii < 0):\n    59\t        negative_indices = np.where(radii < 0)[0]\n    60\t        msg = f\"Negative radii found for circles at indices: {negative_indices}\"\n    61\t        return False, msg\n    62\t\n    63\t    if not np.isclose(np.sum(radii), reported_sum, atol=atol):\n    64\t        msg = (\n    65\t            f\"Sum of radii ({np.sum(radii):.6f}) does not match \"\n    66\t            f\"reported ({reported_sum:.6f})\"\n    67\t        )\n    68\t        return False, msg\n    69\t\n    70\t    for i in range(n_expected):\n    71\t        x, y = centers[i]\n    72\t        r = radii[i]\n    73\t        is_outside = (\n    74\t            x - r < -atol or x + r > 1 + atol or y - r < -atol or y + r > 1 + atol\n    75\t        )\n    76\t        if is_outside:\n    77\t            msg = (\n    78\t                f\"Circle {i} (x={x:.4f}, y={y:.4f}, r={r:.4f}) is outside unit square.\"\n    79\t            )\n    80\t            return False, msg\n    81\t\n    82\t    for i in range(n_expected):\n    83\t        for j in range(i + 1, n_expected):\n    84\t            dist = np.sqrt(np.sum((centers[i] - centers[j]) ** 2))\n    85\t            if dist < radii[i] + radii[j] - atol:\n    86\t                msg = (\n    87\t                    f\"Circles {i} & {j} overlap. Dist: {dist:.4f}, \"\n    88\t                    f\"Sum Radii: {(radii[i] + radii[j]):.4f}\"\n    89\t                )\n    90\t                return False, msg\n    91\t    return True, msg\n    92\t\n    93\t\n    94\tdef get_circle_packing_kwargs(run_index: int) -> Dict[str, Any]:\n    95\t    \"\"\"Provides keyword arguments for circle packing runs (none needed).\"\"\"\n    96\t    return {}\n    97\t\n    98\t\n    99\tdef aggregate_circle_packing_metrics(\n   100\t    results: List[Tuple[np.ndarray, np.ndarray, float]], results_dir: str\n   101\t) -> Dict[str, Any]:\n   102\t    \"\"\"\n   103\t    Aggregates metrics for circle packing. Assumes num_runs=1.\n   104\t    Saves extra.npz with detailed packing information.\n   105\t    \"\"\"\n   106\t    if not results:\n   107\t        return {\"combined_score\": 0.0, \"error\": \"No results to aggregate\"}\n   108\t\n   109\t    centers, radii, reported_sum = results[0]\n   110\t\n   111\t    public_metrics = {\n   112\t        \"centers_str\": format_centers_string(centers),\n   113\t        \"num_circles\": centers.shape[0],\n   114\t    }\n   115\t    private_metrics = {\n   116\t        \"reported_sum_of_radii\": float(reported_sum),\n   117\t    }\n   118\t    metrics = {\n   119\t        \"combined_score\": float(reported_sum),\n   120\t        \"public\": public_metrics,\n   121\t        \"private\": private_metrics,\n   122\t    }\n   123\t\n   124\t    extra_file = os.path.join(results_dir, \"extra.npz\")\n   125\t    try:\n   126\t        np.savez(\n   127\t            extra_file,\n   128\t            centers=centers,\n   129\t            radii=radii,\n   130\t            reported_sum=reported_sum,\n   131\t        )\n   132\t        print(f\"Detailed packing data saved to {extra_file}\")\n   133\t    except Exception as e:\n   134\t        print(f\"Error saving extra.npz: {e}\")\n   135\t        metrics[\"extra_npz_save_error\"] = str(e)\n   136\t\n   137\t    return metrics\n   138\t\n   139\t\n   140\tdef main(program_path: str, results_dir: str):\n   141\t    \"\"\"Runs the circle packing evaluation using shinka.eval.\"\"\"\n   142\t    print(f\"Evaluating program: {program_path}\")\n   143\t    print(f\"Saving results to: {results_dir}\")\n   144\t    os.makedirs(results_dir, exist_ok=True)\n   145\t\n   146\t    num_experiment_runs = 1\n   147\t\n   148\t    # Define a nested function to pass results_dir to the aggregator\n   149\t    def _aggregator_with_context(\n   150\t        r: List[Tuple[np.ndarray, np.ndarray, float]],\n   151\t    ) -> Dict[str, Any]:\n   152\t        return aggregate_circle_packing_metrics(r, results_dir)\n   153\t\n   154\t    metrics, correct, error_msg = run_shinka_eval(\n   155\t        program_path=program_path,\n   156\t        results_dir=results_dir,\n   157\t        experiment_fn_name=\"run_packing\",\n   158\t        num_runs=num_experiment_runs,\n   159\t        get_experiment_kwargs=get_circle_packing_kwargs,\n   160\t        validate_fn=adapted_validate_packing,\n   161\t        aggregate_metrics_fn=_aggregator_with_context,\n   162\t    )\n   163\t\n   164\t    if correct:\n   165\t        print(\"Evaluation and Validation completed successfully.\")\n   166\t    else:\n   167\t        print(f\"Evaluation or Validation failed: {error_msg}\")\n   168\t\n   169\t    print(\"Metrics:\")\n   170\t    for key, value in metrics.items():\n   171\t        if isinstance(value, str) and len(value) > 100:\n   172\t            print(f\"  {key}: <string_too_long_to_display>\")\n   173\t        else:\n   174\t            print(f\"  {key}: {value}\")\n   175\t\n   176\t\n   177\tif __name__ == \"__main__\":\n   178\t    parser = argparse.ArgumentParser(\n   179\t        description=\"Circle packing evaluator using shinka.eval\"\n   180\t    )\n   181\t    parser.add_argument(\n   182\t        \"--program_path\",\n   183\t        type=str,\n   184\t        default=\"tasks/circle_packing/initial.py\",\n   185\t        help=\"Path to program to evaluate (must contain 'run_packing')\",\n   186\t    )\n   187\t    parser.add_argument(\n   188\t        \"--results_dir\",\n   189\t        type=str,\n   190\t        default=\"tasks/circle_packing/results/debug_eval\",\n   191\t        help=\"Dir to save results (metrics.json, correct.json, extra.npz)\",\n   192\t    )\n   193\t    parsed_args = parser.parse_args()\n   194\t    main(parsed_args.program_path, parsed_args.results_dir)\n   195\t\n"}], "role": "tool", "tool_call_id": "call_fecd6aa19ba64f4d81c53dd2be8b__thought__CoQCAY89a1+jEv3BqiRbJIaZLRvIM0UClNb5VPexxmtT/Vn1COsr5eY6WX0cDmkPJDEyZQWpwxGlgDPZnerckYra6OOXKHJAhxRA8Vsjty3ijAZb8w3y4Dr8pAzVZK5dn+NWGuqac1RXpFtYfLC5bxmyVe7Ky95A8Ixh8FwubxKNayfdthJFLXxXrLsCNK+O2Le0wSyfrr4NDHDWknb4ketwSIC9QLMKtnC6wnt5Y72bJyLO03k8TO8zgaV/cglEUMndBa0g3xaYyfCyIY0/f0IIjCfk1UIJtb5Up0TEJp5aH2yXRm9eDW3JdhxfxJLg+lTgEo5Lay+Gz9zNLVM29Uou9/wZEbw=", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking. I'm looking at `evaluate_ori.py` and immediately see the core problem: packing 26 circles within a unit square. The goal, as judged by the `combined_score`, is to maximize the sum of the radii.\n\nThe key constraints are no negative radii, all circles inside the square, and no overlaps. The `adapted_validate_packing` function is the gatekeeper here, ensuring these constraints are met. A problem in Gen 4 has become apparent, with a dramatic score drop from Gen 3. The `combined_score` has significantly decreased, indicating something is seriously wrong with the circle packing in Gen 4.\n\nI need to understand why. My initial hypothesis is that either the constraints are being violated (overlaps or boundary violations) or the radii of the circles have shrunk dramatically, causing a lower total. I'll need to dig into this by analyzing the `primary_result` and `main.py` files for Gen 4. It will be helpful to find out where the `extra.npz` files are being saved, since that's where the critical `centers` and `radii` data are stored. The `results_dir` is the place to start.\n\nTo help diagnose this, I need to create some auxiliary metrics. I need to define them in `EVAL_AGENTS.md` since it's currently empty, and then write the code in `auxiliary_metrics.py`.\n\nThe key will be to confirm constraint violations:\n\n*   I'll calculate `aux_total_overlap_distance` and `aux_max_overlap_distance` to quantify the overlap problem.\n*   `aux_boundary_violation_distance` to measure how much circles are going outside the square.\n*   `aux_min_radius` to see if the radii are becoming suspiciously small.\n*   `aux_num_violated_circles`, a simple count of constraint violations.\n\nEssentially, I'm now in a diagnosis phase – either `EXPLORATION` or `OPTIMIZATION` -- where I am trying to figure out why the packing quality has regressed so significantly. The next step is to update `auxiliary_metrics.py` based on this analysis.\n", "signature": null}], "tool_calls": [{"id": "call_93797a325ec240039ed95cf3c33e__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"file_text\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"create\"}"}}]}, {"content": [{"type": "text", "text": "[An error occurred during execution.]\n"}, {"type": "text", "text": "Invalid `path` parameter: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. File already exists at: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md. Cannot overwrite files using command `create`."}], "role": "tool", "tool_call_id": "call_93797a325ec240039ed95cf3c33e__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Right, a slip of the mind, completely my fault. I got ahead of myself and forgot that the system's already laid the groundwork with `EVAL_AGENTS.md`, even if it's currently a blank slate. Silly me, I should have been using `str_replace` or something similar for the update, not a fresh `create`. \n\nOkay, no problem. I'll utilize `str_replace` now. That's the correct way to inject the relevant registry and generation log into that pre-existing file. Time to make sure the initial content is correctly appended.\n", "signature": null}], "tool_calls": [{"id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"str_replace\", \"old_str\": \"# EV2 Agent Memory\\n\\n- Initialized by eval service.\\n- Use this file as compact cross-generation memory.\\n\", \"new_str\": \"# Active Metric Registry\\n- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\\n- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\\n- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\\n- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\\n- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\\n\\n## Generation 4\\nStage: OPTIMIZATION\\nPrimary score: 1.6221\\nHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\\nStagnant metrics: none\\nActions:\\n- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\\n- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\\n- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\\n- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\\n- ADD `aux_num_violated_circles` (To count how many circles are problematic)\\nNotes:\\n- Initializing auxiliary metrics for the first time.\\n- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\\n\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_c19c6008b3e24138bbe5066e1153__thought__CukCAY89a19ZhuA8SKGMtzsLo0NedBPMBCecuaUzoXKdZFD+A5zOH/zniGzLHa52lX0UlJugcrcJIVwVfNIpXEPtHOkWwm2nHyF5X78K4ZSZ3EYs4Ia1Ii1C9wStaH1SNbxSkg92aBLLYhJVh9naUOFOm0Gtbldz9aq/hXP2MgkYTufvU+5uBM3UiFd9xzFXSwQuMFt4cHk+Lyks6J2VwdQskcFEVk1CmrGUhCkNNyN46IUzB/bD071HjceHQCIgdHaxJpPfUh+528vFP1fBv7GABk8n2WxmJR894+tm12cxXlHyc0jOk+1Ir+bK/7hTwTxs8naEo8C1eQmvF7pp8TaWMIMpVttbmpZPUsn1bfhLFUicngeNCJa3iAl+6Xf3WzPnWBTNTsEBHjWdQFecfViwJdYzD/MqPfL7AmARuhmd2quTH9bK67/uAUZDfT0Ytbxpqg1nB2NcrI4m3LQwggkLVAVLCA6glEkbuw==", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, now that I've meticulously updated `EVAL_AGENTS.md`, ensuring all the agent evaluation criteria are up to snuff, it's time to dive into the code. My next task is to implement the `evaluate_aux` function within `auxiliary_metrics.py`. This is where the real analytical work begins. I'll need to crack open `extra.npz` to extract the crucial `centers` and `radii` data. These parameters are fundamental; they'll serve as the foundation for the upcoming analysis. It’s all about the details; they underpin the whole operation.\n", "signature": null}], "tool_calls": [{"id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"command\": \"str_replace\", \"old_str\": \"def evaluate_aux(results_dir, primary_result=None):\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    return {}\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"new_str\": \"import numpy as np\\nimport os\\nfrom typing import Dict, Any\\n\\ndef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\\n    \\\"\\\"\\\"Return auxiliary metrics as a dict.\\\"\\\"\\\"\\n    metrics = {}\\n    try:\\n        extra_file_path = os.path.join(results_dir, \\\"extra.npz\\\")\\n        if not os.path.exists(extra_file_path):\\n            print(f\\\"Warning: extra.npz not found at {extra_file_path}\\\")\\n            return metrics\\n\\n        data = np.load(extra_file_path)\\n        centers = data[\\\"centers\\\"]\\n        radii = data[\\\"radii\\\"]\\n\\n        num_circles = centers.shape[0]\\n        \\n        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\\n        total_overlap_distance = 0.0\\n        max_overlap_distance = 0.0\\n        num_overlapping_pairs = 0\\n\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    overlap = min_dist_for_no_overlap - dist\\n                    total_overlap_distance += overlap\\n                    if overlap > max_overlap_distance:\\n                        max_overlap_distance = overlap\\n                    num_overlapping_pairs += 1\\n\\n        metrics[\\\"aux_total_overlap_distance\\\"] = float(total_overlap_distance)\\n        metrics[\\\"aux_max_overlap_distance\\\"] = float(max_overlap_distance)\\n        \\n        # --- aux_boundary_violation_distance ---\\n        boundary_violation_distance = 0.0\\n        \\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n\\n            # Check x-bounds\\n            if x - r < 0:\\n                boundary_violation_distance += abs(x - r)\\n            if x + r > 1:\\n                boundary_violation_distance += abs(x + r - 1)\\n            \\n            # Check y-bounds\\n            if y - r < 0:\\n                boundary_violation_distance += abs(y - r)\\n            if y + r > 1:\\n                boundary_violation_distance += abs(y + r - 1)\\n                \\n        metrics[\\\"aux_boundary_violation_distance\\\"] = float(boundary_violation_distance)\\n\\n        # --- aux_min_radius ---\\n        metrics[\\\"aux_min_radius\\\"] = float(np.min(radii)) if radii.size > 0 else 0.0\\n        \\n        # --- aux_num_violated_circles ---\\n        num_violated_circles = 0\\n        violated_circles_indices = set()\\n\\n        # Check for overlaps (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            for j in range(i + 1, num_circles):\\n                dist = np.linalg.norm(centers[i] - centers[j])\\n                min_dist_for_no_overlap = radii[i] + radii[j]\\n                if dist < min_dist_for_no_overlap:\\n                    violated_circles_indices.add(i)\\n                    violated_circles_indices.add(j)\\n\\n        # Check for boundary violations (re-do from above to get specific circles)\\n        for i in range(num_circles):\\n            x, y = centers[i]\\n            r = radii[i]\\n            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\\n                violated_circles_indices.add(i)\\n                \\n        metrics[\\\"aux_num_violated_circles\\\"] = len(violated_circles_indices)\\n\\n\\n    except Exception as e:\\n        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        # Return default 0.0 for all metrics in case of failure\\n        metrics[\\\"aux_total_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_max_overlap_distance\\\"] = 0.0\\n        metrics[\\\"aux_boundary_violation_distance\\\"] = 0.0\\n        metrics[\\\"aux_min_radius\\\"] = 0.0\\n        metrics[\\\"aux_num_violated_circles\\\"] = 0\\n        \\n    return metrics\\n\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"]\n    17\t\n    18\t        num_circles = centers.shape[0]\n    19\t        \n    20\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    21\t        total_overlap_distance = 0.0\n    22\t        max_overlap_distance = 0.0\n    23\t        num_overlapping_pairs = 0\n    24\t\n    25\t        for i in range(num_circles):\n    26\t            for j in range(i + 1, num_circles):\n    27\t                dist = np.linalg.norm(centers[i] - centers[j])\n    28\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    29\t                if dist < min_dist_for_no_overlap:\n    30\t                    overlap = min_dist_for_no_overlap - dist\n    31\t                    total_overlap_distance += overlap\n    32\t                    if overlap > max_overlap_distance:\n    33\t                        max_overlap_distance = overlap\n    34\t                    num_overlapping_pairs += 1\n    35\t\n    36\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    37\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    38\t        \n    39\t        # --- aux_boundary_violation_distance ---\n    40\t        boundary_violation_distance = 0.0\n    41\t        \n    42\t        for i in range(num_circles):\n    43\t            x, y = centers[i]\n    44\t            r = radii[i]\n    45\t\n    46\t            # Check x-bounds\n    47\t            if x - r < 0:\n    48\t                boundary_violation_distance += abs(x - r)\n    49\t            if x + r > 1:\n    50\t                boundary_violation_distance += abs(x + r - 1)\n    51\t            \n    52\t            # Check y-bounds\n    53\t            if y - r < 0:\n    54\t                boundary_violation_distance += abs(y - r)\n    55\t            if y + r > 1:\n    56\t                boundary_violation_distance += abs(y + r - 1)\n    57\t                \n    58\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    59\t\n    60\t        # --- aux_min_radius ---\n    61\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    62\t        \n    63\t        # --- aux_num_violated_circles ---\n    64\t        num_violated_circles = 0\n    65\t        violated_circles_indices = set()\n    66\t\n    67\t        # Check for overlaps (re-do from above to get specific circles)\n    68\t        for i in range(num_circles):\n    69\t            for j in range(i + 1, num_circles):\n    70\t                dist = np.linalg.norm(centers[i] - centers[j])\n    71\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    72\t                if dist < min_dist_for_no_overlap:\n    73\t                    violated_circles_indices.add(i)\n    74\t                    violated_circles_indices.add(j)\n    75\t\n    76\t        # Check for boundary violations (re-do from above to get specific circles)\n    77\t        for i in range(num_circles):\n    78\t            x, y = centers[i]\n    79\t            r = radii[i]\n    80\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    81\t                violated_circles_indices.add(i)\n    82\t                \n    83\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    84\t\n    85\t\n    86\t    except Exception as e:\n    87\t        print(f\"Error in evaluate_aux: {e}\")\n    88\t        # Return default 0.0 for all metrics in case of failure\n    89\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    90\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    91\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    92\t        metrics[\"aux_min_radius\"] = 0.0\n    93\t        metrics[\"aux_num_violated_circles\"] = 0\n    94\t        \n    95\t    return metrics\n    96\t\n    97\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_ae190cbcd5d24c678e443ceffa06__thought__CtMBAY89a19r8pKoFsTVggsuOw5Do/NQEDZIpmOp35G7YdCeoY7M7LFMZCGKhP013+hJExODK1+awRvZkF1K/kckZrT8swHGwWtSGo5+7gph9lH0fRhHIJl3z39BJfc2I9P93ojfoqIRHmcZIk5Wt0oJVDToE7BlprT1SPfrX0VaTvFps8Xsnennggdie0wmqhDsrupx+tcyVZa7XVluHQ775ZwpEQEBSeiSrWs7KmlHwwBFP7E0ogj/sHBtrr7IXqaUevWlIFqljllraHV5rA9bhf5oqQ==", "name": "file_editor"}, {"content": [], "role": "assistant"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking:\n\nAlright, that `auxiliary_metrics.py` script is finally updated with the new metrics. Now, the real work begins. I need to verify that it's robust and actually *works* with the current data. I can't just assume it'll run flawlessly, especially after adding new calculations.\n\nSo, I'll leverage that `call_tool` function to trigger `evaluate_aux`. That's the most efficient way to get it running. First things first though; I need to get the `results_dir` for `gen_4`. That's where the data for this generation of the project lives. Then, I'll be able to run `evaluate_aux` and confirm that everything is behaving as expected. No errors, and, more importantly, the output needs to be valid. The integrity of the results is paramount.\n", "signature": null}], "tool_calls": [{"id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"python3 -c 'from eval_agent.tool_box.api import call_tool; import os; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))'\", \"summary\": \"Smoke test auxiliary_metrics.py for gen_4.\"}"}}]}, {"content": [{"type": "text", "text": "Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    from eval_agent.tool_box.api import call_tool; import os; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; from auxiliary_metrics import evaluate_aux; print(evaluate_aux(results_dir))\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'eval_agent'\n[The command completed with exit code 1.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 1]"}], "role": "tool", "tool_call_id": "call_bc9da17f4bc146c6b1a97d51fa7d__thought__CrgCAY89a19NSmKjBdgjsC1qsrNkn3jbtgNh9PN0j9HUksB2KUQzbgtWcyU1NkyXHYSoqw2yqQ1LzIxDqZW7kay5kNKXUY+cbI4iF38DJ530sihflvIa1SCwdreFyYP/s/vNlsXGBmR4Y2W8jJd/uujOwI6qZuqSkt+OUhFjqsAzIeB1deKxkgKYU8i5PdAmwLrwjLnoWpguQrgRerqYQDQ4883aMvdDNKU1cYa71QPLsAPtuo7DUlI1yaiNt4syaw84HDeCI28MDwNhVn7B6ZSjfw+I+Hh6r1M4iWDRA18vPWtJgqXQw+nt+Yu+0++u9sHxCz2CNiu87rMKu+7TJRdYTveypdsbrUJxWDouRNClN86mbiFFtdXzQ8xnqWAh7vU5RW4cq/8sran+Q66Gel/9xiHK8e/SmPuv", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as I analyze this failure.\n\nUgh, the smoke test just bombed. I got a `ModuleNotFoundError: No module named 'eval_agent'`. Right, that makes sense. I was trying to import `eval_agent.tool_box.api` in that direct `python3 -c` call. The problem is, the Python interpreter spawned by that command probably doesn't have the `eval_agent` package in its search path. That's a classic gotcha.\n\nOn top of that, I think I messed up the command itself. I'm calling `evaluate_aux`, which is actually defined in `auxiliary_metrics.py`.  That file is *right here* in the current directory, inside `eval_agent_memory`, so I should be able to import it directly. I also realize now that the `call_tool` function is totally unnecessary in that `python3 -c` snippet for `evaluate_aux`.\n\nAlright, let's fix these issues. I'll need to adjust the import path to properly reference `auxiliary_metrics.py`. I have to be absolutely sure that the import works correctly. I'll focus on ensuring that this local import works. It's crucial.\n", "signature": null}], "tool_calls": [{"id": "call_c6dad52b16544ff6ad6d15fac601__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"summary\": \"Smoke test auxiliary_metrics.py for gen_4 by adding current directory to sys.path.\", \"command\": \"python3 -c 'import os; import sys; sys.path.insert(0, \\\".\\\"); from auxiliary_metrics import evaluate_aux; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; print(evaluate_aux(results_dir))'; \", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "python3 -c 'import os; import sys; sys.path.insert(0, \".\"); from auxiliary_metrics import evaluate_aux; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; print(evaluate_aux(results_dir))'\n{'aux_total_overlap_distance': 0.0, 'aux_max_overlap_distance': 0.0, 'aux_boundary_violation_distance': 0.0, 'aux_min_radius': 0.0, 'aux_num_violated_circles': 0}\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_c6dad52b16544ff6ad6d15fac601__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, the smoke test just finished, and though it ran without errors, all the evaluation metrics are reporting zero. That immediately flags a problem with the data. My gut tells me something's up with the `extra.npz` file, specifically within the `gen_4/results` directory of the `gen_100` run. It seems like either the file isn't present, or more likely, it's empty or got corrupted during the process. The results are being pulled from the `results_circle_packing_v2_1_gen100_periodic5_20260226_023318` run, so I need to check the file system. Right, let me go check if that `extra.npz` file actually exists, and if it does, what its contents look like within the `results` directory. Need to dive in there and see what's what.\n", "signature": null}], "tool_calls": [{"id": "call_319dcfe7a9ef42db87cdefdb77fe__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"summary\": \"Check contents of gen_4 results directory.\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results/\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "total 16\n-rw-r--r-- 1 tengxiao tengxiao  112 Feb 26 02:41 auxiliary_metrics_snapshot.py\n-rw-r--r-- 1 tengxiao tengxiao   42 Feb 26 02:41 correct.json\n-rw-r--r-- 1 tengxiao tengxiao 1398 Feb 26 02:41 extra.npz\n-rw-r--r-- 1 tengxiao tengxiao 1666 Feb 26 02:41 metrics.json\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_319dcfe7a9ef42db87cdefdb77fe__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what I'm thinking:\n\nAlright, that `extra.npz` file, I know for a fact it's sitting right there in `gen_4/results`. So the issue *isn't* the file's absence. That eliminates a whole class of potential problems.  My gut says `np.load` is the culprit. Either it choked on the format, or the data inside wasn't structured how I anticipated. That's what's triggering the `except` block and causing all the metrics to zero out.\n\nRight, time to dive deeper. I'll need to see the full stack trace to pinpoint exactly *where* and *why* `np.load` is failing.  I'll modify `auxiliary_metrics.py` to spit out all the gory details of the exception. That should give me the diagnostic clues I need to fix this quickly. \n", "signature": null}], "tool_calls": [{"id": "call_0bd8712946544dd8ae3779039a57__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"old_str\": \"        print(f\\\"Error in evaluate_aux: {e}\\\")\", \"new_str\": \"        print(f\\\"Error in evaluate_aux: {e}\\\")\\n        import traceback\\n        traceback.print_exc()\", \"command\": \"str_replace\"}"}}]}, {"content": [{"type": "text", "text": "The file /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py has been edited. Here's the result of running `cat -n` on a snippet of /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n    84\t\n    85\t\n    86\t    except Exception as e:\n    87\t        print(f\"Error in evaluate_aux: {e}\")\n    88\t        import traceback\n    89\t        traceback.print_exc()\n    90\t        # Return default 0.0 for all metrics in case of failure\n    91\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    92\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    93\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    94\t\nReview the changes and make sure they are as expected. Edit the file again if necessary."}], "role": "tool", "tool_call_id": "call_0bd8712946544dd8ae3779039a57__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, time to get down to brass tacks. I need to reproduce the error, and the best way to do that, in this situation, is a fresh smoke test. Let's see that traceback again. I need to pinpoint the exact line or sequence of operations that's causing this problem. It’s the only way to effectively diagnose and resolve this.\n", "signature": null}], "tool_calls": [{"id": "call_85022261e90b42d380002ce4ec52__thought__ClcBjz1rX4++5gq0qCJQvQrvUdLpvXeOcV+0eJzfBXBAxfBVj+CmobKNuGCu7Ci5A5Kmek0PcmjBC7LnktL1ZkGbxdyKMbjXkenkrdZWiXeevC1W8oTZ0hg=", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python3 -c 'import os; import sys; sys.path.insert(0, \\\".\\\"); from auxiliary_metrics import evaluate_aux; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; print(evaluate_aux(results_dir))'; \", \"summary\": \"Smoke test auxiliary_metrics.py for gen_4 with traceback enabled.\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "python3 -c 'import os; import sys; sys.path.insert(0, \".\"); from auxiliary_metrics import evaluate_aux; results_dir = \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\"; print(evaluate_aux(results_dir))'\n{'aux_total_overlap_distance': 0.0, 'aux_max_overlap_distance': 0.0, 'aux_boundary_violation_distance': 0.0, 'aux_min_radius': 0.0, 'aux_num_violated_circles': 0}\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_85022261e90b42d380002ce4ec52__thought__ClcBjz1rX4++5gq0qCJQvQrvUdLpvXeOcV+0eJzfBXBAxfBVj+CmobKNuGCu7Ci5A5Kmek0PcmjBC7LnktL1ZkGbxdyKMbjXkenkrdZWiXeevC1W8oTZ0hg=", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's break this down. The frustrating part is, the traceback isn't showing up where it should.  It should be hitting `stderr`, but it's not being captured by the terminal's default output when I'm just using `print`. That tells me the exception is probably being dealt with upstream, *before* the `print_exc()` call, or the issue isn't even an exception to begin with.\n\nI know my code has a crucial check: `if not os.path.exists(extra_file_path): print(f\"Warning: extra.npz not found at {extra_file_path}\") return metrics`. But `ls -l` confirmed that file's there. So, that can't be it.\n\nThat leaves a few possibilities for it to silently return those zero-valued `metrics` without raising an exception and hitting the warning. Either the `np.load(extra_file_path)` call is somehow succeeding, but then something goes wrong accessing either `data[\"centers\"]` or `data[\"radii\"]` within it. Or, and this is another possibility, `radii.size` is somehow zero after loading the file. \n\nRight now, the best path forward is to dig directly into `extra.npz`. I could install `h5py` since `np.load` often relies on it for `.npz` files, but for a quick check, I'll just load the file and print out the keys to see what's really inside. This should give me a good handle on where the problem lies.\n", "signature": null}], "tool_calls": [{"id": "call_92781713072d4b658e5b48d04564__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"summary\": \"Inspect keys in extra.npz for gen_4.\", \"command\": \"python3 -c 'import numpy as np; import os; results_dir = \\\"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_4/results\\\"; extra_file_path = os.path.join(results_dir, \\\"extra.npz\\\"); data = np.load(extra_file_path); print(list(data.keys())); data.close()'\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "['centers', 'radii', 'reported_sum']\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_92781713072d4b658e5b48d04564__thought__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", "name": "terminal"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "HLOfaYOfEvHE694P3OSx-A0", "created": 1772073756, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"security_risk\": \"LOW\", \"command\": \"insert\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"new_str\": \"        print(f\\\"Loaded centers: {centers.shape}, radii: {radii.shape}\\\")\\n        print(f\\\"Number of circles: {num_circles}\\\")\", \"insert_line\": 16}", "name": "file_editor"}, "id": "call_e588efee7fed4d81985cf51ec40a__thought__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", "type": "function"}], "images": [], "reasoning_content": "Alright, let's break this down. First, I'm expecting `extra.npz` to house the key ingredients: the `centers`, `radii`, and `reported_sum` data. Thus, accessing `data[\"centers\"]` and `data[\"radii\"]` should be straightforward. No problem there.\n\nNow, the puzzling part: `evaluate_aux` is consistently spitting out all zeros. That's a red flag. The `try` block is supposed to catch any errors, so it’s likely *not* failing outright. Instead, it seems like the calculations are yielding a zero result, for some reason.\n\nTo nail down the culprit, I need visibility. I'm going to add some print statements inside the `evaluate_aux` function. I'll print out the values of `centers`, `radii`, and, critically, `num_circles`, just to verify that the data is being loaded and interpreted as I expect. This is the surest way to debug this and understand what's going on under the hood. Hopefully, this should pinpoint where the calculation is failing.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, let's break this down. First, I'm expecting `extra.npz` to house the key ingredients: the `centers`, `radii`, and `reported_sum` data. Thus, accessing `data[\"centers\"]` and `data[\"radii\"]` should be straightforward. No problem there.\n\nNow, the puzzling part: `evaluate_aux` is consistently spitting out all zeros. That's a red flag. The `try` block is supposed to catch any errors, so it’s likely *not* failing outright. Instead, it seems like the calculations are yielding a zero result, for some reason.\n\nTo nail down the culprit, I need visibility. I'm going to add some print statements inside the `evaluate_aux` function. I'll print out the values of `centers`, `radii`, and, critically, `num_circles`, just to verify that the data is being loaded and interpreted as I expect. This is the surest way to debug this and understand what's going on under the hood. Hopefully, this should pinpoint where the calculation is failing.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 242, "prompt_tokens": 18000, "total_tokens": 18242, "completion_tokens_details": {"reasoning_tokens": 119, "text_tokens": 123}, "prompt_tokens_details": {"text_tokens": 18000}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.006005, "timestamp": 1772073760.204527, "latency_sec": 3.9250919818878174, "usage_summary": {"prompt_tokens": 18000, "completion_tokens": 242, "reasoning_tokens": 119, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073801.221-3032.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 5 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_5/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_5/main.py\n- Current metrics: $RESULTS_DIR/gen_5/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.4535\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 5 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221\n- Net change (gen_0 -> gen_4): +0.6623"}], "role": "user"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "RrOfaeG2H4r0694P6Y_ImAo", "created": 1772073798, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "CusBAY89a1/EuHpoyhYChlUfBT5iC3yoEOkSM9/OlSAVo6ecbhHsPMZcvhb4lvIVNILkprgyFWwuDiA5W/pE83ywd35VSVY6sYhHTz4CXSHVLneHVA1UpRSCmA160nCW0e36V0ZGAY9DO9LXV4res6WAjrhhB/iLcykxMz+WqdxN/M3cXzZx/5Vl2atUCL/vzJr81UJviDobCQ5QtqlHtAe0UcRgYcTGcLQbim1TfJ6aZVoak+SwJFB7kkoym/mG44iZon8IMFn+SIH/yttJ+8yLOxvWtBcxExgvIVobBcpd7cPcMVEMWHiJEk6dlA=="}, "function": {"arguments": "{\"security_risk\": \"LOW\", \"command\": \"view\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\"}", "name": "file_editor"}, "id": "call_f435d9337ae94bab879c5ee72396__thought__CusBAY89a1/EuHpoyhYChlUfBT5iC3yoEOkSM9/OlSAVo6ecbhHsPMZcvhb4lvIVNILkprgyFWwuDiA5W/pE83ywd35VSVY6sYhHTz4CXSHVLneHVA1UpRSCmA160nCW0e36V0ZGAY9DO9LXV4res6WAjrhhB/iLcykxMz+WqdxN/M3cXzZx/5Vl2atUCL/vzJr81UJviDobCQ5QtqlHtAe0UcRgYcTGcLQbim1TfJ6aZVoak+SwJFB7kkoym/mG44iZon8IMFn+SIH/yttJ+8yLOxvWtBcxExgvIVobBcpd7cPcMVEMWHiJEk6dlA==", "type": "function"}], "images": [], "reasoning_content": "Okay, here's my thought process on this:\n\nDamn, the `old_str` didn't quite line up. That's a classic gotcha. Probably just some minor whitespace variations or maybe I tweaked something else unintentionally. I need to be more diligent about that moving forward. Gotta be precise.\n\nAlright, before I try that replacement again, I'm going to take another close look at the file content. Need to confirm exactly what's there now. Then, and only then, will I make the replacement, ensuring it's spot on this time. Precision is key.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's my thought process on this:\n\nDamn, the `old_str` didn't quite line up. That's a classic gotcha. Probably just some minor whitespace variations or maybe I tweaked something else unintentionally. I need to be more diligent about that moving forward. Gotta be precise.\n\nAlright, before I try that replacement again, I'm going to take another close look at the file content. Need to confirm exactly what's there now. Then, and only then, will I make the replacement, ensuring it's spot on this time. Precision is key.\n"}], "provider_specific_fields": {"thought_signatures": ["CusBAY89a1/EuHpoyhYChlUfBT5iC3yoEOkSM9/OlSAVo6ecbhHsPMZcvhb4lvIVNILkprgyFWwuDiA5W/pE83ywd35VSVY6sYhHTz4CXSHVLneHVA1UpRSCmA160nCW0e36V0ZGAY9DO9LXV4res6WAjrhhB/iLcykxMz+WqdxN/M3cXzZx/5Vl2atUCL/vzJr81UJviDobCQ5QtqlHtAe0UcRgYcTGcLQbim1TfJ6aZVoak+SwJFB7kkoym/mG44iZon8IMFn+SIH/yttJ+8yLOxvWtBcxExgvIVobBcpd7cPcMVEMWHiJEk6dlA=="]}}}], "usage": {"completion_tokens": 125, "prompt_tokens": 23515, "total_tokens": 23640, "completion_tokens_details": {"reasoning_tokens": 45, "text_tokens": 80}, "prompt_tokens_details": {"text_tokens": 23515}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.007367, "timestamp": 1772073801.2215178, "latency_sec": 2.0660178661346436, "usage_summary": {"prompt_tokens": 23515, "completion_tokens": 125, "reasoning_tokens": 45, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073813.586-07bc.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 5 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_5/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_5/main.py\n- Current metrics: $RESULTS_DIR/gen_5/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.4535\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 5 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221\n- Net change (gen_0 -> gen_4): +0.6623"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. I'm going to dive deep into this Circle Packing problem, acting as the numerical analyst and optimization architect I'm supposed to be. My primary goal? To craft a suite of insightful auxiliary evaluation metrics. These aren't just for show; they're the navigational tools that will guide the code evolution system towards that elusive global optimum.\n\nFirst things first: understand the landscape. I'll need to dissect what `evaluate_ori.py` is doing. That's the core, the source of truth for the primary metric. I'll also glance at `gen_5/main.py` to get a feel for how the solutions are structured, the very building blocks we're working with.\n\nThen, I'll scrutinize the progress. That score trend, that's the roadmap. Fluctuations? That's what I live for. Gen-2 peaked, then dipped. Gen-5 is better than Gen-4, but still below the peak. Hmmm… sounds like we're either oscillating, or maybe stuck in a local optimum. Time to diagnose. Overlapping circles? Underutilized space? Boundary violations? Number of circles? Common culprits in this arena.\n\nWith that understanding, I'll design my auxiliary metrics. Given the current state, and the task, it looks like we're in the **OPTIMIZATION** stage, trying to fine-tune and escape local traps. Or maybe **EXPLORATION** if the system needs to find new configurations. I'll craft metrics that capture constraint tension – boundary gradients and circle overlap. I'll also consider metrics that offer insights into the structural sensitivity and component contribution. The idea is to find what helps to escape the local minima.\n\nBut before I can implement a thing, I'll need to check the `EVAL_AGENTS.md` and the existing metrics. Are any of the metrics currently active? Are any of those active metrics just stagnant?\n\nNext up: implementation. I'll build `auxiliary_metrics.py`. I'll need to parse the circle data from `gen_5/results/metrics.json` – positions, radii, the whole shebang. The holy grail is `evaluate_ori.py`, and how it processes the data. I'll read it thoroughly.\n\nFinally, the validation. A simple run to ensure my script runs without errors, and produces output in the right format. After the initial smoke test, I'll update `EVAL_AGENTS.md` to document my journey.\nI need to understand what's in `eval_agent_memory/EVAL_AGENTS.md` and `auxiliary_metrics.py`. Let's get to work!\n", "signature": null}], "tool_calls": [{"id": 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TErd1AOzClf2/zlTkzNrbKMHg", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"summary\": \"View EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\n"}], "role": "tool", "tool_call_id": 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"name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "UbOfaYaYHvHE694P3OSx-A0", "created": 1772073809, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"new_str\": \"        radii = data[\\\"radii\\\"]\", \"insert_line\": 16, \"security_risk\": \"LOW\", \"command\": \"insert\"}", "name": "file_editor"}, "id": "call_5da3bdb81c58462cbb82cc490d7a__thought__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", "type": "function"}], "images": [], "reasoning_content": "Alright, let's get this fixed.  First, the error message clearly points to the missing `radii` variable when loading the data. I knew something was off when I saw the traceback. So, right after extracting the `centers` array on line 16, I need to add `radii = data[\"radii\"]`.  That should take care of that particular problem. \n\nThen, I'll go back and clean up the debugging remnants. I'm sure I left some print statements in there while troubleshooting.  Specifically, let's kill the warning message about the `extra.npz` file, as it was only there for debugging the `load_aux_data` function. That's on line 11, if I recall.  And finally, the error-reporting print statement in `evaluate_aux` also needs to go. I don't need all that noise now that I've found the root cause.  Let's get rid of the `print(f\"Error in evaluate_aux: {e}\")` on line 83.\n\nOkay, that sums it up perfectly.  That should be a clean fix.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, let's get this fixed.  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That should be a clean fix.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 247, "prompt_tokens": 27415, "total_tokens": 27662, "completion_tokens_details": {"reasoning_tokens": 151, "text_tokens": 96}, "prompt_tokens_details": {"text_tokens": 27415}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.008842, "timestamp": 1772073813.58621, "latency_sec": 3.490333318710327, "usage_summary": {"prompt_tokens": 27415, "completion_tokens": 247, "reasoning_tokens": 151, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073828.519-5ac1.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 5 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_5/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_5/main.py\n- Current metrics: $RESULTS_DIR/gen_5/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.4535\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 5 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221\n- Net change (gen_0 -> gen_4): +0.6623"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. I'm going to dive deep into this Circle Packing problem, acting as the numerical analyst and optimization architect I'm supposed to be. My primary goal? To craft a suite of insightful auxiliary evaluation metrics. These aren't just for show; they're the navigational tools that will guide the code evolution system towards that elusive global optimum.\n\nFirst things first: understand the landscape. I'll need to dissect what `evaluate_ori.py` is doing. That's the core, the source of truth for the primary metric. I'll also glance at `gen_5/main.py` to get a feel for how the solutions are structured, the very building blocks we're working with.\n\nThen, I'll scrutinize the progress. That score trend, that's the roadmap. Fluctuations? That's what I live for. Gen-2 peaked, then dipped. Gen-5 is better than Gen-4, but still below the peak. Hmmm… sounds like we're either oscillating, or maybe stuck in a local optimum. Time to diagnose. Overlapping circles? Underutilized space? Boundary violations? Number of circles? Common culprits in this arena.\n\nWith that understanding, I'll design my auxiliary metrics. Given the current state, and the task, it looks like we're in the **OPTIMIZATION** stage, trying to fine-tune and escape local traps. Or maybe **EXPLORATION** if the system needs to find new configurations. I'll craft metrics that capture constraint tension – boundary gradients and circle overlap. I'll also consider metrics that offer insights into the structural sensitivity and component contribution. The idea is to find what helps to escape the local minima.\n\nBut before I can implement a thing, I'll need to check the `EVAL_AGENTS.md` and the existing metrics. Are any of the metrics currently active? Are any of those active metrics just stagnant?\n\nNext up: implementation. I'll build `auxiliary_metrics.py`. I'll need to parse the circle data from `gen_5/results/metrics.json` – positions, radii, the whole shebang. The holy grail is `evaluate_ori.py`, and how it processes the data. I'll read it thoroughly.\n\nFinally, the validation. A simple run to ensure my script runs without errors, and produces output in the right format. After the initial smoke test, I'll update `EVAL_AGENTS.md` to document my journey.\nI need to understand what's in `eval_agent_memory/EVAL_AGENTS.md` and `auxiliary_metrics.py`. Let's get to work!\n", "signature": null}], "tool_calls": [{"id": 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TErd1AOzClf2/zlTkzNrbKMHg", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"summary\": \"View EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\n"}], "role": "tool", "tool_call_id": 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"name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "YLOfaauYCJ3F694PuZW8wQw", "created": 1772073824, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"command\": \"undo_edit\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\"}", "name": "file_editor"}, "id": "call_52681a3f9a144bc2ba355ce692bf__thought__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", "type": "function"}], "images": [], "reasoning_content": "Okay, here's the deal. I'm staring at this code, and I'm *still* making that classic blunder. Dammit! I see it now. I'm trying to insert a new line at line 16, but that's where I declare `num_circles`. So, I insert the new line, which bumps `num_circles` down to line 17. The problem, as clear as day to me, is that I'm referencing `radii` in line 25 – where I'm doing the calculation `radii[i] + radii[j]` – which means `radii` *must* be defined before I can even *think* about using `num_circles`. \n\nRight. Deep breath. The correct order, the *only* sensible order, should be this: First, I grab `centers` from the data, that's fine, stays at line 15. Then, *immediately after*, I need to load the `radii` from the data on a new line, line 16 now. *Then*, and *only then*, can I calculate `num_circles` using the shape of centers on a new line, which will be line 17.\n\nAlright, back to the editor. Time to undo and get this absolutely right. Precision is key here. 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Let's make this work.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 238, "prompt_tokens": 30166, "total_tokens": 30404, "completion_tokens_details": {"reasoning_tokens": 156, "text_tokens": 82}, "prompt_tokens_details": {"text_tokens": 30166}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0096448, "timestamp": 1772073828.5188615, "latency_sec": 2.632753610610962, "usage_summary": {"prompt_tokens": 30166, "completion_tokens": 238, "reasoning_tokens": 156, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073829.759-8188.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 5 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_5/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_5/main.py\n- Current metrics: $RESULTS_DIR/gen_5/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.4535\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 5 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221\n- Net change (gen_0 -> gen_4): +0.6623"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. I'm going to dive deep into this Circle Packing problem, acting as the numerical analyst and optimization architect I'm supposed to be. My primary goal? To craft a suite of insightful auxiliary evaluation metrics. These aren't just for show; they're the navigational tools that will guide the code evolution system towards that elusive global optimum.\n\nFirst things first: understand the landscape. I'll need to dissect what `evaluate_ori.py` is doing. That's the core, the source of truth for the primary metric. I'll also glance at `gen_5/main.py` to get a feel for how the solutions are structured, the very building blocks we're working with.\n\nThen, I'll scrutinize the progress. That score trend, that's the roadmap. Fluctuations? That's what I live for. Gen-2 peaked, then dipped. Gen-5 is better than Gen-4, but still below the peak. Hmmm… sounds like we're either oscillating, or maybe stuck in a local optimum. Time to diagnose. Overlapping circles? Underutilized space? Boundary violations? Number of circles? Common culprits in this arena.\n\nWith that understanding, I'll design my auxiliary metrics. Given the current state, and the task, it looks like we're in the **OPTIMIZATION** stage, trying to fine-tune and escape local traps. Or maybe **EXPLORATION** if the system needs to find new configurations. I'll craft metrics that capture constraint tension – boundary gradients and circle overlap. I'll also consider metrics that offer insights into the structural sensitivity and component contribution. The idea is to find what helps to escape the local minima.\n\nBut before I can implement a thing, I'll need to check the `EVAL_AGENTS.md` and the existing metrics. Are any of the metrics currently active? Are any of those active metrics just stagnant?\n\nNext up: implementation. I'll build `auxiliary_metrics.py`. I'll need to parse the circle data from `gen_5/results/metrics.json` – positions, radii, the whole shebang. The holy grail is `evaluate_ori.py`, and how it processes the data. I'll read it thoroughly.\n\nFinally, the validation. A simple run to ensure my script runs without errors, and produces output in the right format. After the initial smoke test, I'll update `EVAL_AGENTS.md` to document my journey.\nI need to understand what's in `eval_agent_memory/EVAL_AGENTS.md` and `auxiliary_metrics.py`. Let's get to work!\n", "signature": null}], "tool_calls": [{"id": 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TErd1AOzClf2/zlTkzNrbKMHg", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"summary\": \"View EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\n"}], "role": "tool", "tool_call_id": 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"name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "YbOfadC1N_HE694P3OSx-A0", "created": 1772073825, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"command\": \"view\", \"summary\": \"View auxiliary_metrics.py\"}", "name": "file_editor"}, "id": "call_54addf87999f47d6b3d26901fc1a__thought__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", "type": "function"}], "images": [], "reasoning_content": "Alright, let's take stock. First, I need to see what we're working with here. I'm looking at the key performance indicators – the `aux_total_overlap_distance`, the `aux_max_overlap_distance`, that pesky `aux_boundary_violation_distance`, the `aux_min_radius` which always gives me fits, and the `aux_num_violated_circles`. Seems like a decent foundation, addressing the critical issues of overlap and constraint violations directly. Gen 4 had a real performance dive, and the initial theory, of course, was those violations got out of hand, or the radii shrunk to near nothing. So far, so good.\n\nNow, the next logical step is to dive into the code itself. I need to get my hands dirty and see exactly how these auxiliary metrics, specifically in `auxiliary_metrics.py`, are being calculated. Gotta see if the implementation aligns with my understanding and the hypotheses we have. Time to roll up the sleeves.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, let's take stock. 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tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772073836.189-0fe7.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 5 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_5/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_5/main.py\n- Current metrics: $RESULTS_DIR/gen_5/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.4535\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 5 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221\n- Net change (gen_0 -> gen_4): +0.6623"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the plan. I'm going to dive deep into this Circle Packing problem, acting as the numerical analyst and optimization architect I'm supposed to be. My primary goal? To craft a suite of insightful auxiliary evaluation metrics. These aren't just for show; they're the navigational tools that will guide the code evolution system towards that elusive global optimum.\n\nFirst things first: understand the landscape. I'll need to dissect what `evaluate_ori.py` is doing. That's the core, the source of truth for the primary metric. I'll also glance at `gen_5/main.py` to get a feel for how the solutions are structured, the very building blocks we're working with.\n\nThen, I'll scrutinize the progress. That score trend, that's the roadmap. Fluctuations? That's what I live for. Gen-2 peaked, then dipped. Gen-5 is better than Gen-4, but still below the peak. Hmmm… sounds like we're either oscillating, or maybe stuck in a local optimum. Time to diagnose. Overlapping circles? Underutilized space? Boundary violations? Number of circles? Common culprits in this arena.\n\nWith that understanding, I'll design my auxiliary metrics. Given the current state, and the task, it looks like we're in the **OPTIMIZATION** stage, trying to fine-tune and escape local traps. Or maybe **EXPLORATION** if the system needs to find new configurations. I'll craft metrics that capture constraint tension – boundary gradients and circle overlap. I'll also consider metrics that offer insights into the structural sensitivity and component contribution. The idea is to find what helps to escape the local minima.\n\nBut before I can implement a thing, I'll need to check the `EVAL_AGENTS.md` and the existing metrics. Are any of the metrics currently active? Are any of those active metrics just stagnant?\n\nNext up: implementation. I'll build `auxiliary_metrics.py`. I'll need to parse the circle data from `gen_5/results/metrics.json` – positions, radii, the whole shebang. The holy grail is `evaluate_ori.py`, and how it processes the data. I'll read it thoroughly.\n\nFinally, the validation. A simple run to ensure my script runs without errors, and produces output in the right format. After the initial smoke test, I'll update `EVAL_AGENTS.md` to document my journey.\nI need to understand what's in `eval_agent_memory/EVAL_AGENTS.md` and `auxiliary_metrics.py`. Let's get to work!\n", "signature": null}], "tool_calls": [{"id": 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TErd1AOzClf2/zlTkzNrbKMHg", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"summary\": \"View EVAL_AGENTS.md\", \"security_risk\": \"LOW\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t\n     8\t## Generation 4\n     9\tStage: OPTIMIZATION\n    10\tPrimary score: 1.6221\n    11\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    12\tStagnant metrics: none\n    13\tActions:\n    14\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    15\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    16\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    17\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    18\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    19\tNotes:\n    20\t- Initializing auxiliary metrics for the first time.\n    21\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    22\t\n"}], "role": "tool", "tool_call_id": 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TErd1AOzClf2/zlTkzNrbKMHg", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, let's take stock. First, I need to see what we're working with here. I'm looking at the key performance indicators – the `aux_total_overlap_distance`, the `aux_max_overlap_distance`, that pesky `aux_boundary_violation_distance`, the `aux_min_radius` which always gives me fits, and the `aux_num_violated_circles`. Seems like a decent foundation, addressing the critical issues of overlap and constraint violations directly. Gen 4 had a real performance dive, and the initial theory, of course, was those violations got out of hand, or the radii shrunk to near nothing. So far, so good.\n\nNow, the next logical step is to dive into the code itself. I need to get my hands dirty and see exactly how these auxiliary metrics, specifically in `auxiliary_metrics.py`, are being calculated. Gotta see if the implementation aligns with my understanding and the hypotheses we have. Time to roll up the sleeves.\n", "signature": null}], "tool_calls": [{"id": "call_54addf87999f47d6b3d26901fc1a__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\", \"security_risk\": \"LOW\", \"command\": \"view\", \"summary\": \"View auxiliary_metrics.py\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        num_circles = centers.shape[0]\n    17\t        \n    18\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    19\t        total_overlap_distance = 0.0\n    20\t        max_overlap_distance = 0.0\n    21\t\n    22\t        for i in range(num_circles):\n    23\t            for j in range(i + 1, num_circles):\n    24\t                dist = np.linalg.norm(centers[i] - centers[j])\n    25\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    26\t                if dist < min_dist_for_no_overlap:\n    27\t                    overlap = min_dist_for_no_overlap - dist\n    28\t                    total_overlap_distance += overlap\n    29\t                    if overlap > max_overlap_distance:\n    30\t                        max_overlap_distance = overlap\n    31\t\n    32\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    33\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    34\t        \n    35\t        # --- aux_boundary_violation_distance ---\n    36\t        boundary_violation_distance = 0.0\n    37\t        \n    38\t        for i in range(num_circles):\n    39\t            x, y = centers[i]\n    40\t            r = radii[i]\n    41\t\n    42\t            # Check x-bounds\n    43\t            if x - r < 0:\n    44\t                boundary_violation_distance += abs(x - r)\n    45\t            if x + r > 1:\n    46\t                boundary_violation_distance += abs(x + r - 1)\n    47\t            \n    48\t            # Check y-bounds\n    49\t            if y - r < 0:\n    50\t                boundary_violation_distance += abs(y - r)\n    51\t            if y + r > 1:\n    52\t                boundary_violation_distance += abs(y + r - 1)\n    53\t                \n    54\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    55\t\n    56\t        # --- aux_min_radius ---\n    57\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    58\t        \n    59\t        # --- aux_num_violated_circles ---\n    60\t        num_violated_circles = 0\n    61\t        violated_circles_indices = set()\n    62\t\n    63\t        # Check for overlaps (re-do from above to get specific circles)\n    64\t        for i in range(num_circles):\n    65\t            for j in range(i + 1, num_circles):\n    66\t                dist = np.linalg.norm(centers[i] - centers[j])\n    67\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    68\t                if dist < min_dist_for_no_overlap:\n    69\t                    violated_circles_indices.add(i)\n    70\t                    violated_circles_indices.add(j)\n    71\t\n    72\t        # Check for boundary violations (re-do from above to get specific circles)\n    73\t        for i in range(num_circles):\n    74\t            x, y = centers[i]\n    75\t            r = radii[i]\n    76\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    77\t                violated_circles_indices.add(i)\n    78\t                \n    79\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    80\t\n    81\t\n    82\t    except Exception as e:\n    83\t        print(f\"Error in evaluate_aux: {e}\")\n    84\t        # Return default 0.0 for all metrics in case of failure\n    85\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    86\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    87\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    88\t        metrics[\"aux_min_radius\"] = 0.0\n    89\t        metrics[\"aux_num_violated_circles\"] = 0\n    90\t        \n    91\t    return metrics\n    92\t\n    93\t\n"}], "role": "tool", "tool_call_id": "call_54addf87999f47d6b3d26901fc1a__thought__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", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "ZbOfafCQMYr0694P6Y_ImAo", "created": 1772073829, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"command\": \"view\", \"summary\": \"View evaluate_ori.py\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\", \"security_risk\": \"LOW\"}", "name": "file_editor"}, "id": "call_a27d2f5ccdd94d4cbd8fdaa0d415__thought__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", "type": "function"}, {"index": 1, "function": {"arguments": "{\"security_risk\": \"LOW\", \"summary\": \"View gen_5/main.py\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_5/main.py\", \"command\": \"view\"}", "name": "file_editor"}, "id": "call_394a56be9d0c41f19db490b6f6dc", "type": "function"}, {"index": 2, "function": {"arguments": "{\"summary\": \"View gen_5/results/metrics.json\", \"command\": \"view\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_5/results/metrics.json\", \"security_risk\": \"LOW\"}", "name": "file_editor"}, "id": "call_1a48bdb48da84e27b857cb83c80d", "type": "function"}], "images": [], "reasoning_content": "Alright, here's the problem. I've spotted a glaring error in `auxiliary_metrics.py`. Line 25, that's where the trouble starts, and it just gets worse on lines 40, 67, and 75. It's clear that `radii` is being called before it's even been *initialized*. This will undoubtedly crash the script the moment it tries to execute. A trivial oversight, really, but it's got to be fixed.\n\nThe solution is straightforward. I'll need to mirror the way `centers` is handled. Specifically, I need to load `radii` directly from the `extra.npz` file. A quick insertion, something like:\n\n```python\n        data = np.load(extra_file_path)\n        centers = data[\"centers\"]\n        radii = data[\"radii\"] # <--- ADD THIS LINE\n        num_circles = centers.shape[0]\n```\n\nThat should do the trick.\n\nHowever, before I implement the fix, it’s best practice to understand the data's origin. I need to get a clear picture of the structure of this `extra.npz` file. To do that, I'll dive into `evaluate_ori.py` – that should give me a good understanding of how this auxiliary data is used within the larger evaluation framework. Furthermore, I'll examine `gen_5/main.py` to see how the data is generated in the first place, and peek at the `gen_5/results/metrics.json` file. This thorough investigation is crucial for understanding the context and confirming my assumptions about the contents of `extra.npz` before making any modifications. Gotta make sure I'm not overlooking anything and that my fix aligns perfectly with how the data is intended to be used.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the problem. I've spotted a glaring error in `auxiliary_metrics.py`. Line 25, that's where the trouble starts, and it just gets worse on lines 40, 67, and 75. It's clear that `radii` is being called before it's even been *initialized*. This will undoubtedly crash the script the moment it tries to execute. A trivial oversight, really, but it's got to be fixed.\n\nThe solution is straightforward. I'll need to mirror the way `centers` is handled. Specifically, I need to load `radii` directly from the `extra.npz` file. A quick insertion, something like:\n\n```python\n        data = np.load(extra_file_path)\n        centers = data[\"centers\"]\n        radii = data[\"radii\"] # <--- ADD THIS LINE\n        num_circles = centers.shape[0]\n```\n\nThat should do the trick.\n\nHowever, before I implement the fix, it’s best practice to understand the data's origin. I need to get a clear picture of the structure of this `extra.npz` file. To do that, I'll dive into `evaluate_ori.py` – that should give me a good understanding of how this auxiliary data is used within the larger evaluation framework. Furthermore, I'll examine `gen_5/main.py` to see how the data is generated in the first place, and peek at the `gen_5/results/metrics.json` file. This thorough investigation is crucial for understanding the context and confirming my assumptions about the contents of `extra.npz` before making any modifications. Gotta make sure I'm not overlooking anything and that my fix aligns perfectly with how the data is intended to be used.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 430, "prompt_tokens": 8751, "total_tokens": 9181, "completion_tokens_details": {"reasoning_tokens": 215, "text_tokens": 215}, "prompt_tokens_details": {"text_tokens": 8751}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0037002999999999997, "timestamp": 1772073836.1890268, "latency_sec": 6.3973708152771, "usage_summary": {"prompt_tokens": 8751, "completion_tokens": 430, "reasoning_tokens": 215, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772074459.984-5be7.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 10 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_10/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_10/main.py\n- Current metrics: $RESULTS_DIR/gen_10/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.2127\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 10 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221 | gen_5: 2.4535 | gen_6: 2.5414 | gen_7: 1.9800 | gen_8: 0.0000 | gen_9: 2.4743\n- Net change (gen_0 -> gen_9): +1.5145"}], "role": "user"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "1rWfadSDOIr0694P6Y_ImAo", "created": 1772074454, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"security_risk\": \"LOW\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/\", \"summary\": \"List contents of eval_agent_memory directory\"}", "name": "terminal"}, "id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "type": "function"}], "images": [], "reasoning_content": "Alright, here's the deal. My task is clear: I need to scrutinize the progress, update those auxiliary metrics we've been tracking, and, of course, keep a detailed log of everything I do. That's the drill.\n\nLet's dive into the data. The current score, 2.2127, is what we're working with. Now, looking back at the scores from previous generations…hmm. The initial score was 0.9598, and it's generally trended upwards, peaking at 2.5414 in gen_6. The really concerning thing here is that gen_8 score of 0.0000. That screams \"failure\" to me. It's a significant outlier, so we *definitely* need to investigate what went wrong there. While the overall trend has improved from the beginning, the up-and-down nature of the scores raises a red flag. It looks like the optimization process might be getting trapped in local optima or maybe just thrashing around the search space. \n\nConsidering that the current score is lower than both gen_9 and gen_6… we're not exactly moving in the right direction right now.\n\nOkay, let's get down to specifics. The first step is to check the `eval_agent_memory` directory. I'll need to see the `auxiliary_metrics.py` file to understand the current auxiliary metric calculations and the `EVAL_AGENTS.md` file to evaluate the performance of each agent. This is where we'll dig into the data and analyze exactly what's happening.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the deal. My task is clear: I need to scrutinize the progress, update those auxiliary metrics we've been tracking, and, of course, keep a detailed log of everything I do. That's the drill.\n\nLet's dive into the data. The current score, 2.2127, is what we're working with. Now, looking back at the scores from previous generations…hmm. The initial score was 0.9598, and it's generally trended upwards, peaking at 2.5414 in gen_6. The really concerning thing here is that gen_8 score of 0.0000. That screams \"failure\" to me. It's a significant outlier, so we *definitely* need to investigate what went wrong there. While the overall trend has improved from the beginning, the up-and-down nature of the scores raises a red flag. It looks like the optimization process might be getting trapped in local optima or maybe just thrashing around the search space. \n\nConsidering that the current score is lower than both gen_9 and gen_6… we're not exactly moving in the right direction right now.\n\nOkay, let's get down to specifics. The first step is to check the `eval_agent_memory` directory. I'll need to see the `auxiliary_metrics.py` file to understand the current auxiliary metric calculations and the `EVAL_AGENTS.md` file to evaluate the performance of each agent. This is where we'll dig into the data and analyze exactly what's happening.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 379, "prompt_tokens": 5569, "total_tokens": 5948, "completion_tokens_details": {"reasoning_tokens": 296, "text_tokens": 83}, "prompt_tokens_details": {"text_tokens": 5569}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0026182, "timestamp": 1772074459.9844162, "latency_sec": 5.108134984970093, "usage_summary": {"prompt_tokens": 5569, "completion_tokens": 379, "reasoning_tokens": 296, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772074463.762-e518.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 10 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_10/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_10/main.py\n- Current metrics: $RESULTS_DIR/gen_10/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.2127\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 10 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221 | gen_5: 2.4535 | gen_6: 2.5414 | gen_7: 1.9800 | gen_8: 0.0000 | gen_9: 2.4743\n- Net change (gen_0 -> gen_9): +1.5145"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the deal. My task is clear: I need to scrutinize the progress, update those auxiliary metrics we've been tracking, and, of course, keep a detailed log of everything I do. That's the drill.\n\nLet's dive into the data. The current score, 2.2127, is what we're working with. Now, looking back at the scores from previous generations…hmm. The initial score was 0.9598, and it's generally trended upwards, peaking at 2.5414 in gen_6. The really concerning thing here is that gen_8 score of 0.0000. That screams \"failure\" to me. It's a significant outlier, so we *definitely* need to investigate what went wrong there. While the overall trend has improved from the beginning, the up-and-down nature of the scores raises a red flag. It looks like the optimization process might be getting trapped in local optima or maybe just thrashing around the search space. \n\nConsidering that the current score is lower than both gen_9 and gen_6… we're not exactly moving in the right direction right now.\n\nOkay, let's get down to specifics. The first step is to check the `eval_agent_memory` directory. I'll need to see the `auxiliary_metrics.py` file to understand the current auxiliary metric calculations and the `EVAL_AGENTS.md` file to evaluate the performance of each agent. This is where we'll dig into the data and analyze exactly what's happening.\n", "signature": null}], "tool_calls": [{"id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/\", \"summary\": \"List contents of eval_agent_memory directory\"}"}}]}, {"content": [{"type": "text", "text": "total 36\n-rw------- 1 tengxiao tengxiao 2946 Feb 26 02:44 EVAL_AGENTS.md\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:46 __pycache__\n-rw-r--r-- 1 tengxiao tengxiao 7423 Feb 26 02:54 agent_behavior.jsonl\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 agent_runs\n-rw------- 1 tengxiao tengxiao 4157 Feb 26 02:44 auxiliary_metrics.py\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 llm_completions\n-rw-r--r-- 1 tengxiao tengxiao 2898 Feb 26 02:54 service_state.json\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "name": "terminal"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "3LWfacPvKN-G694Po_7k8Qs", "created": 1772074460, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP"}, "function": {"arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"summary\": \"View EVAL_AGENTS.md\", \"command\": \"view\", \"security_risk\": \"LOW\"}", "name": "file_editor"}, "id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "type": "function"}], "images": [], "reasoning_content": "Alright, time to get down to brass tacks. I need to get a handle on where we are, what the current benchmarks look like, and what they're actually measuring. My next step is absolutely crucial: I need to dig into the `EVAL_AGENTS.md` file. That's where all the relevant details regarding the evaluation protocols, the chosen agents, and the specific test conditions will be laid out. Understanding that document is non-negotiable for me. After that, I'll move on to `auxiliary_metrics.py`. That's where the rubber meets the road on the performance side. I need to dissect that file to understand how they are *actually* computing and interpreting these auxiliary metrics. Gotta see the nitty-gritty details, the implementation choices, the specific functions being used. Without a solid grip on these two resources, any further work would be just guesswork, pure noise. This is the foundation - got to get this right.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, time to get down to brass tacks. I need to get a handle on where we are, what the current benchmarks look like, and what they're actually measuring. My next step is absolutely crucial: I need to dig into the `EVAL_AGENTS.md` file. That's where all the relevant details regarding the evaluation protocols, the chosen agents, and the specific test conditions will be laid out. Understanding that document is non-negotiable for me. After that, I'll move on to `auxiliary_metrics.py`. That's where the rubber meets the road on the performance side. I need to dissect that file to understand how they are *actually* computing and interpreting these auxiliary metrics. Gotta see the nitty-gritty details, the implementation choices, the specific functions being used. Without a solid grip on these two resources, any further work would be just guesswork, pure noise. This is the foundation - got to get this right.\n"}], "provider_specific_fields": {"thought_signatures": ["CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP"]}}}], "usage": {"completion_tokens": 120, "prompt_tokens": 6307, "total_tokens": 6427, "completion_tokens_details": {"reasoning_tokens": 31, "text_tokens": 89}, "prompt_tokens_details": {"text_tokens": 6307}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0021920999999999998, "timestamp": 1772074463.7625518, "latency_sec": 3.1190881729125977, "usage_summary": {"prompt_tokens": 6307, "completion_tokens": 120, "reasoning_tokens": 31, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772074467.526-43d2.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 10 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_10/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_10/main.py\n- Current metrics: $RESULTS_DIR/gen_10/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.2127\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 10 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221 | gen_5: 2.4535 | gen_6: 2.5414 | gen_7: 1.9800 | gen_8: 0.0000 | gen_9: 2.4743\n- Net change (gen_0 -> gen_9): +1.5145"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the deal. My task is clear: I need to scrutinize the progress, update those auxiliary metrics we've been tracking, and, of course, keep a detailed log of everything I do. That's the drill.\n\nLet's dive into the data. The current score, 2.2127, is what we're working with. Now, looking back at the scores from previous generations…hmm. The initial score was 0.9598, and it's generally trended upwards, peaking at 2.5414 in gen_6. The really concerning thing here is that gen_8 score of 0.0000. That screams \"failure\" to me. It's a significant outlier, so we *definitely* need to investigate what went wrong there. While the overall trend has improved from the beginning, the up-and-down nature of the scores raises a red flag. It looks like the optimization process might be getting trapped in local optima or maybe just thrashing around the search space. \n\nConsidering that the current score is lower than both gen_9 and gen_6… we're not exactly moving in the right direction right now.\n\nOkay, let's get down to specifics. The first step is to check the `eval_agent_memory` directory. I'll need to see the `auxiliary_metrics.py` file to understand the current auxiliary metric calculations and the `EVAL_AGENTS.md` file to evaluate the performance of each agent. This is where we'll dig into the data and analyze exactly what's happening.\n", "signature": null}], "tool_calls": [{"id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/\", \"summary\": \"List contents of eval_agent_memory directory\"}"}}]}, {"content": [{"type": "text", "text": "total 36\n-rw------- 1 tengxiao tengxiao 2946 Feb 26 02:44 EVAL_AGENTS.md\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:46 __pycache__\n-rw-r--r-- 1 tengxiao tengxiao 7423 Feb 26 02:54 agent_behavior.jsonl\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 agent_runs\n-rw------- 1 tengxiao tengxiao 4157 Feb 26 02:44 auxiliary_metrics.py\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 llm_completions\n-rw-r--r-- 1 tengxiao tengxiao 2898 Feb 26 02:54 service_state.json\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, time to get down to brass tacks. I need to get a handle on where we are, what the current benchmarks look like, and what they're actually measuring. My next step is absolutely crucial: I need to dig into the `EVAL_AGENTS.md` file. That's where all the relevant details regarding the evaluation protocols, the chosen agents, and the specific test conditions will be laid out. Understanding that document is non-negotiable for me. After that, I'll move on to `auxiliary_metrics.py`. That's where the rubber meets the road on the performance side. I need to dissect that file to understand how they are *actually* computing and interpreting these auxiliary metrics. Gotta see the nitty-gritty details, the implementation choices, the specific functions being used. Without a solid grip on these two resources, any further work would be just guesswork, pure noise. This is the foundation - got to get this right.\n", "signature": null}], "tool_calls": [{"id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"summary\": \"View EVAL_AGENTS.md\", \"command\": \"view\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t- `aux_average_radii`: Average radius of all circles.\n     8\t- `aux_radius_standard_deviation`: Standard deviation of radii.\n     9\t- `aux_packed_area_ratio`: Ratio of the total area covered by circles to the unit square area.\n    10\t\n    11\t## Generation 4\n    12\tStage: OPTIMIZATION\n    13\tPrimary score: 1.6221\n    14\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    15\tStagnant metrics: none\n    16\tActions:\n    17\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    18\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    19\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    20\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    21\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    22\tNotes:\n    23\t- Initializing auxiliary metrics for the first time.\n    24\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    25\t\n    26\t## Generation 5\n    27\tStage: OPTIMIZATION\n    28\tPrimary score: 2.4535\n    29\tHypothesis: The previous auxiliary metrics were failing due to a bug (radii not loaded), leading to all 0.0 values. Fixing this bug will enable proper constraint violation diagnosis. Additionally, new metrics are introduced to assess the quality of valid packings, focusing on average circle size, radius distribution, and overall space utilization to guide further optimization.\n    30\tStagnant metrics: aux_total_overlap_distance, aux_max_overlap_distance, aux_boundary_violation_distance, aux_min_radius, aux_num_violated_circles (all were 0.0 due to a bug in previous generation)\n    31\tActions:\n    32\t- REPLACE `auxiliary_metrics.py` (Fixed `radii` loading bug and added `aux_average_radii`, `aux_radius_standard_deviation`, `aux_packed_area_ratio` to improve diagnostic capabilities).\n    33\t- ADD `aux_average_radii` (To track the average size of circles, directly related to the primary score).\n    34\t- ADD `aux_radius_standard_deviation` (To understand the uniformity of circle sizes and identify imbalances).\n    35\t- ADD `aux_packed_area_ratio` (To measure the overall density and efficiency of the packing solution).\n    36\tNotes:\n    37\t- The primary score has improved from gen_4, suggesting the system is recovering.\n    38\t- The previous auxiliary metrics were not providing useful signal due to an implementation error.\n    39\t\n    40\t\n"}], "role": "tool", "tool_call_id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "37WfaaqPM_HE694P3OSx-A0", "created": 1772074463, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py\", \"command\": \"view\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\"}", "name": "file_editor"}, "id": "call_c064bfa737b345c0a69fe558a5f5__thought__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", "type": "function"}], "images": [], "reasoning_content": "Okay, here's what's going through my mind as an expert:\n\nRight, so the `EVAL_AGENTS.md` file clued me in - a nasty bug bit us in `Generation 5`. Damn, looks like the `radii` weren't getting loaded properly. That explains why all the auxiliary metrics went flat to `0.0`.  That's a classic sign of a missing or misconfigured data dependency.\n\nGood, the fix was to swap out the `auxiliary_metrics.py` file. But, more importantly, they've added some new metrics to that file. We've got `aux_average_radii`, `aux_radius_standard_deviation`, and `aux_packed_area_ratio` now. Sounds like we're aiming for a more in-depth analysis of the radius distributions and packing density.\n\nNow, to really understand what's going on, I need to see the current implementation of `auxiliary_metrics.py`. Time to dive in and see how they're calculating these things. I need to check the logic, data dependencies, and potential edge cases of these new metrics, making sure they're correctly implemented and don't introduce any new biases. Let's see what we've got.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as an expert:\n\nRight, so the `EVAL_AGENTS.md` file clued me in - a nasty bug bit us in `Generation 5`. Damn, looks like the `radii` weren't getting loaded properly. That explains why all the auxiliary metrics went flat to `0.0`.  That's a classic sign of a missing or misconfigured data dependency.\n\nGood, the fix was to swap out the `auxiliary_metrics.py` file. But, more importantly, they've added some new metrics to that file. We've got `aux_average_radii`, `aux_radius_standard_deviation`, and `aux_packed_area_ratio` now. Sounds like we're aiming for a more in-depth analysis of the radius distributions and packing density.\n\nNow, to really understand what's going on, I need to see the current implementation of `auxiliary_metrics.py`. Time to dive in and see how they're calculating these things. I need to check the logic, data dependencies, and potential edge cases of these new metrics, making sure they're correctly implemented and don't introduce any new biases. Let's see what we've got.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 191, "prompt_tokens": 7396, "total_tokens": 7587, "completion_tokens_details": {"reasoning_tokens": 104, "text_tokens": 87}, "prompt_tokens_details": {"text_tokens": 7396}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0026963, "timestamp": 1772074467.5260274, "latency_sec": 3.717472791671753, "usage_summary": {"prompt_tokens": 7396, "completion_tokens": 191, "reasoning_tokens": 104, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772074470.992-83bd.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 10 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_10/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_10/main.py\n- Current metrics: $RESULTS_DIR/gen_10/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.2127\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 10 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221 | gen_5: 2.4535 | gen_6: 2.5414 | gen_7: 1.9800 | gen_8: 0.0000 | gen_9: 2.4743\n- Net change (gen_0 -> gen_9): +1.5145"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the deal. My task is clear: I need to scrutinize the progress, update those auxiliary metrics we've been tracking, and, of course, keep a detailed log of everything I do. That's the drill.\n\nLet's dive into the data. The current score, 2.2127, is what we're working with. Now, looking back at the scores from previous generations…hmm. The initial score was 0.9598, and it's generally trended upwards, peaking at 2.5414 in gen_6. The really concerning thing here is that gen_8 score of 0.0000. That screams \"failure\" to me. It's a significant outlier, so we *definitely* need to investigate what went wrong there. While the overall trend has improved from the beginning, the up-and-down nature of the scores raises a red flag. It looks like the optimization process might be getting trapped in local optima or maybe just thrashing around the search space. \n\nConsidering that the current score is lower than both gen_9 and gen_6… we're not exactly moving in the right direction right now.\n\nOkay, let's get down to specifics. The first step is to check the `eval_agent_memory` directory. I'll need to see the `auxiliary_metrics.py` file to understand the current auxiliary metric calculations and the `EVAL_AGENTS.md` file to evaluate the performance of each agent. This is where we'll dig into the data and analyze exactly what's happening.\n", "signature": null}], "tool_calls": [{"id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/\", \"summary\": \"List contents of eval_agent_memory directory\"}"}}]}, {"content": [{"type": "text", "text": "total 36\n-rw------- 1 tengxiao tengxiao 2946 Feb 26 02:44 EVAL_AGENTS.md\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:46 __pycache__\n-rw-r--r-- 1 tengxiao tengxiao 7423 Feb 26 02:54 agent_behavior.jsonl\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 agent_runs\n-rw------- 1 tengxiao tengxiao 4157 Feb 26 02:44 auxiliary_metrics.py\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 llm_completions\n-rw-r--r-- 1 tengxiao tengxiao 2898 Feb 26 02:54 service_state.json\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, time to get down to brass tacks. I need to get a handle on where we are, what the current benchmarks look like, and what they're actually measuring. My next step is absolutely crucial: I need to dig into the `EVAL_AGENTS.md` file. That's where all the relevant details regarding the evaluation protocols, the chosen agents, and the specific test conditions will be laid out. Understanding that document is non-negotiable for me. After that, I'll move on to `auxiliary_metrics.py`. That's where the rubber meets the road on the performance side. I need to dissect that file to understand how they are *actually* computing and interpreting these auxiliary metrics. Gotta see the nitty-gritty details, the implementation choices, the specific functions being used. Without a solid grip on these two resources, any further work would be just guesswork, pure noise. This is the foundation - got to get this right.\n", "signature": null}], "tool_calls": [{"id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"summary\": \"View EVAL_AGENTS.md\", \"command\": \"view\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t- `aux_average_radii`: Average radius of all circles.\n     8\t- `aux_radius_standard_deviation`: Standard deviation of radii.\n     9\t- `aux_packed_area_ratio`: Ratio of the total area covered by circles to the unit square area.\n    10\t\n    11\t## Generation 4\n    12\tStage: OPTIMIZATION\n    13\tPrimary score: 1.6221\n    14\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    15\tStagnant metrics: none\n    16\tActions:\n    17\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    18\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    19\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    20\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    21\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    22\tNotes:\n    23\t- Initializing auxiliary metrics for the first time.\n    24\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    25\t\n    26\t## Generation 5\n    27\tStage: OPTIMIZATION\n    28\tPrimary score: 2.4535\n    29\tHypothesis: The previous auxiliary metrics were failing due to a bug (radii not loaded), leading to all 0.0 values. Fixing this bug will enable proper constraint violation diagnosis. Additionally, new metrics are introduced to assess the quality of valid packings, focusing on average circle size, radius distribution, and overall space utilization to guide further optimization.\n    30\tStagnant metrics: aux_total_overlap_distance, aux_max_overlap_distance, aux_boundary_violation_distance, aux_min_radius, aux_num_violated_circles (all were 0.0 due to a bug in previous generation)\n    31\tActions:\n    32\t- REPLACE `auxiliary_metrics.py` (Fixed `radii` loading bug and added `aux_average_radii`, `aux_radius_standard_deviation`, `aux_packed_area_ratio` to improve diagnostic capabilities).\n    33\t- ADD `aux_average_radii` (To track the average size of circles, directly related to the primary score).\n    34\t- ADD `aux_radius_standard_deviation` (To understand the uniformity of circle sizes and identify imbalances).\n    35\t- ADD `aux_packed_area_ratio` (To measure the overall density and efficiency of the packing solution).\n    36\tNotes:\n    37\t- The primary score has improved from gen_4, suggesting the system is recovering.\n    38\t- The previous auxiliary metrics were not providing useful signal due to an implementation error.\n    39\t\n    40\t\n"}], "role": "tool", "tool_call_id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as an expert:\n\nRight, so the `EVAL_AGENTS.md` file clued me in - a nasty bug bit us in `Generation 5`. Damn, looks like the `radii` weren't getting loaded properly. That explains why all the auxiliary metrics went flat to `0.0`.  That's a classic sign of a missing or misconfigured data dependency.\n\nGood, the fix was to swap out the `auxiliary_metrics.py` file. But, more importantly, they've added some new metrics to that file. We've got `aux_average_radii`, `aux_radius_standard_deviation`, and `aux_packed_area_ratio` now. Sounds like we're aiming for a more in-depth analysis of the radius distributions and packing density.\n\nNow, to really understand what's going on, I need to see the current implementation of `auxiliary_metrics.py`. Time to dive in and see how they're calculating these things. I need to check the logic, data dependencies, and potential edge cases of these new metrics, making sure they're correctly implemented and don't introduce any new biases. Let's see what we've got.\n", "signature": null}], "tool_calls": [{"id": "call_c064bfa737b345c0a69fe558a5f5__thought__CpMDAY89a19jFYM/SRl3IajTHkIyN3VMslGIe2RIeNZLQEB2Vh1pQEbwq1vWOR7q89+FYQU9hJJSBtn09zGmYfTSIfOP8YX6rNURzFih24FAxLq2ZUS2OJvhABwtrF24hRUsH207zLFGHAiwoos5gh190t7akTL3nWzPQ2+HMhB251yqRXhP7ipvbvpK90fUJIuoFX/oMaCEOq4tkskfw30J3U/VlVMfl5i63CqS7v8EFsSsGxEVb6pOSuWdpOA9G2ih87J9kt5bj+KVCno0Tzyhgm1ZE/dkG0lcDQoVEXfHEGvE7mwnBvRy1R3RH1qRIla155wPpFxWleNcZElLPn0doNSKfyYliGFgxW00qinYW6S/il+3q3aVQA9Ifny40Q5EClUasqX0vFf8q0rDGmTWG036uc1usPFcJNiW2drAon1oiNRsvTfRYpY5CdlBmapJwndblpPSl0r4Atd4m1jWgqrpmOLJS/009M/8IffZO1metYLzYGhR5OvkNMvYpetu4ATdTiWVrx+rJbuilI5MyuF/UQ==", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py\", \"command\": \"view\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"] # Fix: Load radii\n    17\t        num_circles = centers.shape[0]\n    18\t        \n    19\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    20\t        total_overlap_distance = 0.0\n    21\t        max_overlap_distance = 0.0\n    22\t\n    23\t        for i in range(num_circles):\n    24\t            for j in range(i + 1, num_circles):\n    25\t                dist = np.linalg.norm(centers[i] - centers[j])\n    26\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    27\t                if dist < min_dist_for_no_overlap:\n    28\t                    overlap = min_dist_for_no_overlap - dist\n    29\t                    total_overlap_distance += overlap\n    30\t                    if overlap > max_overlap_distance:\n    31\t                        max_overlap_distance = overlap\n    32\t\n    33\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    34\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    35\t        \n    36\t        # --- aux_boundary_violation_distance ---\n    37\t        boundary_violation_distance = 0.0\n    38\t        \n    39\t        for i in range(num_circles):\n    40\t            x, y = centers[i]\n    41\t            r = radii[i]\n    42\t\n    43\t            # Check x-bounds\n    44\t            if x - r < 0:\n    45\t                boundary_violation_distance += abs(x - r)\n    46\t            if x + r > 1:\n    47\t                boundary_violation_distance += abs(x + r - 1)\n    48\t            \n    49\t            # Check y-bounds\n    50\t            if y - r < 0:\n    51\t                boundary_violation_distance += abs(y - r)\n    52\t            if y + r > 1:\n    53\t                boundary_violation_distance += abs(y + r - 1)\n    54\t                \n    55\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    56\t\n    57\t        # --- aux_min_radius ---\n    58\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    59\t        \n    60\t        # --- aux_num_violated_circles ---\n    61\t        num_violated_circles = 0\n    62\t        violated_circles_indices = set()\n    63\t\n    64\t        # Check for overlaps (re-do from above to get specific circles)\n    65\t        for i in range(num_circles):\n    66\t            for j in range(i + 1, num_circles):\n    67\t                dist = np.linalg.norm(centers[i] - centers[j])\n    68\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    69\t                if dist < min_dist_for_no_overlap:\n    70\t                    violated_circles_indices.add(i)\n    71\t                    violated_circles_indices.add(j)\n    72\t\n    73\t        # Check for boundary violations (re-do from above to get specific circles)\n    74\t        for i in range(num_circles):\n    75\t            x, y = centers[i]\n    76\t            r = radii[i]\n    77\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    78\t                violated_circles_indices.add(i)\n    79\t                \n    80\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    81\t        \n    82\t        # --- New metrics ---\n    83\t        # aux_average_radii\n    84\t        metrics[\"aux_average_radii\"] = float(np.mean(radii)) if radii.size > 0 else 0.0\n    85\t\n    86\t        # aux_radius_standard_deviation\n    87\t        metrics[\"aux_radius_standard_deviation\"] = float(np.std(radii)) if radii.size > 0 else 0.0\n    88\t        \n    89\t        # aux_packed_area_ratio\n    90\t        metrics[\"aux_packed_area_ratio\"] = float(np.sum(np.pi * radii**2))\n    91\t\n    92\t\n    93\t    except Exception as e:\n    94\t        print(f\"Error in evaluate_aux: {e}\")\n    95\t        # Return default 0.0 for all metrics in case of failure\n    96\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    97\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    98\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    99\t        metrics[\"aux_min_radius\"] = 0.0\n   100\t        metrics[\"aux_num_violated_circles\"] = 0\n   101\t        metrics[\"aux_average_radii\"] = 0.0\n   102\t        metrics[\"aux_radius_standard_deviation\"] = 0.0\n   103\t        metrics[\"aux_packed_area_ratio\"] = 0.0\n   104\t        \n   105\t    return metrics\n   106\t\n   107\t\n"}], "role": "tool", "tool_call_id": "call_c064bfa737b345c0a69fe558a5f5__thought__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", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "47WfaYaMI4r0694P6Y_ImAo", "created": 1772074467, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "CtkDAY89a1+WJGFEqUDGX6zZhC+xbKVnzHUwL85VQ7BIvvjKT2DJjF199sjWoEOCPiP6wTPM8vmZFU3ztBSzhQTwy3U+z3GHC0DNAmFNv11e4G3w88CrlwN9igM8cmcJpjzesN483fggJiSM0cybqD4QuYb1FGaY2jMrhH9UppK3tSMTBpLcXo1scGctV+hHlV3DfPYItxMGtJJ86/6c5qhUpqzdiiX3rfLrGi4wTHST8Z2kl78UPhvMuUA7vgjKOIoRAAsQi5zcqLHsJf2tBWMz4gIaboTZCGAkIfruk53C+Ij4F7Ec5k7IIihctdrYu7PE5cyEDzccVtGatvQuZcYaeKs+SBdJk52hbyo5AVFXuLFQv3K0OydD+CnFz823xLgd8ZGv6YlxvyiREtSkZ+Mgsvy7N89KVqm/tnPwhkv5Pmmf6ZdSlaPDT3n86xG++Tj5aFFsEPTqkaNqrblXIAr4dfwHgkRjwQLO2fLxQ9icMOpPTlOpFLOjuKNTp+Q7CmUgk5ey0761q83qqqb3lrGUBvpv/stwFo06QvliVATXzWsv/jRSM5jv0Vxqk+QrofOe2lnjmgPzWW8LrM1IwY0nr5jFFoVFJJfn4Bsv3y95aQwu1FEflYbL4mI="}, "function": {"arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/results/metrics.json\", \"summary\": \"View metrics.json for gen_10\", \"command\": \"view\"}", "name": "file_editor"}, "id": "call_0b7cf572259f4166bc04d5489b3f__thought__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", "type": "function"}], "images": [], "reasoning_content": "Okay, let's get down to brass tacks. That `auxiliary_metrics.py` file is a solid foundation. I like the inclusion of those overlap, boundary violation, radii distribution, and packed area metrics – they give a good, holistic view of the system's performance. And the `try-except` blocks with those default `0.0` values are a crucial safety net; prevents the whole thing from crashing if something goes awry during calculation. Smart.\n\nNow, the real work begins. I need to dive into `metrics.json` for generation 10. That's where the rubber meets the road. I'll be scrutinizing the values for those key metrics. My goal is to spot any stagnation. Are any of these performance indicators plateauing? That will be the key. This analysis is crucial for formulating my hypothesis for the next phase. Where are the weak points? What improvements are we seeing, and what's holding us back? Let's see what the data reveals.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's get down to brass tacks. That `auxiliary_metrics.py` file is a solid foundation. I like the inclusion of those overlap, boundary violation, radii distribution, and packed area metrics – they give a good, holistic view of the system's performance. And the `try-except` blocks with those default `0.0` values are a crucial safety net; prevents the whole thing from crashing if something goes awry during calculation. Smart.\n\nNow, the real work begins. I need to dive into `metrics.json` for generation 10. That's where the rubber meets the road. I'll be scrutinizing the values for those key metrics. My goal is to spot any stagnation. Are any of these performance indicators plateauing? That will be the key. This analysis is crucial for formulating my hypothesis for the next phase. Where are the weak points? What improvements are we seeing, and what's holding us back? Let's see what the data reveals.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 189, "prompt_tokens": 9350, "total_tokens": 9539, "completion_tokens_details": {"reasoning_tokens": 101, "text_tokens": 88}, "prompt_tokens_details": {"text_tokens": 9350}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0032775, "timestamp": 1772074470.9918292, "latency_sec": 3.42950177192688, "usage_summary": {"prompt_tokens": 9350, "completion_tokens": 189, "reasoning_tokens": 101, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772074477.055-6c83.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 10 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_10/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_10/main.py\n- Current metrics: $RESULTS_DIR/gen_10/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.2127\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 10 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221 | gen_5: 2.4535 | gen_6: 2.5414 | gen_7: 1.9800 | gen_8: 0.0000 | gen_9: 2.4743\n- Net change (gen_0 -> gen_9): +1.5145"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the deal. My task is clear: I need to scrutinize the progress, update those auxiliary metrics we've been tracking, and, of course, keep a detailed log of everything I do. That's the drill.\n\nLet's dive into the data. The current score, 2.2127, is what we're working with. Now, looking back at the scores from previous generations…hmm. The initial score was 0.9598, and it's generally trended upwards, peaking at 2.5414 in gen_6. The really concerning thing here is that gen_8 score of 0.0000. That screams \"failure\" to me. It's a significant outlier, so we *definitely* need to investigate what went wrong there. While the overall trend has improved from the beginning, the up-and-down nature of the scores raises a red flag. It looks like the optimization process might be getting trapped in local optima or maybe just thrashing around the search space. \n\nConsidering that the current score is lower than both gen_9 and gen_6… we're not exactly moving in the right direction right now.\n\nOkay, let's get down to specifics. The first step is to check the `eval_agent_memory` directory. I'll need to see the `auxiliary_metrics.py` file to understand the current auxiliary metric calculations and the `EVAL_AGENTS.md` file to evaluate the performance of each agent. This is where we'll dig into the data and analyze exactly what's happening.\n", "signature": null}], "tool_calls": [{"id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/\", \"summary\": \"List contents of eval_agent_memory directory\"}"}}]}, {"content": [{"type": "text", "text": "total 36\n-rw------- 1 tengxiao tengxiao 2946 Feb 26 02:44 EVAL_AGENTS.md\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:46 __pycache__\n-rw-r--r-- 1 tengxiao tengxiao 7423 Feb 26 02:54 agent_behavior.jsonl\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 agent_runs\n-rw------- 1 tengxiao tengxiao 4157 Feb 26 02:44 auxiliary_metrics.py\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 llm_completions\n-rw-r--r-- 1 tengxiao tengxiao 2898 Feb 26 02:54 service_state.json\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__CqgGAY89a1+2wwUfCcACupqMX219b8JOZjKfZnJ6a0eglEntnijsqcqUcLOTa1fj6hDKywYsZTSBHDfy26W3aQeli9d2BVExuQ248FfJOTFxBnbp4dvscexi1cNyKWejMB/1DLL1xo/0mdaEfs255Q9zvN4qTc5oBDbuLznz2+DVwKojiOHNM8DpK/hbe8aaRfuz/QxNjVOIkQ6I8C7A23qWenWEZ//AWXiI+eCRxzqavhGzk9fmymJ2VYOZxAbAYgg9GttRBGoXXC7O6iOjUogjvilb5xCeSiZuK9u81J/NR9YZYzpd58L/qVKURpVe7wuG3tTVHLweAv9jtfMO5YMlYTa27nyLXADcMcyEQaFl+GwJElCLQAXLwm+ODkzroteP/3y7GY2crOXxOL7Jz4wkIb6/PrgxmnI6D49S9mDfeDoYkzFN4Yjy5h7Q1mLlPGb72zuDnq4yJdTZJORgIlEzfujB8Ths2sP8NMKOk9SmjJvmvldkOZEtfn9dscmaHd7Fhn+sTVC/PFm5X4t+jBxSikJnJE/ZGwYtATTAqKUmoxlR1N2MMX/y+78B/+gzya7cSHJlCJnMY8XzZbDyBLOpWURJqmr4k70ggN4poKwhRQy3Il67gxXV4NE+PqQitK9EbaE+NagpD0q7TGJOutmelVorLTJETJMQhrmN/W8nfpBphBGoUh+D3xlzsD7a0FY82Cl5noO2OQXsIRaRxaF8uFRdeSEdBKilARjRS+xzEt2evkCwxsYnvHW1XLH5whuczdr9JuIgNIcDiXccnCzmzCijh8Gv0LNv/dxpW4c1Gm9XO+yZAkTqsTtwfb2gXcovbZSQdERzXxvXh9NRmaFv5wTQK4tZmwWRD0i7+tD6G/z88/xOsio05nKPbK0mY+hMPqfKZhGk7d/63zUDby/KO0v32CEqdffrF2JijZgwEIvEEvNg9vrNNUwp5UYpkh/fEs+PSNDPDeFR+71kp27bAH5/2cSaSUjQ5KvtrZIFcYvI6OGFP8ueW907dR4Zjbd8hOICyjFqb4CG4Oj7HTn1lQ91xzu3pTJbSQqWX7ufN7Fr0limVQyFww==", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, time to get down to brass tacks. I need to get a handle on where we are, what the current benchmarks look like, and what they're actually measuring. My next step is absolutely crucial: I need to dig into the `EVAL_AGENTS.md` file. That's where all the relevant details regarding the evaluation protocols, the chosen agents, and the specific test conditions will be laid out. Understanding that document is non-negotiable for me. After that, I'll move on to `auxiliary_metrics.py`. That's where the rubber meets the road on the performance side. I need to dissect that file to understand how they are *actually* computing and interpreting these auxiliary metrics. Gotta see the nitty-gritty details, the implementation choices, the specific functions being used. Without a solid grip on these two resources, any further work would be just guesswork, pure noise. This is the foundation - got to get this right.\n", "signature": null}], "tool_calls": [{"id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"summary\": \"View EVAL_AGENTS.md\", \"command\": \"view\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t- `aux_average_radii`: Average radius of all circles.\n     8\t- `aux_radius_standard_deviation`: Standard deviation of radii.\n     9\t- `aux_packed_area_ratio`: Ratio of the total area covered by circles to the unit square area.\n    10\t\n    11\t## Generation 4\n    12\tStage: OPTIMIZATION\n    13\tPrimary score: 1.6221\n    14\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    15\tStagnant metrics: none\n    16\tActions:\n    17\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    18\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    19\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    20\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    21\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    22\tNotes:\n    23\t- Initializing auxiliary metrics for the first time.\n    24\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    25\t\n    26\t## Generation 5\n    27\tStage: OPTIMIZATION\n    28\tPrimary score: 2.4535\n    29\tHypothesis: The previous auxiliary metrics were failing due to a bug (radii not loaded), leading to all 0.0 values. Fixing this bug will enable proper constraint violation diagnosis. Additionally, new metrics are introduced to assess the quality of valid packings, focusing on average circle size, radius distribution, and overall space utilization to guide further optimization.\n    30\tStagnant metrics: aux_total_overlap_distance, aux_max_overlap_distance, aux_boundary_violation_distance, aux_min_radius, aux_num_violated_circles (all were 0.0 due to a bug in previous generation)\n    31\tActions:\n    32\t- REPLACE `auxiliary_metrics.py` (Fixed `radii` loading bug and added `aux_average_radii`, `aux_radius_standard_deviation`, `aux_packed_area_ratio` to improve diagnostic capabilities).\n    33\t- ADD `aux_average_radii` (To track the average size of circles, directly related to the primary score).\n    34\t- ADD `aux_radius_standard_deviation` (To understand the uniformity of circle sizes and identify imbalances).\n    35\t- ADD `aux_packed_area_ratio` (To measure the overall density and efficiency of the packing solution).\n    36\tNotes:\n    37\t- The primary score has improved from gen_4, suggesting the system is recovering.\n    38\t- The previous auxiliary metrics were not providing useful signal due to an implementation error.\n    39\t\n    40\t\n"}], "role": "tool", "tool_call_id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as an expert:\n\nRight, so the `EVAL_AGENTS.md` file clued me in - a nasty bug bit us in `Generation 5`. Damn, looks like the `radii` weren't getting loaded properly. That explains why all the auxiliary metrics went flat to `0.0`.  That's a classic sign of a missing or misconfigured data dependency.\n\nGood, the fix was to swap out the `auxiliary_metrics.py` file. But, more importantly, they've added some new metrics to that file. We've got `aux_average_radii`, `aux_radius_standard_deviation`, and `aux_packed_area_ratio` now. Sounds like we're aiming for a more in-depth analysis of the radius distributions and packing density.\n\nNow, to really understand what's going on, I need to see the current implementation of `auxiliary_metrics.py`. Time to dive in and see how they're calculating these things. I need to check the logic, data dependencies, and potential edge cases of these new metrics, making sure they're correctly implemented and don't introduce any new biases. Let's see what we've got.\n", "signature": null}], "tool_calls": [{"id": "call_c064bfa737b345c0a69fe558a5f5__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py\", \"command\": \"view\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"] # Fix: Load radii\n    17\t        num_circles = centers.shape[0]\n    18\t        \n    19\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    20\t        total_overlap_distance = 0.0\n    21\t        max_overlap_distance = 0.0\n    22\t\n    23\t        for i in range(num_circles):\n    24\t            for j in range(i + 1, num_circles):\n    25\t                dist = np.linalg.norm(centers[i] - centers[j])\n    26\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    27\t                if dist < min_dist_for_no_overlap:\n    28\t                    overlap = min_dist_for_no_overlap - dist\n    29\t                    total_overlap_distance += overlap\n    30\t                    if overlap > max_overlap_distance:\n    31\t                        max_overlap_distance = overlap\n    32\t\n    33\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    34\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    35\t        \n    36\t        # --- aux_boundary_violation_distance ---\n    37\t        boundary_violation_distance = 0.0\n    38\t        \n    39\t        for i in range(num_circles):\n    40\t            x, y = centers[i]\n    41\t            r = radii[i]\n    42\t\n    43\t            # Check x-bounds\n    44\t            if x - r < 0:\n    45\t                boundary_violation_distance += abs(x - r)\n    46\t            if x + r > 1:\n    47\t                boundary_violation_distance += abs(x + r - 1)\n    48\t            \n    49\t            # Check y-bounds\n    50\t            if y - r < 0:\n    51\t                boundary_violation_distance += abs(y - r)\n    52\t            if y + r > 1:\n    53\t                boundary_violation_distance += abs(y + r - 1)\n    54\t                \n    55\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    56\t\n    57\t        # --- aux_min_radius ---\n    58\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    59\t        \n    60\t        # --- aux_num_violated_circles ---\n    61\t        num_violated_circles = 0\n    62\t        violated_circles_indices = set()\n    63\t\n    64\t        # Check for overlaps (re-do from above to get specific circles)\n    65\t        for i in range(num_circles):\n    66\t            for j in range(i + 1, num_circles):\n    67\t                dist = np.linalg.norm(centers[i] - centers[j])\n    68\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    69\t                if dist < min_dist_for_no_overlap:\n    70\t                    violated_circles_indices.add(i)\n    71\t                    violated_circles_indices.add(j)\n    72\t\n    73\t        # Check for boundary violations (re-do from above to get specific circles)\n    74\t        for i in range(num_circles):\n    75\t            x, y = centers[i]\n    76\t            r = radii[i]\n    77\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    78\t                violated_circles_indices.add(i)\n    79\t                \n    80\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    81\t        \n    82\t        # --- New metrics ---\n    83\t        # aux_average_radii\n    84\t        metrics[\"aux_average_radii\"] = float(np.mean(radii)) if radii.size > 0 else 0.0\n    85\t\n    86\t        # aux_radius_standard_deviation\n    87\t        metrics[\"aux_radius_standard_deviation\"] = float(np.std(radii)) if radii.size > 0 else 0.0\n    88\t        \n    89\t        # aux_packed_area_ratio\n    90\t        metrics[\"aux_packed_area_ratio\"] = float(np.sum(np.pi * radii**2))\n    91\t\n    92\t\n    93\t    except Exception as e:\n    94\t        print(f\"Error in evaluate_aux: {e}\")\n    95\t        # Return default 0.0 for all metrics in case of failure\n    96\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    97\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    98\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    99\t        metrics[\"aux_min_radius\"] = 0.0\n   100\t        metrics[\"aux_num_violated_circles\"] = 0\n   101\t        metrics[\"aux_average_radii\"] = 0.0\n   102\t        metrics[\"aux_radius_standard_deviation\"] = 0.0\n   103\t        metrics[\"aux_packed_area_ratio\"] = 0.0\n   104\t        \n   105\t    return metrics\n   106\t\n   107\t\n"}], "role": "tool", "tool_call_id": "call_c064bfa737b345c0a69fe558a5f5__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's get down to brass tacks. That `auxiliary_metrics.py` file is a solid foundation. I like the inclusion of those overlap, boundary violation, radii distribution, and packed area metrics – they give a good, holistic view of the system's performance. And the `try-except` blocks with those default `0.0` values are a crucial safety net; prevents the whole thing from crashing if something goes awry during calculation. Smart.\n\nNow, the real work begins. I need to dive into `metrics.json` for generation 10. That's where the rubber meets the road. I'll be scrutinizing the values for those key metrics. My goal is to spot any stagnation. Are any of these performance indicators plateauing? That will be the key. This analysis is crucial for formulating my hypothesis for the next phase. Where are the weak points? What improvements are we seeing, and what's holding us back? Let's see what the data reveals.\n", "signature": null}], "tool_calls": [{"id": "call_0b7cf572259f4166bc04d5489b3f__thought__CtkDAY89a1+WJGFEqUDGX6zZhC+xbKVnzHUwL85VQ7BIvvjKT2DJjF199sjWoEOCPiP6wTPM8vmZFU3ztBSzhQTwy3U+z3GHC0DNAmFNv11e4G3w88CrlwN9igM8cmcJpjzesN483fggJiSM0cybqD4QuYb1FGaY2jMrhH9UppK3tSMTBpLcXo1scGctV+hHlV3DfPYItxMGtJJ86/6c5qhUpqzdiiX3rfLrGi4wTHST8Z2kl78UPhvMuUA7vgjKOIoRAAsQi5zcqLHsJf2tBWMz4gIaboTZCGAkIfruk53C+Ij4F7Ec5k7IIihctdrYu7PE5cyEDzccVtGatvQuZcYaeKs+SBdJk52hbyo5AVFXuLFQv3K0OydD+CnFz823xLgd8ZGv6YlxvyiREtSkZ+Mgsvy7N89KVqm/tnPwhkv5Pmmf6ZdSlaPDT3n86xG++Tj5aFFsEPTqkaNqrblXIAr4dfwHgkRjwQLO2fLxQ9icMOpPTlOpFLOjuKNTp+Q7CmUgk5ey0761q83qqqb3lrGUBvpv/stwFo06QvliVATXzWsv/jRSM5jv0Vxqk+QrofOe2lnjmgPzWW8LrM1IwY0nr5jFFoVFJJfn4Bsv3y95aQwu1FEflYbL4mI=", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/results/metrics.json\", \"summary\": \"View metrics.json for gen_10\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/results/metrics.json:\n     1\t{\n     2\t  \"combined_score\": 2.2127378037142638,\n     3\t  \"correct\": true,\n     4\t  \"primary\": {\n     5\t    \"combined_score\": 2.2127378037142638,\n     6\t    \"public\": {\n     7\t      \"centers_str\": \"  centers[0] = (0.0860, 0.0915)\\n  centers[1] = (0.2918, 0.0922)\\n  centers[2] = (0.5000, 0.0864)\\n  centers[3] = (0.7072, 0.0845)\\n  centers[4] = (0.9190, 0.0968)\\n  centers[5] = (0.0902, 0.3050)\\n  centers[6] = (0.2967, 0.2972)\\n  centers[7] = (0.5025, 0.2914)\\n  centers[8] = (0.7075, 0.2895)\\n  centers[9] = (0.9138, 0.3033)\\n  centers[10] = (0.0000, 0.4997)\\n  centers[11] = (0.2043, 0.5146)\\n  centers[12] = (0.4047, 0.4715)\\n  centers[13] = (0.6015, 0.5286)\\n  centers[14] = (0.7987, 0.4730)\\n  centers[15] = (1.0000, 0.5115)\\n  centers[16] = (0.0877, 0.6980)\\n  centers[17] = (0.2930, 0.6994)\\n  centers[18] = (0.4981, 0.7056)\\n  centers[19] = (0.7108, 0.7025)\\n  centers[20] = (0.9162, 0.6993)\\n  centers[21] = (0.0884, 0.9118)\\n  centers[22] = (0.2962, 0.9050)\\n  centers[23] = (0.5048, 0.9113)\\n  centers[24] = (0.7136, 0.9082)\\n  centers[25] = (0.9186, 0.9065)\",\n     8\t      \"num_circles\": 26\n     9\t    },\n    10\t    \"private\": {\n    11\t      \"reported_sum_of_radii\": 2.2127378037142638\n    12\t    },\n    13\t    \"execution_time_mean\": 4.81304601021111,\n    14\t    \"execution_time_std\": 0.0,\n    15\t    \"num_valid_runs\": 1,\n    16\t    \"num_invalid_runs\": 0,\n    17\t    \"all_validation_errors\": [],\n    18\t    \"correct\": true,\n    19\t    \"validation_error\": null\n    20\t  },\n    21\t  \"auxiliary\": {\n    22\t    \"aux_total_overlap_distance\": 0.0,\n    23\t    \"aux_max_overlap_distance\": 0.0,\n    24\t    \"aux_boundary_violation_distance\": 0.0,\n    25\t    \"aux_min_radius\": 0.0,\n    26\t    \"aux_num_violated_circles\": 0.0,\n    27\t    \"aux_average_radii\": 0.0851053001428563,\n    28\t    \"aux_radius_standard_deviation\": 0.027904573430154296,\n    29\t    \"aux_packed_area_ratio\": 0.6552137387225716,\n    30\t    \"aux_metric_eval_success\": 1.0,\n    31\t    \"aux_metric_error_code\": 0.0,\n    32\t    \"aux_metric_error_message_length\": 0.0,\n    33\t    \"aux_metric_error_detail_length\": 0.0,\n    34\t    \"aux_metric_non_numeric_dropped_count\": 0.0\n    35\t  },\n    36\t  \"auxiliary_descriptions\": {\n    37\t    \"aux_total_overlap_distance\": \"Total sum of penetration depths between overlapping circles.\",\n    38\t    \"aux_max_overlap_distance\": \"Maximum penetration depth between any two overlapping circles.\",\n    39\t    \"aux_boundary_violation_distance\": \"Total sum of distances a circle extends beyond the unit square boundary.\",\n    40\t    \"aux_min_radius\": \"Smallest radius among all circles, indicating potential degeneration.\",\n    41\t    \"aux_num_violated_circles\": \"Count of circles that violate either overlap or boundary constraints.\",\n    42\t    \"aux_average_radii\": \"Average radius of all circles.\",\n    43\t    \"aux_radius_standard_deviation\": \"Standard deviation of radii.\",\n    44\t    \"aux_packed_area_ratio\": \"Ratio of the total area covered by circles to the unit square area.\"\n    45\t  },\n    46\t  \"timestamp\": 1772074454.809435,\n    47\t  \"generation\": 10\n    48\t}\n"}], "role": "tool", "tool_call_id": "call_0b7cf572259f4166bc04d5489b3f__thought__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", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "57Wfaa2wA_2R694Pl4bWwA4", "created": 1772074471, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_9/results/metrics.json\", \"security_risk\": \"LOW\", \"summary\": \"View metrics.json for gen_9\", \"command\": \"view\"}", "name": "file_editor"}, "id": "call_d737af0b89834ae5bb663af49c64__thought__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", "type": "function"}], "images": [], "reasoning_content": "Okay, let's break this down. First off, I'm looking at the metrics from `gen_10/results/metrics.json`. The good news is, the overlap and boundary violation metrics are all zero. Great! No feasibility issues here.\n\nBut hold on, `aux_min_radius` is also zero. Hmm. That's a red flag. I know from the code that this gets calculated as the minimum radius of all the circles. If it's zero, that means at least one circle has a radius of zero. That's not ideal because it's essentially a non-existent circle. It doesn't contribute to the overall objective – maximizing the packed area.\n\nThis smells like a potential optimization \"cheat\". The algorithm might be adding these zero-radius circles to satisfy constraints like a minimum number of circles, without actually improving the packing density. It's essentially gaming the system.\n\nI need to investigate further. To see if this zero-radius situation is a recent phenomenon or a recurring issue, I need to check the metrics from previous generations. I'll need the `metrics.json` files from earlier runs. Let's start by looking at `gen_9` and `gen_7`. I'll skip `gen_8` because it looks like a failed run with zero primary score. This will let me see if this zero-radius issue is new, or if it's been present for a while, potentially indicating a problem with the optimization strategy.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's break this down. First off, I'm looking at the metrics from `gen_10/results/metrics.json`. The good news is, the overlap and boundary violation metrics are all zero. Great! No feasibility issues here.\n\nBut hold on, `aux_min_radius` is also zero. Hmm. That's a red flag. I know from the code that this gets calculated as the minimum radius of all the circles. If it's zero, that means at least one circle has a radius of zero. That's not ideal because it's essentially a non-existent circle. It doesn't contribute to the overall objective – maximizing the packed area.\n\nThis smells like a potential optimization \"cheat\". The algorithm might be adding these zero-radius circles to satisfy constraints like a minimum number of circles, without actually improving the packing density. It's essentially gaming the system.\n\nI need to investigate further. To see if this zero-radius situation is a recent phenomenon or a recurring issue, I need to check the metrics from previous generations. I'll need the `metrics.json` files from earlier runs. Let's start by looking at `gen_9` and `gen_7`. I'll skip `gen_8` because it looks like a failed run with zero primary score. This will let me see if this zero-radius issue is new, or if it's been present for a while, potentially indicating a problem with the optimization strategy.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 528, "prompt_tokens": 11141, "total_tokens": 11669, "completion_tokens_details": {"reasoning_tokens": 442, "text_tokens": 86}, "prompt_tokens_details": {"text_tokens": 11141}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.004662299999999999, "timestamp": 1772074477.0551884, "latency_sec": 6.024538040161133, "usage_summary": {"prompt_tokens": 11141, "completion_tokens": 528, "reasoning_tokens": 442, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772074481.525-eea4.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 10 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_10/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_10/main.py\n- Current metrics: $RESULTS_DIR/gen_10/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.2127\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 10 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221 | gen_5: 2.4535 | gen_6: 2.5414 | gen_7: 1.9800 | gen_8: 0.0000 | gen_9: 2.4743\n- Net change (gen_0 -> gen_9): +1.5145"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the deal. My task is clear: I need to scrutinize the progress, update those auxiliary metrics we've been tracking, and, of course, keep a detailed log of everything I do. That's the drill.\n\nLet's dive into the data. The current score, 2.2127, is what we're working with. Now, looking back at the scores from previous generations…hmm. The initial score was 0.9598, and it's generally trended upwards, peaking at 2.5414 in gen_6. The really concerning thing here is that gen_8 score of 0.0000. That screams \"failure\" to me. It's a significant outlier, so we *definitely* need to investigate what went wrong there. While the overall trend has improved from the beginning, the up-and-down nature of the scores raises a red flag. It looks like the optimization process might be getting trapped in local optima or maybe just thrashing around the search space. \n\nConsidering that the current score is lower than both gen_9 and gen_6… we're not exactly moving in the right direction right now.\n\nOkay, let's get down to specifics. The first step is to check the `eval_agent_memory` directory. I'll need to see the `auxiliary_metrics.py` file to understand the current auxiliary metric calculations and the `EVAL_AGENTS.md` file to evaluate the performance of each agent. This is where we'll dig into the data and analyze exactly what's happening.\n", "signature": null}], "tool_calls": [{"id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/\", \"summary\": \"List contents of eval_agent_memory directory\"}"}}]}, {"content": [{"type": "text", "text": "total 36\n-rw------- 1 tengxiao tengxiao 2946 Feb 26 02:44 EVAL_AGENTS.md\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:46 __pycache__\n-rw-r--r-- 1 tengxiao tengxiao 7423 Feb 26 02:54 agent_behavior.jsonl\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 agent_runs\n-rw------- 1 tengxiao tengxiao 4157 Feb 26 02:44 auxiliary_metrics.py\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 llm_completions\n-rw-r--r-- 1 tengxiao tengxiao 2898 Feb 26 02:54 service_state.json\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, time to get down to brass tacks. I need to get a handle on where we are, what the current benchmarks look like, and what they're actually measuring. My next step is absolutely crucial: I need to dig into the `EVAL_AGENTS.md` file. That's where all the relevant details regarding the evaluation protocols, the chosen agents, and the specific test conditions will be laid out. Understanding that document is non-negotiable for me. After that, I'll move on to `auxiliary_metrics.py`. That's where the rubber meets the road on the performance side. I need to dissect that file to understand how they are *actually* computing and interpreting these auxiliary metrics. Gotta see the nitty-gritty details, the implementation choices, the specific functions being used. Without a solid grip on these two resources, any further work would be just guesswork, pure noise. This is the foundation - got to get this right.\n", "signature": null}], "tool_calls": [{"id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"summary\": \"View EVAL_AGENTS.md\", \"command\": \"view\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t- `aux_average_radii`: Average radius of all circles.\n     8\t- `aux_radius_standard_deviation`: Standard deviation of radii.\n     9\t- `aux_packed_area_ratio`: Ratio of the total area covered by circles to the unit square area.\n    10\t\n    11\t## Generation 4\n    12\tStage: OPTIMIZATION\n    13\tPrimary score: 1.6221\n    14\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    15\tStagnant metrics: none\n    16\tActions:\n    17\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    18\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    19\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    20\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    21\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    22\tNotes:\n    23\t- Initializing auxiliary metrics for the first time.\n    24\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    25\t\n    26\t## Generation 5\n    27\tStage: OPTIMIZATION\n    28\tPrimary score: 2.4535\n    29\tHypothesis: The previous auxiliary metrics were failing due to a bug (radii not loaded), leading to all 0.0 values. Fixing this bug will enable proper constraint violation diagnosis. Additionally, new metrics are introduced to assess the quality of valid packings, focusing on average circle size, radius distribution, and overall space utilization to guide further optimization.\n    30\tStagnant metrics: aux_total_overlap_distance, aux_max_overlap_distance, aux_boundary_violation_distance, aux_min_radius, aux_num_violated_circles (all were 0.0 due to a bug in previous generation)\n    31\tActions:\n    32\t- REPLACE `auxiliary_metrics.py` (Fixed `radii` loading bug and added `aux_average_radii`, `aux_radius_standard_deviation`, `aux_packed_area_ratio` to improve diagnostic capabilities).\n    33\t- ADD `aux_average_radii` (To track the average size of circles, directly related to the primary score).\n    34\t- ADD `aux_radius_standard_deviation` (To understand the uniformity of circle sizes and identify imbalances).\n    35\t- ADD `aux_packed_area_ratio` (To measure the overall density and efficiency of the packing solution).\n    36\tNotes:\n    37\t- The primary score has improved from gen_4, suggesting the system is recovering.\n    38\t- The previous auxiliary metrics were not providing useful signal due to an implementation error.\n    39\t\n    40\t\n"}], "role": "tool", "tool_call_id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as an expert:\n\nRight, so the `EVAL_AGENTS.md` file clued me in - a nasty bug bit us in `Generation 5`. Damn, looks like the `radii` weren't getting loaded properly. That explains why all the auxiliary metrics went flat to `0.0`.  That's a classic sign of a missing or misconfigured data dependency.\n\nGood, the fix was to swap out the `auxiliary_metrics.py` file. But, more importantly, they've added some new metrics to that file. We've got `aux_average_radii`, `aux_radius_standard_deviation`, and `aux_packed_area_ratio` now. Sounds like we're aiming for a more in-depth analysis of the radius distributions and packing density.\n\nNow, to really understand what's going on, I need to see the current implementation of `auxiliary_metrics.py`. Time to dive in and see how they're calculating these things. I need to check the logic, data dependencies, and potential edge cases of these new metrics, making sure they're correctly implemented and don't introduce any new biases. Let's see what we've got.\n", "signature": null}], "tool_calls": [{"id": "call_c064bfa737b345c0a69fe558a5f5__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py\", \"command\": \"view\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"] # Fix: Load radii\n    17\t        num_circles = centers.shape[0]\n    18\t        \n    19\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    20\t        total_overlap_distance = 0.0\n    21\t        max_overlap_distance = 0.0\n    22\t\n    23\t        for i in range(num_circles):\n    24\t            for j in range(i + 1, num_circles):\n    25\t                dist = np.linalg.norm(centers[i] - centers[j])\n    26\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    27\t                if dist < min_dist_for_no_overlap:\n    28\t                    overlap = min_dist_for_no_overlap - dist\n    29\t                    total_overlap_distance += overlap\n    30\t                    if overlap > max_overlap_distance:\n    31\t                        max_overlap_distance = overlap\n    32\t\n    33\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    34\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    35\t        \n    36\t        # --- aux_boundary_violation_distance ---\n    37\t        boundary_violation_distance = 0.0\n    38\t        \n    39\t        for i in range(num_circles):\n    40\t            x, y = centers[i]\n    41\t            r = radii[i]\n    42\t\n    43\t            # Check x-bounds\n    44\t            if x - r < 0:\n    45\t                boundary_violation_distance += abs(x - r)\n    46\t            if x + r > 1:\n    47\t                boundary_violation_distance += abs(x + r - 1)\n    48\t            \n    49\t            # Check y-bounds\n    50\t            if y - r < 0:\n    51\t                boundary_violation_distance += abs(y - r)\n    52\t            if y + r > 1:\n    53\t                boundary_violation_distance += abs(y + r - 1)\n    54\t                \n    55\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    56\t\n    57\t        # --- aux_min_radius ---\n    58\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    59\t        \n    60\t        # --- aux_num_violated_circles ---\n    61\t        num_violated_circles = 0\n    62\t        violated_circles_indices = set()\n    63\t\n    64\t        # Check for overlaps (re-do from above to get specific circles)\n    65\t        for i in range(num_circles):\n    66\t            for j in range(i + 1, num_circles):\n    67\t                dist = np.linalg.norm(centers[i] - centers[j])\n    68\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    69\t                if dist < min_dist_for_no_overlap:\n    70\t                    violated_circles_indices.add(i)\n    71\t                    violated_circles_indices.add(j)\n    72\t\n    73\t        # Check for boundary violations (re-do from above to get specific circles)\n    74\t        for i in range(num_circles):\n    75\t            x, y = centers[i]\n    76\t            r = radii[i]\n    77\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    78\t                violated_circles_indices.add(i)\n    79\t                \n    80\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    81\t        \n    82\t        # --- New metrics ---\n    83\t        # aux_average_radii\n    84\t        metrics[\"aux_average_radii\"] = float(np.mean(radii)) if radii.size > 0 else 0.0\n    85\t\n    86\t        # aux_radius_standard_deviation\n    87\t        metrics[\"aux_radius_standard_deviation\"] = float(np.std(radii)) if radii.size > 0 else 0.0\n    88\t        \n    89\t        # aux_packed_area_ratio\n    90\t        metrics[\"aux_packed_area_ratio\"] = float(np.sum(np.pi * radii**2))\n    91\t\n    92\t\n    93\t    except Exception as e:\n    94\t        print(f\"Error in evaluate_aux: {e}\")\n    95\t        # Return default 0.0 for all metrics in case of failure\n    96\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    97\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    98\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    99\t        metrics[\"aux_min_radius\"] = 0.0\n   100\t        metrics[\"aux_num_violated_circles\"] = 0\n   101\t        metrics[\"aux_average_radii\"] = 0.0\n   102\t        metrics[\"aux_radius_standard_deviation\"] = 0.0\n   103\t        metrics[\"aux_packed_area_ratio\"] = 0.0\n   104\t        \n   105\t    return metrics\n   106\t\n   107\t\n"}], "role": "tool", "tool_call_id": "call_c064bfa737b345c0a69fe558a5f5__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's get down to brass tacks. That `auxiliary_metrics.py` file is a solid foundation. I like the inclusion of those overlap, boundary violation, radii distribution, and packed area metrics – they give a good, holistic view of the system's performance. And the `try-except` blocks with those default `0.0` values are a crucial safety net; prevents the whole thing from crashing if something goes awry during calculation. Smart.\n\nNow, the real work begins. I need to dive into `metrics.json` for generation 10. That's where the rubber meets the road. I'll be scrutinizing the values for those key metrics. My goal is to spot any stagnation. Are any of these performance indicators plateauing? That will be the key. This analysis is crucial for formulating my hypothesis for the next phase. Where are the weak points? What improvements are we seeing, and what's holding us back? Let's see what the data reveals.\n", "signature": null}], "tool_calls": [{"id": "call_0b7cf572259f4166bc04d5489b3f__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/results/metrics.json\", \"summary\": \"View metrics.json for gen_10\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/results/metrics.json:\n     1\t{\n     2\t  \"combined_score\": 2.2127378037142638,\n     3\t  \"correct\": true,\n     4\t  \"primary\": {\n     5\t    \"combined_score\": 2.2127378037142638,\n     6\t    \"public\": {\n     7\t      \"centers_str\": \"  centers[0] = (0.0860, 0.0915)\\n  centers[1] = (0.2918, 0.0922)\\n  centers[2] = (0.5000, 0.0864)\\n  centers[3] = (0.7072, 0.0845)\\n  centers[4] = (0.9190, 0.0968)\\n  centers[5] = (0.0902, 0.3050)\\n  centers[6] = (0.2967, 0.2972)\\n  centers[7] = (0.5025, 0.2914)\\n  centers[8] = (0.7075, 0.2895)\\n  centers[9] = (0.9138, 0.3033)\\n  centers[10] = (0.0000, 0.4997)\\n  centers[11] = (0.2043, 0.5146)\\n  centers[12] = (0.4047, 0.4715)\\n  centers[13] = (0.6015, 0.5286)\\n  centers[14] = (0.7987, 0.4730)\\n  centers[15] = (1.0000, 0.5115)\\n  centers[16] = (0.0877, 0.6980)\\n  centers[17] = (0.2930, 0.6994)\\n  centers[18] = (0.4981, 0.7056)\\n  centers[19] = (0.7108, 0.7025)\\n  centers[20] = (0.9162, 0.6993)\\n  centers[21] = (0.0884, 0.9118)\\n  centers[22] = (0.2962, 0.9050)\\n  centers[23] = (0.5048, 0.9113)\\n  centers[24] = (0.7136, 0.9082)\\n  centers[25] = (0.9186, 0.9065)\",\n     8\t      \"num_circles\": 26\n     9\t    },\n    10\t    \"private\": {\n    11\t      \"reported_sum_of_radii\": 2.2127378037142638\n    12\t    },\n    13\t    \"execution_time_mean\": 4.81304601021111,\n    14\t    \"execution_time_std\": 0.0,\n    15\t    \"num_valid_runs\": 1,\n    16\t    \"num_invalid_runs\": 0,\n    17\t    \"all_validation_errors\": [],\n    18\t    \"correct\": true,\n    19\t    \"validation_error\": null\n    20\t  },\n    21\t  \"auxiliary\": {\n    22\t    \"aux_total_overlap_distance\": 0.0,\n    23\t    \"aux_max_overlap_distance\": 0.0,\n    24\t    \"aux_boundary_violation_distance\": 0.0,\n    25\t    \"aux_min_radius\": 0.0,\n    26\t    \"aux_num_violated_circles\": 0.0,\n    27\t    \"aux_average_radii\": 0.0851053001428563,\n    28\t    \"aux_radius_standard_deviation\": 0.027904573430154296,\n    29\t    \"aux_packed_area_ratio\": 0.6552137387225716,\n    30\t    \"aux_metric_eval_success\": 1.0,\n    31\t    \"aux_metric_error_code\": 0.0,\n    32\t    \"aux_metric_error_message_length\": 0.0,\n    33\t    \"aux_metric_error_detail_length\": 0.0,\n    34\t    \"aux_metric_non_numeric_dropped_count\": 0.0\n    35\t  },\n    36\t  \"auxiliary_descriptions\": {\n    37\t    \"aux_total_overlap_distance\": \"Total sum of penetration depths between overlapping circles.\",\n    38\t    \"aux_max_overlap_distance\": \"Maximum penetration depth between any two overlapping circles.\",\n    39\t    \"aux_boundary_violation_distance\": \"Total sum of distances a circle extends beyond the unit square boundary.\",\n    40\t    \"aux_min_radius\": \"Smallest radius among all circles, indicating potential degeneration.\",\n    41\t    \"aux_num_violated_circles\": \"Count of circles that violate either overlap or boundary constraints.\",\n    42\t    \"aux_average_radii\": \"Average radius of all circles.\",\n    43\t    \"aux_radius_standard_deviation\": \"Standard deviation of radii.\",\n    44\t    \"aux_packed_area_ratio\": \"Ratio of the total area covered by circles to the unit square area.\"\n    45\t  },\n    46\t  \"timestamp\": 1772074454.809435,\n    47\t  \"generation\": 10\n    48\t}\n"}], "role": "tool", "tool_call_id": "call_0b7cf572259f4166bc04d5489b3f__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's break this down. First off, I'm looking at the metrics from `gen_10/results/metrics.json`. The good news is, the overlap and boundary violation metrics are all zero. Great! No feasibility issues here.\n\nBut hold on, `aux_min_radius` is also zero. Hmm. That's a red flag. I know from the code that this gets calculated as the minimum radius of all the circles. If it's zero, that means at least one circle has a radius of zero. That's not ideal because it's essentially a non-existent circle. It doesn't contribute to the overall objective – maximizing the packed area.\n\nThis smells like a potential optimization \"cheat\". The algorithm might be adding these zero-radius circles to satisfy constraints like a minimum number of circles, without actually improving the packing density. It's essentially gaming the system.\n\nI need to investigate further. To see if this zero-radius situation is a recent phenomenon or a recurring issue, I need to check the metrics from previous generations. I'll need the `metrics.json` files from earlier runs. Let's start by looking at `gen_9` and `gen_7`. I'll skip `gen_8` because it looks like a failed run with zero primary score. This will let me see if this zero-radius issue is new, or if it's been present for a while, potentially indicating a problem with the optimization strategy.\n", "signature": null}], "tool_calls": [{"id": "call_d737af0b89834ae5bb663af49c64__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_9/results/metrics.json\", \"security_risk\": \"LOW\", \"summary\": \"View metrics.json for gen_9\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_9/results/metrics.json:\n     1\t{\n     2\t  \"combined_score\": 2.4742543201305223,\n     3\t  \"correct\": true,\n     4\t  \"primary\": {\n     5\t    \"combined_score\": 2.4742543201305223,\n     6\t    \"public\": {\n     7\t      \"centers_str\": \"  centers[0] = (0.0993, 0.0995)\\n  centers[1] = (0.2996, 0.0962)\\n  centers[2] = (0.5002, 0.0931)\\n  centers[3] = (0.7002, 0.0982)\\n  centers[4] = (0.9006, 0.1000)\\n  centers[5] = (0.1006, 0.3001)\\n  centers[6] = (0.3008, 0.2901)\\n  centers[7] = (0.5004, 0.2823)\\n  centers[8] = (0.7002, 0.2958)\\n  centers[9] = (0.9001, 0.3009)\\n  centers[10] = (0.0998, 0.5011)\\n  centers[11] = (0.2997, 0.4830)\\n  centers[12] = (0.4991, 0.4712)\\n  centers[13] = (0.6991, 0.4932)\\n  centers[14] = (0.8998, 0.5013)\\n  centers[15] = (0.0944, 0.7016)\\n  centers[16] = (0.2835, 0.6738)\\n  centers[17] = (0.5223, 0.6577)\\n  centers[18] = (0.7116, 0.6904)\\n  centers[19] = (0.9047, 0.7026)\\n  centers[20] = (0.0864, 0.9002)\\n  centers[21] = (0.2674, 0.9084)\\n  centers[22] = (0.4146, 0.8075)\\n  centers[23] = (0.5596, 0.9104)\\n  centers[24] = (0.7378, 0.8845)\\n  centers[25] = (0.9148, 0.9023)\",\n     8\t      \"num_circles\": 26\n     9\t    },\n    10\t    \"private\": {\n    11\t      \"reported_sum_of_radii\": 2.4742543201305223\n    12\t    },\n    13\t    \"execution_time_mean\": 0.49658707808703184,\n    14\t    \"execution_time_std\": 0.0,\n    15\t    \"num_valid_runs\": 1,\n    16\t    \"num_invalid_runs\": 0,\n    17\t    \"all_validation_errors\": [],\n    18\t    \"correct\": true,\n    19\t    \"validation_error\": null\n    20\t  },\n    21\t  \"auxiliary\": {\n    22\t    \"aux_total_overlap_distance\": 2.7755575615628914e-17,\n    23\t    \"aux_max_overlap_distance\": 2.7755575615628914e-17,\n    24\t    \"aux_boundary_violation_distance\": 0.0,\n    25\t    \"aux_min_radius\": 0.08523411119073121,\n    26\t    \"aux_num_violated_circles\": 2.0,\n    27\t    \"aux_average_radii\": 0.09516362769732778,\n    28\t    \"aux_radius_standard_deviation\": 0.004395210151437102,\n    29\t    \"aux_packed_area_ratio\": 0.7412942288959274,\n    30\t    \"aux_metric_eval_success\": 1.0,\n    31\t    \"aux_metric_error_code\": 0.0,\n    32\t    \"aux_metric_error_message_length\": 0.0,\n    33\t    \"aux_metric_error_detail_length\": 0.0,\n    34\t    \"aux_metric_non_numeric_dropped_count\": 0.0\n    35\t  },\n    36\t  \"auxiliary_descriptions\": {\n    37\t    \"aux_total_overlap_distance\": \"Total sum of penetration depths between overlapping circles.\",\n    38\t    \"aux_max_overlap_distance\": \"Maximum penetration depth between any two overlapping circles.\",\n    39\t    \"aux_boundary_violation_distance\": \"Total sum of distances a circle extends beyond the unit square boundary.\",\n    40\t    \"aux_min_radius\": \"Smallest radius among all circles, indicating potential degeneration.\",\n    41\t    \"aux_num_violated_circles\": \"Count of circles that violate either overlap or boundary constraints.\",\n    42\t    \"aux_average_radii\": \"Average radius of all circles.\",\n    43\t    \"aux_radius_standard_deviation\": \"Standard deviation of radii.\",\n    44\t    \"aux_packed_area_ratio\": \"Ratio of the total area covered by circles to the unit square area.\"\n    45\t  },\n    46\t  \"timestamp\": 1772074340.404484,\n    47\t  \"generation\": 9\n    48\t}\n"}], "role": "tool", "tool_call_id": "call_d737af0b89834ae5bb663af49c64__thought__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", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "7bWfafDeBor0694P6Y_ImAo", "created": 1772074477, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "CpkEAY89a19NHE1d5uIvoCiHw/vlef6qm7CsicbhLh/FaOZ1mfpEjiWEeIroQZvInyGaSpsfNAqxjaqb+n+7N6in/uOhAcEKIfho3ayuCVq1sdFOZKUx95F2oKz1uFRKObPIJbXYZumcDaGzIvxIeTQPeMGiXkmzRpb+dlsYvGO625ueN8bJWILM384Tm27vYh6bLv3fZPKYyq7UCMifsSG8hSMEUpNspF5NPLJ7KKXs+n0HDIKUD3i3kAbt2pNvwPl1d4Pn0031GASaTnbHX+gJwPF0Ur7TXWk53bqkZ/0wSlbfiXUt5H9/b6KJvmnVzzDFiQMJ95cN/33WL8FRDDONYehFh3J4xuFcl4cADyy9bK+3ckK34KGtBfdN+H5Bie4UUT8HF+krcXFhP0vtjRhdb7HIHoiDR68YAe+OjQROThzmAptQ2vT2ad005vV7TWAxazrvG66qmTjnByrpuTS6WGEi9MH8rT31MhiEKhzJqGELwmeAUFJn3tSY5XZFmKBa6QJO5rhjwojrTv1Vx9tkm2XzXe8GrKy/AyljEQmJXcJMpVfgF6d7eokqc9ekBdYyrKIAbCaD6o6XlWnhPWfFxH9T6D6sNVv1BdNNYKZyWxkIteNV28h23TCM6T9hEYb03jn0M7Og1yweuBWjrluG73pTJGtcdWHBF1x57AGxUrMgv6k6h+x5hEdPn13Kpcw6a0D72gWqbqrB"}, "function": {"arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_7/results/metrics.json\", \"summary\": \"View metrics.json for gen_7\", \"command\": \"view\"}", "name": "file_editor"}, "id": "call_09efcde944a6497bad2faf4efee4__thought__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", "type": "function"}], "images": [], "reasoning_content": "Okay, let's see what the latest generation, `gen_9`, is doing. Looking at the data... Hmm. The overlap metrics are essentially zero: `aux_total_overlap_distance` and `aux_max_overlap_distance` are both at 2.77e-17 – practically nothing. Boundary violations, too, are coming in at zero. That's good.\n\nThe minimum radius, `aux_min_radius`, is at 0.0852, which is expected. The number of violated circles, `aux_num_violated_circles`, however, is showing 2.0. That's a little peculiar. With zero overlap and zero boundary violation distance, this seems like a very slight issue, likely a consequence of some numerical precision or rounding issue. I'll flag this to monitor in subsequent generations, but I'm not overly concerned at this stage.\n\nThe average radii, `aux_average_radii`, is 0.0951, and the standard deviation, `aux_radius_standard_deviation`, is a tight 0.0043. Consistent. The `aux_packed_area_ratio` is at 0.7412, indicating reasonable packing efficiency.\n\nAlright, that gives me a good baseline for `gen_9`. Now, I should compare this to some earlier generations to track progress. Let's see what `gen_7` looked like.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's see what the latest generation, `gen_9`, is doing. Looking at the data... Hmm. The overlap metrics are essentially zero: `aux_total_overlap_distance` and `aux_max_overlap_distance` are both at 2.77e-17 – practically nothing. Boundary violations, too, are coming in at zero. That's good.\n\nThe minimum radius, `aux_min_radius`, is at 0.0852, which is expected. The number of violated circles, `aux_num_violated_circles`, however, is showing 2.0. That's a little peculiar. With zero overlap and zero boundary violation distance, this seems like a very slight issue, likely a consequence of some numerical precision or rounding issue. I'll flag this to monitor in subsequent generations, but I'm not overly concerned at this stage.\n\nThe average radii, `aux_average_radii`, is 0.0951, and the standard deviation, `aux_radius_standard_deviation`, is a tight 0.0043. Consistent. The `aux_packed_area_ratio` is at 0.7412, indicating reasonable packing efficiency.\n\nAlright, that gives me a good baseline for `gen_9`. Now, I should compare this to some earlier generations to track progress. Let's see what `gen_7` looked like.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 286, "prompt_tokens": 13327, "total_tokens": 13613, "completion_tokens_details": {"reasoning_tokens": 200, "text_tokens": 86}, "prompt_tokens_details": {"text_tokens": 13327}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0047131000000000004, "timestamp": 1772074481.5247285, "latency_sec": 4.428136587142944, "usage_summary": {"prompt_tokens": 13327, "completion_tokens": 286, "reasoning_tokens": 200, "cache_read_tokens": 0}}

tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772074493.979-cd5a.json

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+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 10 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_10/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_10/main.py\n- Current metrics: $RESULTS_DIR/gen_10/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.2127\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 10 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221 | gen_5: 2.4535 | gen_6: 2.5414 | gen_7: 1.9800 | gen_8: 0.0000 | gen_9: 2.4743\n- Net change (gen_0 -> gen_9): +1.5145"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the deal. My task is clear: I need to scrutinize the progress, update those auxiliary metrics we've been tracking, and, of course, keep a detailed log of everything I do. That's the drill.\n\nLet's dive into the data. The current score, 2.2127, is what we're working with. Now, looking back at the scores from previous generations…hmm. The initial score was 0.9598, and it's generally trended upwards, peaking at 2.5414 in gen_6. The really concerning thing here is that gen_8 score of 0.0000. That screams \"failure\" to me. It's a significant outlier, so we *definitely* need to investigate what went wrong there. While the overall trend has improved from the beginning, the up-and-down nature of the scores raises a red flag. It looks like the optimization process might be getting trapped in local optima or maybe just thrashing around the search space. \n\nConsidering that the current score is lower than both gen_9 and gen_6… we're not exactly moving in the right direction right now.\n\nOkay, let's get down to specifics. The first step is to check the `eval_agent_memory` directory. I'll need to see the `auxiliary_metrics.py` file to understand the current auxiliary metric calculations and the `EVAL_AGENTS.md` file to evaluate the performance of each agent. This is where we'll dig into the data and analyze exactly what's happening.\n", "signature": null}], "tool_calls": [{"id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/\", \"summary\": \"List contents of eval_agent_memory directory\"}"}}]}, {"content": [{"type": "text", "text": "total 36\n-rw------- 1 tengxiao tengxiao 2946 Feb 26 02:44 EVAL_AGENTS.md\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:46 __pycache__\n-rw-r--r-- 1 tengxiao tengxiao 7423 Feb 26 02:54 agent_behavior.jsonl\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 agent_runs\n-rw------- 1 tengxiao tengxiao 4157 Feb 26 02:44 auxiliary_metrics.py\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 llm_completions\n-rw-r--r-- 1 tengxiao tengxiao 2898 Feb 26 02:54 service_state.json\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, time to get down to brass tacks. I need to get a handle on where we are, what the current benchmarks look like, and what they're actually measuring. My next step is absolutely crucial: I need to dig into the `EVAL_AGENTS.md` file. That's where all the relevant details regarding the evaluation protocols, the chosen agents, and the specific test conditions will be laid out. Understanding that document is non-negotiable for me. After that, I'll move on to `auxiliary_metrics.py`. That's where the rubber meets the road on the performance side. I need to dissect that file to understand how they are *actually* computing and interpreting these auxiliary metrics. Gotta see the nitty-gritty details, the implementation choices, the specific functions being used. Without a solid grip on these two resources, any further work would be just guesswork, pure noise. This is the foundation - got to get this right.\n", "signature": null}], "tool_calls": [{"id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"summary\": \"View EVAL_AGENTS.md\", \"command\": \"view\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t- `aux_average_radii`: Average radius of all circles.\n     8\t- `aux_radius_standard_deviation`: Standard deviation of radii.\n     9\t- `aux_packed_area_ratio`: Ratio of the total area covered by circles to the unit square area.\n    10\t\n    11\t## Generation 4\n    12\tStage: OPTIMIZATION\n    13\tPrimary score: 1.6221\n    14\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    15\tStagnant metrics: none\n    16\tActions:\n    17\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    18\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    19\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    20\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    21\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    22\tNotes:\n    23\t- Initializing auxiliary metrics for the first time.\n    24\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    25\t\n    26\t## Generation 5\n    27\tStage: OPTIMIZATION\n    28\tPrimary score: 2.4535\n    29\tHypothesis: The previous auxiliary metrics were failing due to a bug (radii not loaded), leading to all 0.0 values. Fixing this bug will enable proper constraint violation diagnosis. Additionally, new metrics are introduced to assess the quality of valid packings, focusing on average circle size, radius distribution, and overall space utilization to guide further optimization.\n    30\tStagnant metrics: aux_total_overlap_distance, aux_max_overlap_distance, aux_boundary_violation_distance, aux_min_radius, aux_num_violated_circles (all were 0.0 due to a bug in previous generation)\n    31\tActions:\n    32\t- REPLACE `auxiliary_metrics.py` (Fixed `radii` loading bug and added `aux_average_radii`, `aux_radius_standard_deviation`, `aux_packed_area_ratio` to improve diagnostic capabilities).\n    33\t- ADD `aux_average_radii` (To track the average size of circles, directly related to the primary score).\n    34\t- ADD `aux_radius_standard_deviation` (To understand the uniformity of circle sizes and identify imbalances).\n    35\t- ADD `aux_packed_area_ratio` (To measure the overall density and efficiency of the packing solution).\n    36\tNotes:\n    37\t- The primary score has improved from gen_4, suggesting the system is recovering.\n    38\t- The previous auxiliary metrics were not providing useful signal due to an implementation error.\n    39\t\n    40\t\n"}], "role": "tool", "tool_call_id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as an expert:\n\nRight, so the `EVAL_AGENTS.md` file clued me in - a nasty bug bit us in `Generation 5`. Damn, looks like the `radii` weren't getting loaded properly. That explains why all the auxiliary metrics went flat to `0.0`.  That's a classic sign of a missing or misconfigured data dependency.\n\nGood, the fix was to swap out the `auxiliary_metrics.py` file. But, more importantly, they've added some new metrics to that file. We've got `aux_average_radii`, `aux_radius_standard_deviation`, and `aux_packed_area_ratio` now. Sounds like we're aiming for a more in-depth analysis of the radius distributions and packing density.\n\nNow, to really understand what's going on, I need to see the current implementation of `auxiliary_metrics.py`. Time to dive in and see how they're calculating these things. I need to check the logic, data dependencies, and potential edge cases of these new metrics, making sure they're correctly implemented and don't introduce any new biases. Let's see what we've got.\n", "signature": null}], "tool_calls": [{"id": "call_c064bfa737b345c0a69fe558a5f5__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py\", \"command\": \"view\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"] # Fix: Load radii\n    17\t        num_circles = centers.shape[0]\n    18\t        \n    19\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    20\t        total_overlap_distance = 0.0\n    21\t        max_overlap_distance = 0.0\n    22\t\n    23\t        for i in range(num_circles):\n    24\t            for j in range(i + 1, num_circles):\n    25\t                dist = np.linalg.norm(centers[i] - centers[j])\n    26\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    27\t                if dist < min_dist_for_no_overlap:\n    28\t                    overlap = min_dist_for_no_overlap - dist\n    29\t                    total_overlap_distance += overlap\n    30\t                    if overlap > max_overlap_distance:\n    31\t                        max_overlap_distance = overlap\n    32\t\n    33\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    34\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    35\t        \n    36\t        # --- aux_boundary_violation_distance ---\n    37\t        boundary_violation_distance = 0.0\n    38\t        \n    39\t        for i in range(num_circles):\n    40\t            x, y = centers[i]\n    41\t            r = radii[i]\n    42\t\n    43\t            # Check x-bounds\n    44\t            if x - r < 0:\n    45\t                boundary_violation_distance += abs(x - r)\n    46\t            if x + r > 1:\n    47\t                boundary_violation_distance += abs(x + r - 1)\n    48\t            \n    49\t            # Check y-bounds\n    50\t            if y - r < 0:\n    51\t                boundary_violation_distance += abs(y - r)\n    52\t            if y + r > 1:\n    53\t                boundary_violation_distance += abs(y + r - 1)\n    54\t                \n    55\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    56\t\n    57\t        # --- aux_min_radius ---\n    58\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    59\t        \n    60\t        # --- aux_num_violated_circles ---\n    61\t        num_violated_circles = 0\n    62\t        violated_circles_indices = set()\n    63\t\n    64\t        # Check for overlaps (re-do from above to get specific circles)\n    65\t        for i in range(num_circles):\n    66\t            for j in range(i + 1, num_circles):\n    67\t                dist = np.linalg.norm(centers[i] - centers[j])\n    68\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    69\t                if dist < min_dist_for_no_overlap:\n    70\t                    violated_circles_indices.add(i)\n    71\t                    violated_circles_indices.add(j)\n    72\t\n    73\t        # Check for boundary violations (re-do from above to get specific circles)\n    74\t        for i in range(num_circles):\n    75\t            x, y = centers[i]\n    76\t            r = radii[i]\n    77\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    78\t                violated_circles_indices.add(i)\n    79\t                \n    80\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    81\t        \n    82\t        # --- New metrics ---\n    83\t        # aux_average_radii\n    84\t        metrics[\"aux_average_radii\"] = float(np.mean(radii)) if radii.size > 0 else 0.0\n    85\t\n    86\t        # aux_radius_standard_deviation\n    87\t        metrics[\"aux_radius_standard_deviation\"] = float(np.std(radii)) if radii.size > 0 else 0.0\n    88\t        \n    89\t        # aux_packed_area_ratio\n    90\t        metrics[\"aux_packed_area_ratio\"] = float(np.sum(np.pi * radii**2))\n    91\t\n    92\t\n    93\t    except Exception as e:\n    94\t        print(f\"Error in evaluate_aux: {e}\")\n    95\t        # Return default 0.0 for all metrics in case of failure\n    96\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    97\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    98\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    99\t        metrics[\"aux_min_radius\"] = 0.0\n   100\t        metrics[\"aux_num_violated_circles\"] = 0\n   101\t        metrics[\"aux_average_radii\"] = 0.0\n   102\t        metrics[\"aux_radius_standard_deviation\"] = 0.0\n   103\t        metrics[\"aux_packed_area_ratio\"] = 0.0\n   104\t        \n   105\t    return metrics\n   106\t\n   107\t\n"}], "role": "tool", "tool_call_id": "call_c064bfa737b345c0a69fe558a5f5__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's get down to brass tacks. That `auxiliary_metrics.py` file is a solid foundation. I like the inclusion of those overlap, boundary violation, radii distribution, and packed area metrics – they give a good, holistic view of the system's performance. And the `try-except` blocks with those default `0.0` values are a crucial safety net; prevents the whole thing from crashing if something goes awry during calculation. Smart.\n\nNow, the real work begins. I need to dive into `metrics.json` for generation 10. That's where the rubber meets the road. I'll be scrutinizing the values for those key metrics. My goal is to spot any stagnation. Are any of these performance indicators plateauing? That will be the key. This analysis is crucial for formulating my hypothesis for the next phase. Where are the weak points? What improvements are we seeing, and what's holding us back? Let's see what the data reveals.\n", "signature": null}], "tool_calls": [{"id": "call_0b7cf572259f4166bc04d5489b3f__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/results/metrics.json\", \"summary\": \"View metrics.json for gen_10\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/results/metrics.json:\n     1\t{\n     2\t  \"combined_score\": 2.2127378037142638,\n     3\t  \"correct\": true,\n     4\t  \"primary\": {\n     5\t    \"combined_score\": 2.2127378037142638,\n     6\t    \"public\": {\n     7\t      \"centers_str\": \"  centers[0] = (0.0860, 0.0915)\\n  centers[1] = (0.2918, 0.0922)\\n  centers[2] = (0.5000, 0.0864)\\n  centers[3] = (0.7072, 0.0845)\\n  centers[4] = (0.9190, 0.0968)\\n  centers[5] = (0.0902, 0.3050)\\n  centers[6] = (0.2967, 0.2972)\\n  centers[7] = (0.5025, 0.2914)\\n  centers[8] = (0.7075, 0.2895)\\n  centers[9] = (0.9138, 0.3033)\\n  centers[10] = (0.0000, 0.4997)\\n  centers[11] = (0.2043, 0.5146)\\n  centers[12] = (0.4047, 0.4715)\\n  centers[13] = (0.6015, 0.5286)\\n  centers[14] = (0.7987, 0.4730)\\n  centers[15] = (1.0000, 0.5115)\\n  centers[16] = (0.0877, 0.6980)\\n  centers[17] = (0.2930, 0.6994)\\n  centers[18] = (0.4981, 0.7056)\\n  centers[19] = (0.7108, 0.7025)\\n  centers[20] = (0.9162, 0.6993)\\n  centers[21] = (0.0884, 0.9118)\\n  centers[22] = (0.2962, 0.9050)\\n  centers[23] = (0.5048, 0.9113)\\n  centers[24] = (0.7136, 0.9082)\\n  centers[25] = (0.9186, 0.9065)\",\n     8\t      \"num_circles\": 26\n     9\t    },\n    10\t    \"private\": {\n    11\t      \"reported_sum_of_radii\": 2.2127378037142638\n    12\t    },\n    13\t    \"execution_time_mean\": 4.81304601021111,\n    14\t    \"execution_time_std\": 0.0,\n    15\t    \"num_valid_runs\": 1,\n    16\t    \"num_invalid_runs\": 0,\n    17\t    \"all_validation_errors\": [],\n    18\t    \"correct\": true,\n    19\t    \"validation_error\": null\n    20\t  },\n    21\t  \"auxiliary\": {\n    22\t    \"aux_total_overlap_distance\": 0.0,\n    23\t    \"aux_max_overlap_distance\": 0.0,\n    24\t    \"aux_boundary_violation_distance\": 0.0,\n    25\t    \"aux_min_radius\": 0.0,\n    26\t    \"aux_num_violated_circles\": 0.0,\n    27\t    \"aux_average_radii\": 0.0851053001428563,\n    28\t    \"aux_radius_standard_deviation\": 0.027904573430154296,\n    29\t    \"aux_packed_area_ratio\": 0.6552137387225716,\n    30\t    \"aux_metric_eval_success\": 1.0,\n    31\t    \"aux_metric_error_code\": 0.0,\n    32\t    \"aux_metric_error_message_length\": 0.0,\n    33\t    \"aux_metric_error_detail_length\": 0.0,\n    34\t    \"aux_metric_non_numeric_dropped_count\": 0.0\n    35\t  },\n    36\t  \"auxiliary_descriptions\": {\n    37\t    \"aux_total_overlap_distance\": \"Total sum of penetration depths between overlapping circles.\",\n    38\t    \"aux_max_overlap_distance\": \"Maximum penetration depth between any two overlapping circles.\",\n    39\t    \"aux_boundary_violation_distance\": \"Total sum of distances a circle extends beyond the unit square boundary.\",\n    40\t    \"aux_min_radius\": \"Smallest radius among all circles, indicating potential degeneration.\",\n    41\t    \"aux_num_violated_circles\": \"Count of circles that violate either overlap or boundary constraints.\",\n    42\t    \"aux_average_radii\": \"Average radius of all circles.\",\n    43\t    \"aux_radius_standard_deviation\": \"Standard deviation of radii.\",\n    44\t    \"aux_packed_area_ratio\": \"Ratio of the total area covered by circles to the unit square area.\"\n    45\t  },\n    46\t  \"timestamp\": 1772074454.809435,\n    47\t  \"generation\": 10\n    48\t}\n"}], "role": "tool", "tool_call_id": "call_0b7cf572259f4166bc04d5489b3f__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's break this down. First off, I'm looking at the metrics from `gen_10/results/metrics.json`. The good news is, the overlap and boundary violation metrics are all zero. Great! No feasibility issues here.\n\nBut hold on, `aux_min_radius` is also zero. Hmm. That's a red flag. I know from the code that this gets calculated as the minimum radius of all the circles. If it's zero, that means at least one circle has a radius of zero. That's not ideal because it's essentially a non-existent circle. It doesn't contribute to the overall objective – maximizing the packed area.\n\nThis smells like a potential optimization \"cheat\". The algorithm might be adding these zero-radius circles to satisfy constraints like a minimum number of circles, without actually improving the packing density. It's essentially gaming the system.\n\nI need to investigate further. To see if this zero-radius situation is a recent phenomenon or a recurring issue, I need to check the metrics from previous generations. I'll need the `metrics.json` files from earlier runs. Let's start by looking at `gen_9` and `gen_7`. I'll skip `gen_8` because it looks like a failed run with zero primary score. This will let me see if this zero-radius issue is new, or if it's been present for a while, potentially indicating a problem with the optimization strategy.\n", "signature": null}], "tool_calls": [{"id": "call_d737af0b89834ae5bb663af49c64__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_9/results/metrics.json\", \"security_risk\": \"LOW\", \"summary\": \"View metrics.json for gen_9\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_9/results/metrics.json:\n     1\t{\n     2\t  \"combined_score\": 2.4742543201305223,\n     3\t  \"correct\": true,\n     4\t  \"primary\": {\n     5\t    \"combined_score\": 2.4742543201305223,\n     6\t    \"public\": {\n     7\t      \"centers_str\": \"  centers[0] = (0.0993, 0.0995)\\n  centers[1] = (0.2996, 0.0962)\\n  centers[2] = (0.5002, 0.0931)\\n  centers[3] = (0.7002, 0.0982)\\n  centers[4] = (0.9006, 0.1000)\\n  centers[5] = (0.1006, 0.3001)\\n  centers[6] = (0.3008, 0.2901)\\n  centers[7] = (0.5004, 0.2823)\\n  centers[8] = (0.7002, 0.2958)\\n  centers[9] = (0.9001, 0.3009)\\n  centers[10] = (0.0998, 0.5011)\\n  centers[11] = (0.2997, 0.4830)\\n  centers[12] = (0.4991, 0.4712)\\n  centers[13] = (0.6991, 0.4932)\\n  centers[14] = (0.8998, 0.5013)\\n  centers[15] = (0.0944, 0.7016)\\n  centers[16] = (0.2835, 0.6738)\\n  centers[17] = (0.5223, 0.6577)\\n  centers[18] = (0.7116, 0.6904)\\n  centers[19] = (0.9047, 0.7026)\\n  centers[20] = (0.0864, 0.9002)\\n  centers[21] = (0.2674, 0.9084)\\n  centers[22] = (0.4146, 0.8075)\\n  centers[23] = (0.5596, 0.9104)\\n  centers[24] = (0.7378, 0.8845)\\n  centers[25] = (0.9148, 0.9023)\",\n     8\t      \"num_circles\": 26\n     9\t    },\n    10\t    \"private\": {\n    11\t      \"reported_sum_of_radii\": 2.4742543201305223\n    12\t    },\n    13\t    \"execution_time_mean\": 0.49658707808703184,\n    14\t    \"execution_time_std\": 0.0,\n    15\t    \"num_valid_runs\": 1,\n    16\t    \"num_invalid_runs\": 0,\n    17\t    \"all_validation_errors\": [],\n    18\t    \"correct\": true,\n    19\t    \"validation_error\": null\n    20\t  },\n    21\t  \"auxiliary\": {\n    22\t    \"aux_total_overlap_distance\": 2.7755575615628914e-17,\n    23\t    \"aux_max_overlap_distance\": 2.7755575615628914e-17,\n    24\t    \"aux_boundary_violation_distance\": 0.0,\n    25\t    \"aux_min_radius\": 0.08523411119073121,\n    26\t    \"aux_num_violated_circles\": 2.0,\n    27\t    \"aux_average_radii\": 0.09516362769732778,\n    28\t    \"aux_radius_standard_deviation\": 0.004395210151437102,\n    29\t    \"aux_packed_area_ratio\": 0.7412942288959274,\n    30\t    \"aux_metric_eval_success\": 1.0,\n    31\t    \"aux_metric_error_code\": 0.0,\n    32\t    \"aux_metric_error_message_length\": 0.0,\n    33\t    \"aux_metric_error_detail_length\": 0.0,\n    34\t    \"aux_metric_non_numeric_dropped_count\": 0.0\n    35\t  },\n    36\t  \"auxiliary_descriptions\": {\n    37\t    \"aux_total_overlap_distance\": \"Total sum of penetration depths between overlapping circles.\",\n    38\t    \"aux_max_overlap_distance\": \"Maximum penetration depth between any two overlapping circles.\",\n    39\t    \"aux_boundary_violation_distance\": \"Total sum of distances a circle extends beyond the unit square boundary.\",\n    40\t    \"aux_min_radius\": \"Smallest radius among all circles, indicating potential degeneration.\",\n    41\t    \"aux_num_violated_circles\": \"Count of circles that violate either overlap or boundary constraints.\",\n    42\t    \"aux_average_radii\": \"Average radius of all circles.\",\n    43\t    \"aux_radius_standard_deviation\": \"Standard deviation of radii.\",\n    44\t    \"aux_packed_area_ratio\": \"Ratio of the total area covered by circles to the unit square area.\"\n    45\t  },\n    46\t  \"timestamp\": 1772074340.404484,\n    47\t  \"generation\": 9\n    48\t}\n"}], "role": "tool", "tool_call_id": "call_d737af0b89834ae5bb663af49c64__thought__CsYLAY89a18NzcXdfRNpO5auxiJA55PwV2GsZYeNkke5Ro2gmo849HDxU/35X+nyyI+SrYv/NNkZ5ecE9COj22bD3QixvPxRtIr2d3ClQocX7tcVtgAVl/qF68Qh0WisUMH9NgwdQm7IVDiaQ0g7OlhbpJDi1DMUWpi/5AOGbxmJVlk5v4mec1qvKFA5U1m+cK/OY8L4K1gqwY2SHArgv/8Sdwag6iQ85jFNW9A6Y54aQ07mSojgcCVOVp6C2W4K97yvlQZjg1DX9uz+ubwaqko/EroFyrW6NXpAZr/48sIcGgucaMpDp9n3dO/KRCddq8hBrQapRbUxpXelP2crEpZ/wViYEG/CI1qKJHYRuqKSN0IUoAlF7AlEtr50kicO3lS0nswuyF2OHF9uLoGIEc92GGYUuQf3ruqXIwitfPnLlbnFboxhbMPstwGl81anW+KEVY65wDtyWJcxdfvnMevMB8DteC8Wusoucl/zk6FE1iO0HeE6V7tU58V60ZPji5BDluhhNNY2HXF8iJn+7LNUXw07TF9kNU8ghwS3QLw1gfLUKfXqT/3y9mLRMMuy0S/YCD7fLpwleLuw2iixqk24r80yLX34qN7GVSPTmOMMw9WdyMefC5D9eaZMVZpUTTjdBOdkOmmse7BNDp69QwuSKZpTeLcMa+MAYWArma3dFjIUYEi6cGYC60uXxRavbAnYoCbjLCLDdwYXp2RrZAIIeJzXxyAA6D8Yg1FY7DSbSz3Ls633Lp9EVIgF85o3V2LpMoPOs73h9Zx82g1SiJ/8yj1LJ8NBmlntAJOZ53e5G1njzZ3L5Mr+X3xLLFBV3HcdVkgiY+1lwQNaQKDt6wr/lnax3d4FN+zSFPQW9TGoU7xo0oIL2EZaSYi4rilzVEm6FviQRdVFdAoqyY651dh48MsFXDKWfRbf8ESPydgcDY5Un8/7RtJyRgJwJfNasOBLWgehI3lsZbW2g633y1CUZEDajoZgn5sBhTi6uX1BJt9JE6GMYtWos8vdL4YmRaEvMcNC4G7cywCURUnzzOw1+INRsckEda2Xbij6CY3T1fKSmfStFT/bFXNDRaAicV4mC8ZJoLvVw5txec373SUY0p3LTuo41KrFH/+2iPKcSYa56dQKpavYupKWJG2ScGDfH8E6SlQkqq/mcmAZ8GMMswSoHw7Qh3ycET4JTA1yF3mrInYaO6SXx3oIU8DhLuWxMYA9fUBW9qnlZPCKgcA2CU/EvEyN5Q9BIjV0qFiIPv7UhsEXZv53LzOGUuTMk1/iBzmVPP7x6cisQUlqNNfXuKI5Y2Rz4XmjX14sYzkVdk5JyTcTpcqNQp+CH6VLpQOAURFtWqx/YwAKgCN08q3ZXPovN2tf0McTU9TQv+XLv9vmLWybRZc9jH3xPD3uAr1NUr1LEFBkL5/Fii1YXROQhzy9m0zf8e4EbcFF/i9UX9Co/Deddq4bx5cM6kriBqPL7mN2nbA80KPvvrnlSFF2Kq83L76YrxEttW+LIO3oqVD3yFvbKJKL//fWZbcGtECcY/YZVve0AGmoeNBK3ff1CtIwgnoqZM3h6nVbw4LCP5VRy+pQY8eUBoogO+4oxmnLyloeU9+mddo1ABbian5VfPAGSpzMO1U3DteB71W3a53No4zqiNnx1oNKFtJ958Qnx8W9V80YWDs2FNlyVlY9KClOWYyJ0tWbrsYimB7Fj3SlHqSk103ztgwsqubi3itKtDFtmF4aEdpFiPyj7Ewbquf0NsXugSJ94H5U+yZ0CWZKKxhiWGTP6olxllMa3Zd+MS0W6ALyyndmezP7I2QOTJyTwxLhHUoWkHMiJUlmyjemz1hvxpQy8AYsi6RTl1jhxbrjoSQiemiD/fd8DNUVtkWt61oIQsyIdevcnPhdA9da3MC1txsu3AQ5YFAXawapFVS076J/WfH0rWypNJcMaREEVbjE0CJ3aUuCdFpqVwJ8pFIRGJA=", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's see what the latest generation, `gen_9`, is doing. Looking at the data... Hmm. The overlap metrics are essentially zero: `aux_total_overlap_distance` and `aux_max_overlap_distance` are both at 2.77e-17 – practically nothing. Boundary violations, too, are coming in at zero. That's good.\n\nThe minimum radius, `aux_min_radius`, is at 0.0852, which is expected. The number of violated circles, `aux_num_violated_circles`, however, is showing 2.0. That's a little peculiar. With zero overlap and zero boundary violation distance, this seems like a very slight issue, likely a consequence of some numerical precision or rounding issue. I'll flag this to monitor in subsequent generations, but I'm not overly concerned at this stage.\n\nThe average radii, `aux_average_radii`, is 0.0951, and the standard deviation, `aux_radius_standard_deviation`, is a tight 0.0043. Consistent. The `aux_packed_area_ratio` is at 0.7412, indicating reasonable packing efficiency.\n\nAlright, that gives me a good baseline for `gen_9`. Now, I should compare this to some earlier generations to track progress. Let's see what `gen_7` looked like.\n", "signature": null}], "tool_calls": [{"id": "call_09efcde944a6497bad2faf4efee4__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_7/results/metrics.json\", \"summary\": \"View metrics.json for gen_7\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_7/results/metrics.json:\n     1\t{\n     2\t  \"combined_score\": 1.9800352414974887,\n     3\t  \"correct\": true,\n     4\t  \"primary\": {\n     5\t    \"combined_score\": 1.9800352414974887,\n     6\t    \"public\": {\n     7\t      \"centers_str\": \"  centers[0] = (0.0936, 0.0959)\\n  centers[1] = (0.2976, 0.0973)\\n  centers[2] = (0.5005, 0.0949)\\n  centers[3] = (0.7028, 0.0957)\\n  centers[4] = (0.9049, 0.0971)\\n  centers[5] = (0.0941, 0.2985)\\n  centers[6] = (0.2963, 0.2992)\\n  centers[7] = (0.4996, 0.2976)\\n  centers[8] = (0.7019, 0.2983)\\n  centers[9] = (0.9041, 0.2993)\\n  centers[10] = (0.0941, 0.5007)\\n  centers[11] = (0.2968, 0.5011)\\n  centers[12] = (0.4994, 0.4995)\\n  centers[13] = (0.7017, 0.5005)\\n  centers[14] = (0.9046, 0.5027)\\n  centers[15] = (0.0958, 0.7044)\\n  centers[16] = (0.2981, 0.7030)\\n  centers[17] = (0.5009, 0.7024)\\n  centers[18] = (0.7029, 0.7028)\\n  centers[19] = (0.9051, 0.7048)\\n  centers[20] = (0.0000, 0.8986)\\n  centers[21] = (0.2002, 0.8974)\\n  centers[22] = (0.4002, 0.9084)\\n  centers[23] = (0.6000, 0.8970)\\n  centers[24] = (0.8000, 0.9053)\\n  centers[25] = (1.0000, 0.8977)\",\n     8\t      \"num_circles\": 26\n     9\t    },\n    10\t    \"private\": {\n    11\t      \"reported_sum_of_radii\": 1.9800352414974887\n    12\t    },\n    13\t    \"execution_time_mean\": 2.5255804620683193,\n    14\t    \"execution_time_std\": 0.0,\n    15\t    \"num_valid_runs\": 1,\n    16\t    \"num_invalid_runs\": 0,\n    17\t    \"all_validation_errors\": [],\n    18\t    \"correct\": true,\n    19\t    \"validation_error\": null\n    20\t  },\n    21\t  \"auxiliary\": {\n    22\t    \"aux_total_overlap_distance\": 5.551115123125783e-17,\n    23\t    \"aux_max_overlap_distance\": 5.551115123125783e-17,\n    24\t    \"aux_boundary_violation_distance\": 0.0,\n    25\t    \"aux_min_radius\": 0.0,\n    26\t    \"aux_num_violated_circles\": 2.0,\n    27\t    \"aux_average_radii\": 0.07615520159605725,\n    28\t    \"aux_radius_standard_deviation\": 0.04748917984078609,\n    29\t    \"aux_packed_area_ratio\": 0.6579304298243859,\n    30\t    \"aux_metric_eval_success\": 1.0,\n    31\t    \"aux_metric_error_code\": 0.0,\n    32\t    \"aux_metric_error_message_length\": 0.0,\n    33\t    \"aux_metric_error_detail_length\": 0.0,\n    34\t    \"aux_metric_non_numeric_dropped_count\": 0.0\n    35\t  },\n    36\t  \"auxiliary_descriptions\": {\n    37\t    \"aux_total_overlap_distance\": \"Total sum of penetration depths between overlapping circles.\",\n    38\t    \"aux_max_overlap_distance\": \"Maximum penetration depth between any two overlapping circles.\",\n    39\t    \"aux_boundary_violation_distance\": \"Total sum of distances a circle extends beyond the unit square boundary.\",\n    40\t    \"aux_min_radius\": \"Smallest radius among all circles, indicating potential degeneration.\",\n    41\t    \"aux_num_violated_circles\": \"Count of circles that violate either overlap or boundary constraints.\",\n    42\t    \"aux_average_radii\": \"Average radius of all circles.\",\n    43\t    \"aux_radius_standard_deviation\": \"Standard deviation of radii.\",\n    44\t    \"aux_packed_area_ratio\": \"Ratio of the total area covered by circles to the unit square area.\"\n    45\t  },\n    46\t  \"timestamp\": 1772074085.0100996,\n    47\t  \"generation\": 7\n    48\t}\n"}], "role": "tool", "tool_call_id": "call_09efcde944a6497bad2faf4efee4__thought__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", "name": "file_editor"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "8bWfaa_QI_HE694P3OSx-A0", "created": 1772074481, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": "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"}, "function": {"arguments": "{\"command\": \"python3 -c \\\"import numpy as np; data = np.load('/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/extra.npz'); radii = data['radii']; print(radii)\\\"\", \"summary\": \"Check radii values in gen_10/extra.npz\", \"security_risk\": \"LOW\"}", "name": "terminal"}, "id": "call_cf7e4028b62e45369ba3d2e74c4b__thought__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", "type": "function"}], "images": [], "reasoning_content": "Okay, let's break this down. Analyzing `gen_7`'s metrics, things seem mostly okay, with the overlaps and boundary violations effectively at zero, though there were two violations. The radii have some spread, and the packing ratio is respectable.\n\nNow, looking at `aux_min_radius` across generations, it's telling a story. It was zero in `gen_7`, then non-zero in `gen_9`, and *back* to zero in `gen_10`. This is the key. The primary score dropped in `gen_10`, and now I see the optimizer possibly introducing circles with zero radius again, which means they are not contributing to the sum of radii, which is what we want to maximize. The fact that the violation metrics are still zero suggests either a very minor violation due to floating-point precision, or simply that the constraint hasn't been re-violated.\n\nSo, the optimizer is perhaps discarding circles by making their radius infinitesimal, so I need to understand that. To address this, I'm going to add a new metric: `aux_num_tiny_radii_circles`. This will count the number of circles whose radius is below a tiny threshold (1e-6, I think). This way I'll understand better if the optimizer is \"discarding\" circles, effectively removing them from the sum of radii by shrinking them to effectively zero. This helps identify the components that are not contributing.\n\nI considered modifying `aux_min_radius`, but the current 0.0 value already gives that signal, so the new metric will give a clearer signal.\n\nI need to confirm the radii data from `main.py` in `gen_10` to see if there are zero-radius circles, too.\n\nMoving on to a larger scope, I've identified some stagnant metrics. The overlap and boundary violation distances are always zero once feasibility is achieved. Those are not giving any \"gradient\" signal. So I'll replace them with new, more insightful metrics. I'm going to introduce:\n\n*   **`aux_average_distance_to_boundary`**: To measure how close circles are to the boundaries. A proxy for \"Constraint Tension\".\n*   **`aux_average_inter_circle_distance_ratio`**: To measure how tightly circles are packed without overlapping. Another proxy for \"Constraint Tension\" and \"Structural Sensitivity\".\n*   And finally, the `aux_num_tiny_radii_circles` to give the \"Component Contribution\" signal, as mentioned before.\n\nSo, I'll be replacing those three stagnant metrics with three new ones to get a better read on the quality of the packing.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's break this down. Analyzing `gen_7`'s metrics, things seem mostly okay, with the overlaps and boundary violations effectively at zero, though there were two violations. The radii have some spread, and the packing ratio is respectable.\n\nNow, looking at `aux_min_radius` across generations, it's telling a story. It was zero in `gen_7`, then non-zero in `gen_9`, and *back* to zero in `gen_10`. This is the key. The primary score dropped in `gen_10`, and now I see the optimizer possibly introducing circles with zero radius again, which means they are not contributing to the sum of radii, which is what we want to maximize. The fact that the violation metrics are still zero suggests either a very minor violation due to floating-point precision, or simply that the constraint hasn't been re-violated.\n\nSo, the optimizer is perhaps discarding circles by making their radius infinitesimal, so I need to understand that. To address this, I'm going to add a new metric: `aux_num_tiny_radii_circles`. This will count the number of circles whose radius is below a tiny threshold (1e-6, I think). This way I'll understand better if the optimizer is \"discarding\" circles, effectively removing them from the sum of radii by shrinking them to effectively zero. This helps identify the components that are not contributing.\n\nI considered modifying `aux_min_radius`, but the current 0.0 value already gives that signal, so the new metric will give a clearer signal.\n\nI need to confirm the radii data from `main.py` in `gen_10` to see if there are zero-radius circles, too.\n\nMoving on to a larger scope, I've identified some stagnant metrics. The overlap and boundary violation distances are always zero once feasibility is achieved. Those are not giving any \"gradient\" signal. So I'll replace them with new, more insightful metrics. I'm going to introduce:\n\n*   **`aux_average_distance_to_boundary`**: To measure how close circles are to the boundaries. A proxy for \"Constraint Tension\".\n*   **`aux_average_inter_circle_distance_ratio`**: To measure how tightly circles are packed without overlapping. Another proxy for \"Constraint Tension\" and \"Structural Sensitivity\".\n*   And finally, the `aux_num_tiny_radii_circles` to give the \"Component Contribution\" signal, as mentioned before.\n\nSo, I'll be replacing those three stagnant metrics with three new ones to get a better read on the quality of the packing.\n"}], "provider_specific_fields": {"thought_signatures": 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tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/llm_completions/vertex_ai__gemini-2.5-flash-1772074501.889-7ed6.json

@@ -0,0 +1 @@
+{"context_window": 1048576, "messages": [{"content": [{"type": "text", "text": "# ROLE: EXPERT EVALUATION AGENT\n\nYou are an expert numerical analyst and optimization architect assisting a code evolution system. Your purpose is to identify the \"hidden geometry\" of the problem space to guide the primary evolution loop toward global optima by writing code.\n\n<CORE_DIRECTIVES>\n**Mission:** Accelerate evolution by creating **GRADIENT-SIGNAL auxiliary evaluation metrics**. You do NOT modify the primary metric (ground truth). You provide \"diagnostic\" insights that measure the optimization process—helping the system escape local optima even when the primary score is stagnant.\n\n**Diagnostic Pillars (Mandatory for metric design):**\n1. **Constraint Tension (The Boundary Gradient):** Measure how \"tightly\" the current solution hits boundaries or limits. \n   - *Signal:* High tension suggests a bottleneck; high slack suggests wasted potential.\n2. **Structural Sensitivity (The Landscape Curvature):** Quantify how a tiny perturbation ($\\epsilon$) to variables changes the outcome.\n   - *Signal:* High sensitivity suggests a \"sharp\" peak (unstable); low sensitivity suggests a \"plateau\" (needs larger jumps).\n3. **Component Contribution (The Resource Balance):** Identify \"Selfish Components\"—sub-elements that achieve a high local score by blocking the global potential.\n   - *Signal:* Points directly to the specific variable index that needs mutation.\n4. **Search Velocity (The Efficiency Metric):** Relate the rate of improvement to the computational effort (iterations/time).\n   - *Signal:* Identifies diminishing returns and signals when to switch strategies.\n\n**Evolution Stages:**\n- **EXPLORATION:** Focus on **Feasibility & Diversity**. (e.g., `aux_constraint_slack`, `aux_variable_entropy`).\n- **OPTIMIZATION:** Focus on **Refinement & Leverage**. (e.g., `aux_parameter_sensitivity`, `aux_local_bottleneck_score`).\n- **CONVERGENCE:** Focus on **Robustness & Precision**. (e.g., `aux_noise_tolerance`, `aux_seed_consistency`).\n- **PLATEAU:** Focus on **Structure Breaking**. (e.g., `aux_symmetry_entropy`, `aux_order_robustness`).\n\n**Guiding Principles:**\n* **Fixed Limits:** Maximum **10 active auxiliary metrics**.\n* **Continuous Signals:** Avoid binary (0/1). Use floats that provide a smooth \"climbing\" landscape.\n* **Fault Tolerance:** If a calculation fails, return `0.0` (Defensive Coding).\n</CORE_DIRECTIVES>\n\n<STRICT_IMPLEMENTATION>\n**File & Interface:**\n* Implement exactly ONE entry point in `auxiliary_metrics.py`:\n    ```python\n    def evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n    ```\n\n**Toolbox:** `from eval_agent.tool_box import call_vision, call_tool`. (No `_internal` modules).\n\n**Coding Standards:**\n1. **Flat Output:** Return `{\"metric_name\": float/int/bool}`. \n2. **Absolute Paths:** Use `/` paths for all tool calls.\n3. NEVER delete, rename, or overwrite existing user/project files via destructive commands.\n- This applies especially to: `EVAL_AGENTS.md`, `auxiliary_metrics.py`, `main.py`, and any file under `$RESULTS_DIR`.\n  \n</STRICT_IMPLEMENTATION>\n\n<CODE_QUALITY>\n* **Self-Contained Implementation:** Ensure `auxiliary_metrics.py` is a complete, runnable script. Every update MUST include necessary imports at the top (e.g., `import numpy as np`, `import os`).\n* **Path Robustness:** - Always use `os.path.join` for file operations.\n* **Minimalist Modification:** When REPLACING a metric, delete all its old helper logic and redundant comments to prevent \"code rot.\"\n</CODE_QUALITY>\n\n<MEMORY_LOGGING>\nYou must maintain `EVAL_AGENTS.md` as your state log.\n\n**File Structure (Strict):**\n1. `# Active Metric Registry`: A list of currently active metrics.\n2. `## Generation <N>`: The log for the current step.\n\n**Metric Management Policy:**\n* Allowed Actions: `ADD`, `DELETE`, `REPLACE`.\n* **Constraint:** If you have 10 metrics, you MUST `DELETE` or `REPLACE` to add a new one.\n* **Retirement Rule:** If a metric's value did not meaningfully change across the last 3 generations, it MUST be `REPLACE`d. Stagnant metrics occupy slots without providing signal.\n\n**Active Metric Registry format (for extraction compatibility)**\nUse this exact bullet style:\n```markdown\n# Active Metric Registry\n- `metric_name`: One-line description.\n- `another_metric`: One-line description.\n```\n\n**Generation Log Format:**\n```markdown\n## Generation <N>\nStage: <EXPLORATION|OPTIMIZATION|CONVERGENCE|PLATEAU>\nPrimary score: <value from context>\nHypothesis: (Why is the current solution stuck? e.g., \"Variable X is blocking global growth.\")\nStagnant metrics: <metric_name> (unchanged for N gens), ... | none\nActions:\n- ADD <metric_name> (reason)\n- DELETE <metric_name> (reason)\n- REPLACE <old> -> <new> (reason)\nNotes:\n- <bullet point observation>\n```\n</MEMORY_LOGGING>\n\n<WORKFLOW>\n0. **Read Code First:** Before designing/updating any metric, you MUST inspect:\n   - current generation `main.py`\n   - current generation `results/metrics.json`\n1. **Diagnose:** Analyze `primary_result` and artifacts. Determine the Stage. Check metric history—flag any metric whose value has not meaningfully changed in the last 3 generations as **stagnant**.\n2. **Hypothesize:** Formulate a theory on what structural flaw is preventing a better primary score.\n3. **Design:** Select/Update metrics from the **Diagnostic Pillars** to test your hypothesis. Retire all stagnant metrics via `REPLACE`.\n4. **Execute (Code):** Update `auxiliary_metrics.py`. Run a smoke test to ensure no crashes.\n5. **Execute (Log):** Update `EVAL_AGENTS.md` following the strict format. Record stagnant metrics in the `Stagnant metrics` field.\n</WORKFLOW>\n\n\n\n\n\n\n\n<IMPORTANT>\nPrimary score tells you WHERE you are. Your auxiliary metrics tell you HOW TO MOVE. Design for the gradient.\n</IMPORTANT>"}], "role": "system"}, {"content": [{"type": "text", "text": "=== Generation 10 Evaluation ===\n\n📁 File Locations:\n- Results directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318\n- Memory: $RESULTS_DIR/eval_agent_memory/EVAL_AGENTS.md\n- Auxiliary metrics: $RESULTS_DIR/eval_agent_memory/auxiliary_metrics.py\n- All generations: $RESULTS_DIR/gen_0/ through gen_10/\n- Each generation has: main.py and results/metrics.json\n- Current code: $RESULTS_DIR/gen_10/main.py\n- Current metrics: $RESULTS_DIR/gen_10/results/metrics.json\n- Toolbox public API: eval_agent/tool_box/api.py\n- Toolbox reference template: eval_agent/templates/auxiliary_metrics_template.py\n- Current score: 2.2127\n\n⚠️  PRIMARY EVALUATOR (FIXED - DO NOT MODIFY):\n- Path: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/evaluate_ori.py\n- This file defines the GROUND TRUTH evaluation metric\n- The primary score comes from this evaluator\n- You MUST NOT modify or replace this evaluator\n- You can READ it to understand what is being optimized\n- Your job is to create AUXILIARY metrics that complement it\n\n🎯 Generation-Specific Focus (follow system prompt for full policy):\n1. Analyze progress up to generation 10 and diagnose current bottlenecks.\n2. Update auxiliary_metrics.py with this generation's metric set.\n   - Keep evaluate_aux as the single entry point and return a flat metric dict.\n   - Smoke-test metric execution on current generation artifacts before finishing.\n3. Update EVAL_AGENTS.md for this generation's actions and rationale.\n\nUse toolbox public APIs when needed:\n- from eval_agent.tool_box import call_vision, call_tool\n- Do NOT import eval_agent/tool_box/_internal/*\n\n📈 Combined Score Trend (previous up to 10 generations):\n- gen_0: 0.9598 | gen_1: 2.2731 | gen_2: 2.4060 | gen_3: 2.3530 | gen_4: 1.6221 | gen_5: 2.4535 | gen_6: 2.5414 | gen_7: 1.9800 | gen_8: 0.0000 | gen_9: 2.4743\n- Net change (gen_0 -> gen_9): +1.5145"}], "role": "user"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, here's the deal. My task is clear: I need to scrutinize the progress, update those auxiliary metrics we've been tracking, and, of course, keep a detailed log of everything I do. That's the drill.\n\nLet's dive into the data. The current score, 2.2127, is what we're working with. Now, looking back at the scores from previous generations…hmm. The initial score was 0.9598, and it's generally trended upwards, peaking at 2.5414 in gen_6. The really concerning thing here is that gen_8 score of 0.0000. That screams \"failure\" to me. It's a significant outlier, so we *definitely* need to investigate what went wrong there. While the overall trend has improved from the beginning, the up-and-down nature of the scores raises a red flag. It looks like the optimization process might be getting trapped in local optima or maybe just thrashing around the search space. \n\nConsidering that the current score is lower than both gen_9 and gen_6… we're not exactly moving in the right direction right now.\n\nOkay, let's get down to specifics. The first step is to check the `eval_agent_memory` directory. I'll need to see the `auxiliary_metrics.py` file to understand the current auxiliary metric calculations and the `EVAL_AGENTS.md` file to evaluate the performance of each agent. This is where we'll dig into the data and analyze exactly what's happening.\n", "signature": null}], "tool_calls": [{"id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"security_risk\": \"LOW\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/\", \"summary\": \"List contents of eval_agent_memory directory\"}"}}]}, {"content": [{"type": "text", "text": "total 36\n-rw------- 1 tengxiao tengxiao 2946 Feb 26 02:44 EVAL_AGENTS.md\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:46 __pycache__\n-rw-r--r-- 1 tengxiao tengxiao 7423 Feb 26 02:54 agent_behavior.jsonl\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 agent_runs\n-rw------- 1 tengxiao tengxiao 4157 Feb 26 02:44 auxiliary_metrics.py\ndrwxr-xr-x 2 tengxiao tengxiao 4096 Feb 26 02:54 llm_completions\n-rw-r--r-- 1 tengxiao tengxiao 2898 Feb 26 02:54 service_state.json\n[The command completed with exit code 0.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 0]"}], "role": "tool", "tool_call_id": "call_9a54c37807ff4cf1a6b5118b65b9__thought__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", "name": "terminal"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Alright, time to get down to brass tacks. I need to get a handle on where we are, what the current benchmarks look like, and what they're actually measuring. My next step is absolutely crucial: I need to dig into the `EVAL_AGENTS.md` file. That's where all the relevant details regarding the evaluation protocols, the chosen agents, and the specific test conditions will be laid out. Understanding that document is non-negotiable for me. After that, I'll move on to `auxiliary_metrics.py`. That's where the rubber meets the road on the performance side. I need to dissect that file to understand how they are *actually* computing and interpreting these auxiliary metrics. Gotta see the nitty-gritty details, the implementation choices, the specific functions being used. Without a solid grip on these two resources, any further work would be just guesswork, pure noise. This is the foundation - got to get this right.\n", "signature": null}], "tool_calls": [{"id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md\", \"summary\": \"View EVAL_AGENTS.md\", \"command\": \"view\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/EVAL_AGENTS.md:\n     1\t# Active Metric Registry\n     2\t- `aux_total_overlap_distance`: Total sum of penetration depths between overlapping circles.\n     3\t- `aux_max_overlap_distance`: Maximum penetration depth between any two overlapping circles.\n     4\t- `aux_boundary_violation_distance`: Total sum of distances a circle extends beyond the unit square boundary.\n     5\t- `aux_min_radius`: Smallest radius among all circles, indicating potential degeneration.\n     6\t- `aux_num_violated_circles`: Count of circles that violate either overlap or boundary constraints.\n     7\t- `aux_average_radii`: Average radius of all circles.\n     8\t- `aux_radius_standard_deviation`: Standard deviation of radii.\n     9\t- `aux_packed_area_ratio`: Ratio of the total area covered by circles to the unit square area.\n    10\t\n    11\t## Generation 4\n    12\tStage: OPTIMIZATION\n    13\tPrimary score: 1.6221\n    14\tHypothesis: The significant drop in primary score (from 2.3530 to 1.6221) is due to severe constraint violations (overlaps or boundary excursions) or a drastic reduction in circle radii.\n    15\tStagnant metrics: none\n    16\tActions:\n    17\t- ADD `aux_total_overlap_distance` (To quantify inter-circle constraint violation)\n    18\t- ADD `aux_max_overlap_distance` (To identify the worst-case overlap)\n    19\t- ADD `aux_boundary_violation_distance` (To quantify container boundary constraint violation)\n    20\t- ADD `aux_min_radius` (To detect if circles are becoming too small, leading to lower total radii)\n    21\t- ADD `aux_num_violated_circles` (To count how many circles are problematic)\n    22\tNotes:\n    23\t- Initializing auxiliary metrics for the first time.\n    24\t- The stage is considered OPTIMIZATION as we expect the system to be refining the solution, but a major regression occurred.\n    25\t\n    26\t## Generation 5\n    27\tStage: OPTIMIZATION\n    28\tPrimary score: 2.4535\n    29\tHypothesis: The previous auxiliary metrics were failing due to a bug (radii not loaded), leading to all 0.0 values. Fixing this bug will enable proper constraint violation diagnosis. Additionally, new metrics are introduced to assess the quality of valid packings, focusing on average circle size, radius distribution, and overall space utilization to guide further optimization.\n    30\tStagnant metrics: aux_total_overlap_distance, aux_max_overlap_distance, aux_boundary_violation_distance, aux_min_radius, aux_num_violated_circles (all were 0.0 due to a bug in previous generation)\n    31\tActions:\n    32\t- REPLACE `auxiliary_metrics.py` (Fixed `radii` loading bug and added `aux_average_radii`, `aux_radius_standard_deviation`, `aux_packed_area_ratio` to improve diagnostic capabilities).\n    33\t- ADD `aux_average_radii` (To track the average size of circles, directly related to the primary score).\n    34\t- ADD `aux_radius_standard_deviation` (To understand the uniformity of circle sizes and identify imbalances).\n    35\t- ADD `aux_packed_area_ratio` (To measure the overall density and efficiency of the packing solution).\n    36\tNotes:\n    37\t- The primary score has improved from gen_4, suggesting the system is recovering.\n    38\t- The previous auxiliary metrics were not providing useful signal due to an implementation error.\n    39\t\n    40\t\n"}], "role": "tool", "tool_call_id": "call_3592e64285b343a283966cd21be1__thought__CooBAY89a1880i+9GdagHaCU/uivcERRunIyaHOVE3vzdGHxa/b2dYId48+jK7fPr6rDk7zG3HVgQtpbkY1S3EbJyjeIUjbEU/ZAlUqPxshtm8YOHoabZ+8mcgeqNZqNcaacYk4ORXThEZvDlIdNz6Mq++aWdIn9vkgNvoNgked93bgRyMiEfxaCRQwP", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind as an expert:\n\nRight, so the `EVAL_AGENTS.md` file clued me in - a nasty bug bit us in `Generation 5`. Damn, looks like the `radii` weren't getting loaded properly. That explains why all the auxiliary metrics went flat to `0.0`.  That's a classic sign of a missing or misconfigured data dependency.\n\nGood, the fix was to swap out the `auxiliary_metrics.py` file. But, more importantly, they've added some new metrics to that file. We've got `aux_average_radii`, `aux_radius_standard_deviation`, and `aux_packed_area_ratio` now. Sounds like we're aiming for a more in-depth analysis of the radius distributions and packing density.\n\nNow, to really understand what's going on, I need to see the current implementation of `auxiliary_metrics.py`. Time to dive in and see how they're calculating these things. I need to check the logic, data dependencies, and potential edge cases of these new metrics, making sure they're correctly implemented and don't introduce any new biases. Let's see what we've got.\n", "signature": null}], "tool_calls": [{"id": "call_c064bfa737b345c0a69fe558a5f5__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"summary\": \"View auxiliary_metrics.py\", \"command\": \"view\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory/auxiliary_metrics.py:\n     1\timport numpy as np\n     2\timport os\n     3\tfrom typing import Dict, Any\n     4\t\n     5\tdef evaluate_aux(results_dir: str, primary_result: Dict[str, Any] | None = None) -> Dict[str, Any]:\n     6\t    \"\"\"Return auxiliary metrics as a dict.\"\"\"\n     7\t    metrics = {}\n     8\t    try:\n     9\t        extra_file_path = os.path.join(results_dir, \"extra.npz\")\n    10\t        if not os.path.exists(extra_file_path):\n    11\t            print(f\"Warning: extra.npz not found at {extra_file_path}\")\n    12\t            return metrics\n    13\t\n    14\t        data = np.load(extra_file_path)\n    15\t        centers = data[\"centers\"]\n    16\t        radii = data[\"radii\"] # Fix: Load radii\n    17\t        num_circles = centers.shape[0]\n    18\t        \n    19\t        # --- aux_total_overlap_distance and aux_max_overlap_distance ---\n    20\t        total_overlap_distance = 0.0\n    21\t        max_overlap_distance = 0.0\n    22\t\n    23\t        for i in range(num_circles):\n    24\t            for j in range(i + 1, num_circles):\n    25\t                dist = np.linalg.norm(centers[i] - centers[j])\n    26\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    27\t                if dist < min_dist_for_no_overlap:\n    28\t                    overlap = min_dist_for_no_overlap - dist\n    29\t                    total_overlap_distance += overlap\n    30\t                    if overlap > max_overlap_distance:\n    31\t                        max_overlap_distance = overlap\n    32\t\n    33\t        metrics[\"aux_total_overlap_distance\"] = float(total_overlap_distance)\n    34\t        metrics[\"aux_max_overlap_distance\"] = float(max_overlap_distance)\n    35\t        \n    36\t        # --- aux_boundary_violation_distance ---\n    37\t        boundary_violation_distance = 0.0\n    38\t        \n    39\t        for i in range(num_circles):\n    40\t            x, y = centers[i]\n    41\t            r = radii[i]\n    42\t\n    43\t            # Check x-bounds\n    44\t            if x - r < 0:\n    45\t                boundary_violation_distance += abs(x - r)\n    46\t            if x + r > 1:\n    47\t                boundary_violation_distance += abs(x + r - 1)\n    48\t            \n    49\t            # Check y-bounds\n    50\t            if y - r < 0:\n    51\t                boundary_violation_distance += abs(y - r)\n    52\t            if y + r > 1:\n    53\t                boundary_violation_distance += abs(y + r - 1)\n    54\t                \n    55\t        metrics[\"aux_boundary_violation_distance\"] = float(boundary_violation_distance)\n    56\t\n    57\t        # --- aux_min_radius ---\n    58\t        metrics[\"aux_min_radius\"] = float(np.min(radii)) if radii.size > 0 else 0.0\n    59\t        \n    60\t        # --- aux_num_violated_circles ---\n    61\t        num_violated_circles = 0\n    62\t        violated_circles_indices = set()\n    63\t\n    64\t        # Check for overlaps (re-do from above to get specific circles)\n    65\t        for i in range(num_circles):\n    66\t            for j in range(i + 1, num_circles):\n    67\t                dist = np.linalg.norm(centers[i] - centers[j])\n    68\t                min_dist_for_no_overlap = radii[i] + radii[j]\n    69\t                if dist < min_dist_for_no_overlap:\n    70\t                    violated_circles_indices.add(i)\n    71\t                    violated_circles_indices.add(j)\n    72\t\n    73\t        # Check for boundary violations (re-do from above to get specific circles)\n    74\t        for i in range(num_circles):\n    75\t            x, y = centers[i]\n    76\t            r = radii[i]\n    77\t            if x - r < 0 or x + r > 1 or y - r < 0 or y + r > 1:\n    78\t                violated_circles_indices.add(i)\n    79\t                \n    80\t        metrics[\"aux_num_violated_circles\"] = len(violated_circles_indices)\n    81\t        \n    82\t        # --- New metrics ---\n    83\t        # aux_average_radii\n    84\t        metrics[\"aux_average_radii\"] = float(np.mean(radii)) if radii.size > 0 else 0.0\n    85\t\n    86\t        # aux_radius_standard_deviation\n    87\t        metrics[\"aux_radius_standard_deviation\"] = float(np.std(radii)) if radii.size > 0 else 0.0\n    88\t        \n    89\t        # aux_packed_area_ratio\n    90\t        metrics[\"aux_packed_area_ratio\"] = float(np.sum(np.pi * radii**2))\n    91\t\n    92\t\n    93\t    except Exception as e:\n    94\t        print(f\"Error in evaluate_aux: {e}\")\n    95\t        # Return default 0.0 for all metrics in case of failure\n    96\t        metrics[\"aux_total_overlap_distance\"] = 0.0\n    97\t        metrics[\"aux_max_overlap_distance\"] = 0.0\n    98\t        metrics[\"aux_boundary_violation_distance\"] = 0.0\n    99\t        metrics[\"aux_min_radius\"] = 0.0\n   100\t        metrics[\"aux_num_violated_circles\"] = 0\n   101\t        metrics[\"aux_average_radii\"] = 0.0\n   102\t        metrics[\"aux_radius_standard_deviation\"] = 0.0\n   103\t        metrics[\"aux_packed_area_ratio\"] = 0.0\n   104\t        \n   105\t    return metrics\n   106\t\n   107\t\n"}], "role": "tool", "tool_call_id": "call_c064bfa737b345c0a69fe558a5f5__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's get down to brass tacks. That `auxiliary_metrics.py` file is a solid foundation. I like the inclusion of those overlap, boundary violation, radii distribution, and packed area metrics – they give a good, holistic view of the system's performance. And the `try-except` blocks with those default `0.0` values are a crucial safety net; prevents the whole thing from crashing if something goes awry during calculation. Smart.\n\nNow, the real work begins. I need to dive into `metrics.json` for generation 10. That's where the rubber meets the road. I'll be scrutinizing the values for those key metrics. My goal is to spot any stagnation. Are any of these performance indicators plateauing? That will be the key. This analysis is crucial for formulating my hypothesis for the next phase. Where are the weak points? What improvements are we seeing, and what's holding us back? Let's see what the data reveals.\n", "signature": null}], "tool_calls": [{"id": "call_0b7cf572259f4166bc04d5489b3f__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/results/metrics.json\", \"summary\": \"View metrics.json for gen_10\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/results/metrics.json:\n     1\t{\n     2\t  \"combined_score\": 2.2127378037142638,\n     3\t  \"correct\": true,\n     4\t  \"primary\": {\n     5\t    \"combined_score\": 2.2127378037142638,\n     6\t    \"public\": {\n     7\t      \"centers_str\": \"  centers[0] = (0.0860, 0.0915)\\n  centers[1] = (0.2918, 0.0922)\\n  centers[2] = (0.5000, 0.0864)\\n  centers[3] = (0.7072, 0.0845)\\n  centers[4] = (0.9190, 0.0968)\\n  centers[5] = (0.0902, 0.3050)\\n  centers[6] = (0.2967, 0.2972)\\n  centers[7] = (0.5025, 0.2914)\\n  centers[8] = (0.7075, 0.2895)\\n  centers[9] = (0.9138, 0.3033)\\n  centers[10] = (0.0000, 0.4997)\\n  centers[11] = (0.2043, 0.5146)\\n  centers[12] = (0.4047, 0.4715)\\n  centers[13] = (0.6015, 0.5286)\\n  centers[14] = (0.7987, 0.4730)\\n  centers[15] = (1.0000, 0.5115)\\n  centers[16] = (0.0877, 0.6980)\\n  centers[17] = (0.2930, 0.6994)\\n  centers[18] = (0.4981, 0.7056)\\n  centers[19] = (0.7108, 0.7025)\\n  centers[20] = (0.9162, 0.6993)\\n  centers[21] = (0.0884, 0.9118)\\n  centers[22] = (0.2962, 0.9050)\\n  centers[23] = (0.5048, 0.9113)\\n  centers[24] = (0.7136, 0.9082)\\n  centers[25] = (0.9186, 0.9065)\",\n     8\t      \"num_circles\": 26\n     9\t    },\n    10\t    \"private\": {\n    11\t      \"reported_sum_of_radii\": 2.2127378037142638\n    12\t    },\n    13\t    \"execution_time_mean\": 4.81304601021111,\n    14\t    \"execution_time_std\": 0.0,\n    15\t    \"num_valid_runs\": 1,\n    16\t    \"num_invalid_runs\": 0,\n    17\t    \"all_validation_errors\": [],\n    18\t    \"correct\": true,\n    19\t    \"validation_error\": null\n    20\t  },\n    21\t  \"auxiliary\": {\n    22\t    \"aux_total_overlap_distance\": 0.0,\n    23\t    \"aux_max_overlap_distance\": 0.0,\n    24\t    \"aux_boundary_violation_distance\": 0.0,\n    25\t    \"aux_min_radius\": 0.0,\n    26\t    \"aux_num_violated_circles\": 0.0,\n    27\t    \"aux_average_radii\": 0.0851053001428563,\n    28\t    \"aux_radius_standard_deviation\": 0.027904573430154296,\n    29\t    \"aux_packed_area_ratio\": 0.6552137387225716,\n    30\t    \"aux_metric_eval_success\": 1.0,\n    31\t    \"aux_metric_error_code\": 0.0,\n    32\t    \"aux_metric_error_message_length\": 0.0,\n    33\t    \"aux_metric_error_detail_length\": 0.0,\n    34\t    \"aux_metric_non_numeric_dropped_count\": 0.0\n    35\t  },\n    36\t  \"auxiliary_descriptions\": {\n    37\t    \"aux_total_overlap_distance\": \"Total sum of penetration depths between overlapping circles.\",\n    38\t    \"aux_max_overlap_distance\": \"Maximum penetration depth between any two overlapping circles.\",\n    39\t    \"aux_boundary_violation_distance\": \"Total sum of distances a circle extends beyond the unit square boundary.\",\n    40\t    \"aux_min_radius\": \"Smallest radius among all circles, indicating potential degeneration.\",\n    41\t    \"aux_num_violated_circles\": \"Count of circles that violate either overlap or boundary constraints.\",\n    42\t    \"aux_average_radii\": \"Average radius of all circles.\",\n    43\t    \"aux_radius_standard_deviation\": \"Standard deviation of radii.\",\n    44\t    \"aux_packed_area_ratio\": \"Ratio of the total area covered by circles to the unit square area.\"\n    45\t  },\n    46\t  \"timestamp\": 1772074454.809435,\n    47\t  \"generation\": 10\n    48\t}\n"}], "role": "tool", "tool_call_id": "call_0b7cf572259f4166bc04d5489b3f__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's break this down. First off, I'm looking at the metrics from `gen_10/results/metrics.json`. The good news is, the overlap and boundary violation metrics are all zero. Great! No feasibility issues here.\n\nBut hold on, `aux_min_radius` is also zero. Hmm. That's a red flag. I know from the code that this gets calculated as the minimum radius of all the circles. If it's zero, that means at least one circle has a radius of zero. That's not ideal because it's essentially a non-existent circle. It doesn't contribute to the overall objective – maximizing the packed area.\n\nThis smells like a potential optimization \"cheat\". The algorithm might be adding these zero-radius circles to satisfy constraints like a minimum number of circles, without actually improving the packing density. It's essentially gaming the system.\n\nI need to investigate further. To see if this zero-radius situation is a recent phenomenon or a recurring issue, I need to check the metrics from previous generations. I'll need the `metrics.json` files from earlier runs. Let's start by looking at `gen_9` and `gen_7`. I'll skip `gen_8` because it looks like a failed run with zero primary score. This will let me see if this zero-radius issue is new, or if it's been present for a while, potentially indicating a problem with the optimization strategy.\n", "signature": null}], "tool_calls": [{"id": "call_d737af0b89834ae5bb663af49c64__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_9/results/metrics.json\", \"security_risk\": \"LOW\", \"summary\": \"View metrics.json for gen_9\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_9/results/metrics.json:\n     1\t{\n     2\t  \"combined_score\": 2.4742543201305223,\n     3\t  \"correct\": true,\n     4\t  \"primary\": {\n     5\t    \"combined_score\": 2.4742543201305223,\n     6\t    \"public\": {\n     7\t      \"centers_str\": \"  centers[0] = (0.0993, 0.0995)\\n  centers[1] = (0.2996, 0.0962)\\n  centers[2] = (0.5002, 0.0931)\\n  centers[3] = (0.7002, 0.0982)\\n  centers[4] = (0.9006, 0.1000)\\n  centers[5] = (0.1006, 0.3001)\\n  centers[6] = (0.3008, 0.2901)\\n  centers[7] = (0.5004, 0.2823)\\n  centers[8] = (0.7002, 0.2958)\\n  centers[9] = (0.9001, 0.3009)\\n  centers[10] = (0.0998, 0.5011)\\n  centers[11] = (0.2997, 0.4830)\\n  centers[12] = (0.4991, 0.4712)\\n  centers[13] = (0.6991, 0.4932)\\n  centers[14] = (0.8998, 0.5013)\\n  centers[15] = (0.0944, 0.7016)\\n  centers[16] = (0.2835, 0.6738)\\n  centers[17] = (0.5223, 0.6577)\\n  centers[18] = (0.7116, 0.6904)\\n  centers[19] = (0.9047, 0.7026)\\n  centers[20] = (0.0864, 0.9002)\\n  centers[21] = (0.2674, 0.9084)\\n  centers[22] = (0.4146, 0.8075)\\n  centers[23] = (0.5596, 0.9104)\\n  centers[24] = (0.7378, 0.8845)\\n  centers[25] = (0.9148, 0.9023)\",\n     8\t      \"num_circles\": 26\n     9\t    },\n    10\t    \"private\": {\n    11\t      \"reported_sum_of_radii\": 2.4742543201305223\n    12\t    },\n    13\t    \"execution_time_mean\": 0.49658707808703184,\n    14\t    \"execution_time_std\": 0.0,\n    15\t    \"num_valid_runs\": 1,\n    16\t    \"num_invalid_runs\": 0,\n    17\t    \"all_validation_errors\": [],\n    18\t    \"correct\": true,\n    19\t    \"validation_error\": null\n    20\t  },\n    21\t  \"auxiliary\": {\n    22\t    \"aux_total_overlap_distance\": 2.7755575615628914e-17,\n    23\t    \"aux_max_overlap_distance\": 2.7755575615628914e-17,\n    24\t    \"aux_boundary_violation_distance\": 0.0,\n    25\t    \"aux_min_radius\": 0.08523411119073121,\n    26\t    \"aux_num_violated_circles\": 2.0,\n    27\t    \"aux_average_radii\": 0.09516362769732778,\n    28\t    \"aux_radius_standard_deviation\": 0.004395210151437102,\n    29\t    \"aux_packed_area_ratio\": 0.7412942288959274,\n    30\t    \"aux_metric_eval_success\": 1.0,\n    31\t    \"aux_metric_error_code\": 0.0,\n    32\t    \"aux_metric_error_message_length\": 0.0,\n    33\t    \"aux_metric_error_detail_length\": 0.0,\n    34\t    \"aux_metric_non_numeric_dropped_count\": 0.0\n    35\t  },\n    36\t  \"auxiliary_descriptions\": {\n    37\t    \"aux_total_overlap_distance\": \"Total sum of penetration depths between overlapping circles.\",\n    38\t    \"aux_max_overlap_distance\": \"Maximum penetration depth between any two overlapping circles.\",\n    39\t    \"aux_boundary_violation_distance\": \"Total sum of distances a circle extends beyond the unit square boundary.\",\n    40\t    \"aux_min_radius\": \"Smallest radius among all circles, indicating potential degeneration.\",\n    41\t    \"aux_num_violated_circles\": \"Count of circles that violate either overlap or boundary constraints.\",\n    42\t    \"aux_average_radii\": \"Average radius of all circles.\",\n    43\t    \"aux_radius_standard_deviation\": \"Standard deviation of radii.\",\n    44\t    \"aux_packed_area_ratio\": \"Ratio of the total area covered by circles to the unit square area.\"\n    45\t  },\n    46\t  \"timestamp\": 1772074340.404484,\n    47\t  \"generation\": 9\n    48\t}\n"}], "role": "tool", "tool_call_id": "call_d737af0b89834ae5bb663af49c64__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's see what the latest generation, `gen_9`, is doing. Looking at the data... Hmm. The overlap metrics are essentially zero: `aux_total_overlap_distance` and `aux_max_overlap_distance` are both at 2.77e-17 – practically nothing. Boundary violations, too, are coming in at zero. That's good.\n\nThe minimum radius, `aux_min_radius`, is at 0.0852, which is expected. The number of violated circles, `aux_num_violated_circles`, however, is showing 2.0. That's a little peculiar. With zero overlap and zero boundary violation distance, this seems like a very slight issue, likely a consequence of some numerical precision or rounding issue. I'll flag this to monitor in subsequent generations, but I'm not overly concerned at this stage.\n\nThe average radii, `aux_average_radii`, is 0.0951, and the standard deviation, `aux_radius_standard_deviation`, is a tight 0.0043. Consistent. The `aux_packed_area_ratio` is at 0.7412, indicating reasonable packing efficiency.\n\nAlright, that gives me a good baseline for `gen_9`. Now, I should compare this to some earlier generations to track progress. Let's see what `gen_7` looked like.\n", "signature": null}], "tool_calls": [{"id": "call_09efcde944a6497bad2faf4efee4__thought__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", "type": "function", "function": {"name": "file_editor", "arguments": "{\"security_risk\": \"LOW\", \"path\": \"/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_7/results/metrics.json\", \"summary\": \"View metrics.json for gen_7\", \"command\": \"view\"}"}}]}, {"content": [{"type": "text", "text": "Here's the result of running `cat -n` on /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_7/results/metrics.json:\n     1\t{\n     2\t  \"combined_score\": 1.9800352414974887,\n     3\t  \"correct\": true,\n     4\t  \"primary\": {\n     5\t    \"combined_score\": 1.9800352414974887,\n     6\t    \"public\": {\n     7\t      \"centers_str\": \"  centers[0] = (0.0936, 0.0959)\\n  centers[1] = (0.2976, 0.0973)\\n  centers[2] = (0.5005, 0.0949)\\n  centers[3] = (0.7028, 0.0957)\\n  centers[4] = (0.9049, 0.0971)\\n  centers[5] = (0.0941, 0.2985)\\n  centers[6] = (0.2963, 0.2992)\\n  centers[7] = (0.4996, 0.2976)\\n  centers[8] = (0.7019, 0.2983)\\n  centers[9] = (0.9041, 0.2993)\\n  centers[10] = (0.0941, 0.5007)\\n  centers[11] = (0.2968, 0.5011)\\n  centers[12] = (0.4994, 0.4995)\\n  centers[13] = (0.7017, 0.5005)\\n  centers[14] = (0.9046, 0.5027)\\n  centers[15] = (0.0958, 0.7044)\\n  centers[16] = (0.2981, 0.7030)\\n  centers[17] = (0.5009, 0.7024)\\n  centers[18] = (0.7029, 0.7028)\\n  centers[19] = (0.9051, 0.7048)\\n  centers[20] = (0.0000, 0.8986)\\n  centers[21] = (0.2002, 0.8974)\\n  centers[22] = (0.4002, 0.9084)\\n  centers[23] = (0.6000, 0.8970)\\n  centers[24] = (0.8000, 0.9053)\\n  centers[25] = (1.0000, 0.8977)\",\n     8\t      \"num_circles\": 26\n     9\t    },\n    10\t    \"private\": {\n    11\t      \"reported_sum_of_radii\": 1.9800352414974887\n    12\t    },\n    13\t    \"execution_time_mean\": 2.5255804620683193,\n    14\t    \"execution_time_std\": 0.0,\n    15\t    \"num_valid_runs\": 1,\n    16\t    \"num_invalid_runs\": 0,\n    17\t    \"all_validation_errors\": [],\n    18\t    \"correct\": true,\n    19\t    \"validation_error\": null\n    20\t  },\n    21\t  \"auxiliary\": {\n    22\t    \"aux_total_overlap_distance\": 5.551115123125783e-17,\n    23\t    \"aux_max_overlap_distance\": 5.551115123125783e-17,\n    24\t    \"aux_boundary_violation_distance\": 0.0,\n    25\t    \"aux_min_radius\": 0.0,\n    26\t    \"aux_num_violated_circles\": 2.0,\n    27\t    \"aux_average_radii\": 0.07615520159605725,\n    28\t    \"aux_radius_standard_deviation\": 0.04748917984078609,\n    29\t    \"aux_packed_area_ratio\": 0.6579304298243859,\n    30\t    \"aux_metric_eval_success\": 1.0,\n    31\t    \"aux_metric_error_code\": 0.0,\n    32\t    \"aux_metric_error_message_length\": 0.0,\n    33\t    \"aux_metric_error_detail_length\": 0.0,\n    34\t    \"aux_metric_non_numeric_dropped_count\": 0.0\n    35\t  },\n    36\t  \"auxiliary_descriptions\": {\n    37\t    \"aux_total_overlap_distance\": \"Total sum of penetration depths between overlapping circles.\",\n    38\t    \"aux_max_overlap_distance\": \"Maximum penetration depth between any two overlapping circles.\",\n    39\t    \"aux_boundary_violation_distance\": \"Total sum of distances a circle extends beyond the unit square boundary.\",\n    40\t    \"aux_min_radius\": \"Smallest radius among all circles, indicating potential degeneration.\",\n    41\t    \"aux_num_violated_circles\": \"Count of circles that violate either overlap or boundary constraints.\",\n    42\t    \"aux_average_radii\": \"Average radius of all circles.\",\n    43\t    \"aux_radius_standard_deviation\": \"Standard deviation of radii.\",\n    44\t    \"aux_packed_area_ratio\": \"Ratio of the total area covered by circles to the unit square area.\"\n    45\t  },\n    46\t  \"timestamp\": 1772074085.0100996,\n    47\t  \"generation\": 7\n    48\t}\n"}], "role": "tool", "tool_call_id": "call_09efcde944a6497bad2faf4efee4__thought__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", "name": "file_editor"}, {"role": "assistant", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, let's break this down. Analyzing `gen_7`'s metrics, things seem mostly okay, with the overlaps and boundary violations effectively at zero, though there were two violations. The radii have some spread, and the packing ratio is respectable.\n\nNow, looking at `aux_min_radius` across generations, it's telling a story. It was zero in `gen_7`, then non-zero in `gen_9`, and *back* to zero in `gen_10`. This is the key. The primary score dropped in `gen_10`, and now I see the optimizer possibly introducing circles with zero radius again, which means they are not contributing to the sum of radii, which is what we want to maximize. The fact that the violation metrics are still zero suggests either a very minor violation due to floating-point precision, or simply that the constraint hasn't been re-violated.\n\nSo, the optimizer is perhaps discarding circles by making their radius infinitesimal, so I need to understand that. To address this, I'm going to add a new metric: `aux_num_tiny_radii_circles`. This will count the number of circles whose radius is below a tiny threshold (1e-6, I think). This way I'll understand better if the optimizer is \"discarding\" circles, effectively removing them from the sum of radii by shrinking them to effectively zero. This helps identify the components that are not contributing.\n\nI considered modifying `aux_min_radius`, but the current 0.0 value already gives that signal, so the new metric will give a clearer signal.\n\nI need to confirm the radii data from `main.py` in `gen_10` to see if there are zero-radius circles, too.\n\nMoving on to a larger scope, I've identified some stagnant metrics. The overlap and boundary violation distances are always zero once feasibility is achieved. Those are not giving any \"gradient\" signal. So I'll replace them with new, more insightful metrics. I'm going to introduce:\n\n*   **`aux_average_distance_to_boundary`**: To measure how close circles are to the boundaries. A proxy for \"Constraint Tension\".\n*   **`aux_average_inter_circle_distance_ratio`**: To measure how tightly circles are packed without overlapping. Another proxy for \"Constraint Tension\" and \"Structural Sensitivity\".\n*   And finally, the `aux_num_tiny_radii_circles` to give the \"Component Contribution\" signal, as mentioned before.\n\nSo, I'll be replacing those three stagnant metrics with three new ones to get a better read on the quality of the packing.\n", "signature": null}], "tool_calls": [{"id": "call_cf7e4028b62e45369ba3d2e74c4b__thought__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", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python3 -c \\\"import numpy as np; data = np.load('/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/extra.npz'); radii = data['radii']; print(radii)\\\"\", \"summary\": \"Check radii values in gen_10/extra.npz\", \"security_risk\": \"LOW\"}"}}]}, {"content": [{"type": "text", "text": "Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    import numpy as np; data = np.load('/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/extra.npz'); radii = data['radii']; print(radii)\n                               ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/tengxiao/miniconda3/lib/python3.13/site-packages/numpy/lib/_npyio_impl.py\", line 454, in load\n    fid = stack.enter_context(open(os.fspath(file), \"rb\"))\n                              ~~~~^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/extra.npz'\n[The command completed with exit code 1.]\n[Current working directory: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory]\n[Python interpreter: /home/tengxiao/miniconda3/bin/python]\n[Command finished with exit code 1]"}], "role": "tool", "tool_call_id": "call_cf7e4028b62e45369ba3d2e74c4b__thought__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"name": "terminal"}], "tools": [{"description": "Execute a bash command in the terminal within a persistent shell session.\n\n\n### Command Execution\n* One command at a time: You can only execute one bash command at a time. If you need to run multiple commands sequentially, use `&&` or `;` to chain them together.\n* Persistent session: Commands execute in a persistent shell session where environment variables, virtual environments, and working directory persist between commands.\n* Soft timeout: Commands have a soft timeout of 10 seconds, once that's reached, you have the option to continue or interrupt the command (see section below for details)\n* Shell options: Do NOT use `set -e`, `set -eu`, or `set -euo pipefail` in shell scripts or commands in this environment. The runtime may not support them and can cause unusable shell sessions. If you want to run multi-line bash commands, write the commands to a file and then run it, instead.\n\n### Long-running Commands\n* For commands that may run indefinitely, run them in the background and redirect output to a file, e.g. `python3 app.py > server.log 2>&1 &`.\n* For commands that may run for a long time (e.g. installation or testing commands), or commands that run for a fixed amount of time (e.g. sleep), you should set the \"timeout\" parameter of your function call to an appropriate value.\n* If a bash command returns exit code `-1`, this means the process hit the soft timeout and is not yet finished. By setting `is_input` to `true`, you can:\n  - Send empty `command` to retrieve additional logs\n  - Send text (set `command` to the text) to STDIN of the running process\n  - Send control commands like `C-c` (Ctrl+C), `C-d` (Ctrl+D), or `C-z` (Ctrl+Z) to interrupt the process\n  - If you do C-c, you can re-start the process with a longer \"timeout\" parameter to let it run to completion\n\n### Best Practices\n* Directory verification: Before creating new directories or files, first verify the parent directory exists and is the correct location.\n* Directory management: Try to maintain working directory by using absolute paths and avoiding excessive use of `cd`.\n\n### Output Handling\n* Output truncation: If the output exceeds a maximum length, it will be truncated before being returned.\n\n### Terminal Reset\n* Terminal reset: If the terminal becomes unresponsive, you can set the \"reset\" parameter to `true` to create a new terminal session. This will terminate the current session and start fresh.\n* Warning: Resetting the terminal will lose all previously set environment variables, working directory changes, and any running processes. Use this only when the terminal stops responding to commands.\n", "action_type": "TerminalAction", "observation_type": "TerminalObservation", "annotations": {"title": "terminal", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": true}, "kind": "TerminalTool", "title": "terminal"}, {"description": "Custom editing tool for viewing, creating and editing files in plain-text format\n* State is persistent across command calls and discussions with the user\n* If `path` is an image file (.png, .jpg, .jpeg, .gif, .webp, .bmp), `view` displays the image content\n* If `path` is a text file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n* This tool can be used for creating and editing files in plain-text format.\n\n\nBefore using this tool:\n1. Use the view tool to understand the file's contents and context\n2. Verify the directory path is correct (only applicable when creating new files):\n   - Use the view tool to verify the parent directory exists and is the correct location\n\nWhen making edits:\n   - Ensure the edit results in idiomatic, correct code\n   - Do not leave the code in a broken state\n   - Always use absolute file paths (starting with /)\n\nCRITICAL REQUIREMENTS FOR USING THIS TOOL:\n\n1. EXACT MATCHING: The `old_str` parameter must match EXACTLY one or more consecutive lines from the file, including all whitespace and indentation. The tool will fail if `old_str` matches multiple locations or doesn't match exactly with the file content.\n\n2. UNIQUENESS: The `old_str` must uniquely identify a single instance in the file:\n   - Include sufficient context before and after the change point (3-5 lines recommended)\n   - If not unique, the replacement will not be performed\n\n3. REPLACEMENT: The `new_str` parameter should contain the edited lines that replace the `old_str`. Both strings must be different.\n\nRemember: when making multiple file edits in a row to the same file, you should prefer to send all edits in a single message with multiple calls to this tool, rather than multiple messages with a single call each.\n\n\nYour current working directory is: /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/eval_agent_memory\nWhen exploring project structure, start with this directory instead of the root filesystem.", "action_type": "FileEditorAction", "observation_type": "FileEditorObservation", "annotations": {"title": "file_editor", "readOnlyHint": false, "destructiveHint": true, "idempotentHint": false, "openWorldHint": false}, "kind": "FileEditorTool", "title": "file_editor"}, {"description": "This tool provides structured task management capabilities for development workflows.\nIt enables systematic tracking of work items, progress monitoring, and efficient\norganization of complex development activities.\n\nThe tool maintains visibility into project status and helps communicate\nprogress effectively to users.\n\n## Application Guidelines\n\nUtilize this tool in the following situations:\n\n1. Multi-phase development work - When projects involve multiple sequential or\n   parallel activities\n2. Complex implementation tasks - Work requiring systematic planning and\n   coordination across multiple components\n3. Explicit user request for task organization - When users specifically ask\n   for structured task management\n4. Multiple concurrent requirements - When users present several work items\n   that need coordination\n5. Project initiation - Capture and organize user requirements at project start\n6. Work commencement - Update task status to in_progress before beginning\n   implementation. Maintain focus by limiting active work to one task\n7. Task completion - Update status to done and identify any additional work\n   that emerged during implementation\n\n## Situations Where Tool Usage Is Unnecessary\n\nAvoid using this tool when:\n\n1. Single atomic tasks that require no decomposition\n2. Trivial operations where tracking adds no organizational value\n3. Simple activities completable in minimal steps\n4. Pure information exchange or discussion\n\nNote: For single straightforward tasks, proceed with direct implementation\nrather than creating tracking overhead.\n\n## Usage Scenarios\n\n**Scenario A: Feature Development with Validation**\nUser request: \"Build a user authentication system with login/logout functionality.\nDon't forget to include input validation and error handling!\"\n\nResponse approach: I'll implement a user authentication system with comprehensive\nvalidation. Let me organize this work systematically.\n\n*Task breakdown includes:*\n1. Design authentication API endpoints and data models\n2. Implement user login/logout controllers with session management\n3. Add client-side form validation and error display\n4. Create backend input sanitization and security checks\n5. Implement comprehensive error handling for edge cases\n6. Perform integration testing and fix any issues\n\n**Scenario B: Codebase Refactoring**\nUser request: \"I need to update all instances of the method 'fetchData' to\n'retrieveInformation' throughout the entire project\"\n\nResponse approach: Let me first analyze your codebase to identify all locations\nwhere 'fetchData' appears.\n\n*After code analysis*\n\nI've located 12 occurrences of 'fetchData' across 6 files in your project.\nI'll create a systematic plan to update these references.\n\n*Organized task list includes specific file-by-file updates*\n\n**Scenario C: Multi-feature Development**\nUser request: \"Set up a blog platform with these components: admin dashboard,\narticle management, comment system, and user profiles.\"\n\nResponse approach: I'll help build your blog platform. Let me break down these\ncomponents into manageable implementation phases.\n\n*Creates structured plan with each feature decomposed into specific development tasks*\n\n## Counter-examples - Direct Implementation Preferred\n\n**Counter-example A: Simple inquiry**\nUser request: \"What's the syntax for a for loop in JavaScript?\"\n\nResponse approach: Here's how to write a for loop in JavaScript:\n\n```javascript\nfor (let i = 0; i < array.length; i++) {\n    console.log(array[i]);\n}\n```\n\n*No task tracking needed - single informational response*\n\n**Counter-example B: Minor modification**\nUser request: \"Please add a docstring to the processPayment function explaining\nits parameters.\"\n\nResponse approach: I'll add a docstring to explain the processPayment function.\n\n*Direct edit without task tracking for simple modification*\n\n## Status Management and Workflow\n\n1. **Status Values**: Track work using these states:\n   - todo: Not yet initiated\n   - in_progress: Currently active (maintain single focus)\n   - done: Successfully completed\n\n2. **Workflow Practices**:\n   - Update status dynamically as work progresses\n   - Mark completion immediately upon task finish\n   - Limit active work to ONE task at any given time\n   - Complete current activities before initiating new ones\n   - Remove obsolete tasks from tracking entirely\n\n3. **Completion Criteria**:\n   - Mark tasks as done only when fully achieved\n   - Keep status as in_progress if errors, blocks, or partial completion exist\n   - Create new tasks for discovered issues or dependencies\n   - Never mark done when:\n       - Test suites are failing\n       - Implementation remains incomplete\n       - Unresolved errors persist\n       - Required resources are unavailable\n\n4. **Task Organization**:\n   - Write precise, actionable descriptions\n   - Decompose complex work into manageable units\n   - Use descriptive, clear naming conventions\n\nWhen uncertain, favor using this tool. Proactive task management demonstrates\nsystematic approach and ensures comprehensive requirement fulfillment.", "action_type": "TaskTrackerAction", "observation_type": "TaskTrackerObservation", "annotations": {"readOnlyHint": false, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "TaskTrackerTool", "title": "task_tracker"}, {"description": "Signals the completion of the current task or conversation.\n\nUse this tool when:\n- You have successfully completed the user's requested task\n- You cannot proceed further due to technical limitations or missing information\n\nThe message should include:\n- A clear summary of actions taken and their results\n- Any next steps for the user\n- Explanation if you're unable to complete the task\n- Any follow-up questions if more information is needed\n", "action_type": "FinishAction", "observation_type": "FinishObservation", "annotations": {"title": "finish", "readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "FinishTool", "title": "finish"}, {"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.\n\nCommon use cases:\n1. When exploring a repository and discovering the source of a bug, call this tool to brainstorm several unique ways of fixing the bug, and assess which change(s) are likely to be simplest and most effective.\n2. After receiving test results, use this tool to brainstorm ways to fix failing tests.\n3. When planning a complex refactoring, use this tool to outline different approaches and their tradeoffs.\n4. When designing a new feature, use this tool to think through architecture decisions and implementation details.\n5. When debugging a complex issue, use this tool to organize your thoughts and hypotheses.\n\nThe tool simply logs your thought process for better transparency and does not execute any code or make changes.", "action_type": "ThinkAction", "observation_type": "ThinkObservation", "annotations": {"readOnlyHint": true, "destructiveHint": false, "idempotentHint": true, "openWorldHint": false}, "kind": "ThinkTool", "title": "think"}], "kwargs": {"max_completion_tokens": 65535, "reasoning_effort": "high"}, "response": {"id": "_rWfabGKKZ3F694PuZW8wQw", "created": 1772074494, "model": "gemini-2.5-flash", "object": "chat.completion", "choices": [{"finish_reason": "tool_calls", "index": 0, "message": {"role": "assistant", "tool_calls": [{"index": 0, "provider_specific_fields": {"thought_signature": 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"function": {"arguments": "{\"summary\": \"List contents of gen_10 directory\", \"command\": \"ls -l /home/tengxiao/pj/ShinkaEvolve/tasks/circle_packing/results/results_circle_packing_v2_1_gen100_periodic5_20260226_023318/gen_10/\", \"security_risk\": \"LOW\"}", "name": "terminal"}, "id": "call_e76285aa4ac9459aab6f1dc04d06__thought__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", "type": "function"}], "images": [], "reasoning_content": "Okay, here's what's going through my mind. I'm staring at a `FileNotFoundError` – the program can't find `extra.npz`. Specifically, it's looking in the `gen_10` directory under the results folder, which is where it *should* be. This is a problem because `extra.npz` is crucial for calculating the auxiliary metrics.\n\nI've checked the code, specifically the `auxiliary_metrics.py` script, and it expects `extra.npz` to reside within that `results_dir`. The `evaluate_aux` function *should* be correctly pointing to the right location. But the key thing is, *the error is still there.*\n\nMy concern stems from the fact that the `metrics.json` file for `gen_10` *does* contain auxiliary metric values. This means the `extra.npz` file was successfully loaded at some point during the initial metric calculation. That indicates either the environment, or the files themselves, has changed in some way *after* the initial run, and between then and now it has been moved or deleted.\n\nThe `auxiliary_metrics.py` has a `try-except` block, so it would return 0.0 for all metrics if `extra.npz` is not found, but I am not receiving zero values. This is unexpected.\n\nIt's likely either a cleanup script has run, or a separate process has interfered.\n\nMy current working directory is the `eval_agent_memory` directory, but the script is correctly trying to find `extra.npz` in the `gen_10` directory. The path is built using `os.path.join(results_dir, \"extra.npz\")`, so there is no apparent coding error.\n\nI need to confirm manually if that file is where it should be.\n", "thinking_blocks": [{"type": "thinking", "thinking": "Okay, here's what's going through my mind. I'm staring at a `FileNotFoundError` – the program can't find `extra.npz`. Specifically, it's looking in the `gen_10` directory under the results folder, which is where it *should* be. This is a problem because `extra.npz` is crucial for calculating the auxiliary metrics.\n\nI've checked the code, specifically the `auxiliary_metrics.py` script, and it expects `extra.npz` to reside within that `results_dir`. The `evaluate_aux` function *should* be correctly pointing to the right location. But the key thing is, *the error is still there.*\n\nMy concern stems from the fact that the `metrics.json` file for `gen_10` *does* contain auxiliary metric values. This means the `extra.npz` file was successfully loaded at some point during the initial metric calculation. That indicates either the environment, or the files themselves, has changed in some way *after* the initial run, and between then and now it has been moved or deleted.\n\nThe `auxiliary_metrics.py` has a `try-except` block, so it would return 0.0 for all metrics if `extra.npz` is not found, but I am not receiving zero values. This is unexpected.\n\nIt's likely either a cleanup script has run, or a separate process has interfered.\n\nMy current working directory is the `eval_agent_memory` directory, but the script is correctly trying to find `extra.npz` in the `gen_10` directory. The path is built using `os.path.join(results_dir, \"extra.npz\")`, so there is no apparent coding error.\n\nI need to confirm manually if that file is where it should be.\n"}], "provider_specific_fields": {"thought_signatures": ["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"]}}}], "usage": {"completion_tokens": 748, "prompt_tokens": 17089, "total_tokens": 17837, "completion_tokens_details": {"reasoning_tokens": 667, "text_tokens": 81}, "prompt_tokens_details": {"text_tokens": 17089}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}, "cost": 0.0069967, "timestamp": 1772074501.8894463, "latency_sec": 7.240424156188965, "usage_summary": {"prompt_tokens": 17089, "completion_tokens": 748, "reasoning_tokens": 667, "cache_read_tokens": 0}}