diff --git a/.gitattributes b/.gitattributes index d9f1aaa8ff70849b6abc371320993488b035f371..d2e8519cbe8c71f53139f0a7382c02b332a5ad1f 100644 --- a/.gitattributes +++ b/.gitattributes @@ -115,3 +115,4 @@ runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/assets/bucket_util runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/assets/chair_office_amf_black_a01/reconstruction-2/candidate/asset.usd filter=lfs diff=lfs merge=lfs -text runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/assets/accessory_eyeglasses_a01/reconstruction-2/candidate/asset.usd filter=lfs diff=lfs merge=lfs -text runs/nv25-gpt-6.1-sol-high-pi-none-turn64-20261020/assets/container_bottle_glass_a01/reconstruction-1/candidate/asset.usd filter=lfs diff=lfs merge=lfs -text +runs/nv25-gpt-6.1-sol-high-pi-none-turn64-20261020/assets/food_beverage_coffeeCup_a01/reconstruction-1/candidate/asset.usd filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 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b/runs/nv25-gpt-6.1-sol-high-pi-none-turn64-20261020/evidence/food_beverage_coffeeCup_a01/reconstruction-1/harbor-trajectory-4.json new file mode 100644 index 0000000000000000000000000000000000000000..322f4af29d3db8da898ed1d7d7f7407f7fa19074 --- /dev/null +++ b/runs/nv25-gpt-6.1-sol-high-pi-none-turn64-20261020/evidence/food_beverage_coffeeCup_a01/reconstruction-1/harbor-trajectory-4.json @@ -0,0 +1,2704 @@ +{ + "schema_version": "ATIF-v1.7", + "session_id": "01a15ba3-9720-76f4-857e-ae66a92261ea", + "agent": { + "name": "pi", + "version": "0.85.1", + "model_name": "harbor-endpoint/gpt-6.1-sol" + }, + "steps": [ + { + "step_id": 1, + "timestamp": "2026-10-20T23:26:52.200Z", + "source": "user", + "message": [ + { + "type": "text", + "text": "\nReconstruct the supplied object as /output/asset.usd.\nNominal dimensions in canonical metres XYZ: [0.08491601049900055, 0.08491601422429085, 0.11700878057696776]\nRead /task/contract.md, /task/tool-docs.md and /task/scoring-policy.json. Identify the rigid parts, joints and their motion before authoring, then reconstruct appearance and mechanical structure. Use Python/PXR. Do not use Blender. Validate and inspect the NVIDIA findings before submitting.\n" + }, + { + "type": "image", + "source": { + "media_type": "image/png", + "path": "images/pi_image_1.png" + } + } + ], + "extra": { + "pi_event_id": "6a9672a7", + "pi_parent_id": "a372faee" + } + }, + { + "step_id": 2, + "timestamp": "2026-10-20T23:26:56.078Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_rZ8f17P3Ep0XQaLd7BMcHLEz|fc_079820ae2e3eaa02016ad7f8bfcde887d2afc868d22c61ed48", + "function_name": "read", + "arguments": { + "path": "/task/contract.md", + "offset": 1, + "limit": 1000 + } + }, + { + "tool_call_id": "call_wFqRU1qO98cqWhcFVC4sDqeS|fc_079820ae2e3eaa02016ad7f8bfcdfc87d2930ff469e8e0c2ed", + "function_name": "read", + "arguments": { + "path": "/task/tool-docs.md", + "offset": 1, + "limit": 1000 + } + }, + { + "tool_call_id": "call_7htr9tdIa14RYTIRWQeQOijC|fc_079820ae2e3eaa02016ad7f8bfce0887d283a23a6d8b22e91f", + "function_name": "read", + "arguments": { + "path": "/task/scoring-policy.json", + "offset": 1, + "limit": 1000 + } + }, + { + "tool_call_id": "call_7gkYWvKEShmvdwKnXyUrCt85|fc_079820ae2e3eaa02016ad7f8bfce1087d2a32cef87be5e4256", + "function_name": "bash", + "arguments": { + "command": "ls -la /output; ls -la /task", + "timeout": 10 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_rZ8f17P3Ep0XQaLd7BMcHLEz|fc_079820ae2e3eaa02016ad7f8bfcde887d2afc868d22c61ed48", + "content": "# Reconstruction contract\n\nReconstruct the supplied object at /output/asset.usd using Python/PXR. Include\nits local USD and texture dependencies and place authoring scripts in /tmp.\nRead the supplied dimensions, image and scoring-policy.json.\n\nThe evaluation reports three independent scores:\n\n- Formal fidelity: NVIDIA SimReady-Foundation Robotics-Prop 4.0.0. Each of its\n 46 mandatory requirements counts once, with 95 * passed / 46 points and five\n further points when all pass. The 51 optional requirements are reported separately.\n- Visual fidelity: reference-scale shape, colours, textures and surface appearance.\n- Kinematic fidelity: the object's connected rigid parts, joint types, hinge or\n sliding axes, attachment locations and allowed ranges of motion.\n\nIdentify the object's parts and their mechanical relationships before authoring.\nKeep parts that move relative to one another as distinct rigid bodies and author\nthe corresponding UsdPhysics joints. Specify each joint's connected bodies,\nlocal positions, local orientations, axis and appropriate motion limits. For\nexample, folding eyewear needs hinged temples rather than one rigid mesh.\nUse fixed joints for genuine fixed attachments and keep genuinely rigid objects\nrigid. Base this structure on the supplied observations and the object's function.\n\nCheck that joint frames meet at each attachment, that movable parts can travel\nthrough their intended range and that the bodies and joints have the appropriate\nenabled and kinematic states. Preserve the observed shape and materials while\nconstructing this mechanical structure.\n\nUse validate_candidate to inspect the NVIDIA findings and repair the candidate.\nThe final submission freezes the authored files for evaluation.", + "extra": { + "tool_name": "read", + "is_error": false + } + }, + { + "source_call_id": "call_wFqRU1qO98cqWhcFVC4sDqeS|fc_079820ae2e3eaa02016ad7f8bfcdfc87d2930ff469e8e0c2ed", + "content": "# Authoring tools\n\nget_task_thumbnail returns the reference image. validate_candidate accepts {\"request_id\":\"check-1\"} and returns the current score, remaining validation calls and nvidia_validator_report.file_path. Search that JSON file with rg for a requirement ID or candidate prim path. get_validation_report reads a selected range of the same report.\n\nUse a new request ID after each repair. submit_asset accepts {\"request_id\":\"final\"} and stops authoring. The model has 64 inference requests, with provider-native output limits. Captures appear in the final report.", + "extra": { + "tool_name": "read", + "is_error": false + } + }, + { + "source_call_id": "call_7htr9tdIa14RYTIRWQeQOijC|fc_079820ae2e3eaa02016ad7f8bfce0887d283a23a6d8b22e91f", + "content": "{\n \"benchmark_output_token_cap\": null,\n \"composite_scores\": {\n \"formal\": \"SimReady Robotics-Prop mandatory requirements\",\n \"kinematic\": \"joint recovery and allowed relative motion correspondence\",\n \"visual\": \"geometric mean of shape and appearance material correspondence\"\n },\n \"limits\": {\n \"agent_seconds\": 231000,\n \"final_seconds\": 600,\n \"inference_requests\": 64,\n \"preview_requests\": 12,\n \"preview_seconds\": 90,\n \"validation_calls\": 5,\n \"validation_seconds\": 120,\n \"views_per_preview\": 4\n },\n \"model_call_budget\": 64,\n \"reward_reporting\": {\n \"formula\": \"95 * passed / fixed_applicable + 5_if_all_pass\",\n \"harbor_key\": \"simready_reward\",\n \"scale\": [\n 0,\n 100\n ]\n },\n \"semantic_labels\": {\n \"criterion\": \"mandatory NVIDIA SL IDs only\",\n \"required\": true,\n \"scoring\": true\n },\n \"validator_scoring\": {\n \"all_pass_bonus_percent\": 5,\n \"applicability\": \"fixed_profile_and_task_before_candidate\",\n \"applicable_requirement_ids\": [\n \"com.nvidia.simready.HI.010\",\n \"com.nvidia.simready.NP.002\",\n \"com.nvidia.simready.NP.003\",\n \"com.nvidia.simready.NP.004\",\n \"com.nvidia.simready.NP.005\",\n \"com.nvidia.simready.NP.007\",\n \"com.nvidia.simready.NP.008\",\n \"com.nvidia.simready.RB.001\",\n \"com.nvidia.simready.RB.007\",\n \"com.nvidia.simready.RB.010\",\n \"com.nvidia.simready.RB.COL.001\",\n \"com.nvidia.simready.RB.COL.002\",\n \"com.nvidia.simready.RB.COL.003\",\n \"com.nvidia.simready.SL.001\",\n \"com.nvidia.simready.SL.002\",\n \"com.nvidia.simready.SL.003\",\n \"com.nvidia.simready.SL.MAT.001\",\n \"com.nvidia.simready.SL.QCODE.001\",\n \"com.nvidia.simready.SL.TIME.001\",\n \"com.nvidia.simready.SR.002\",\n \"com.nvidia.simready.SR.004\",\n \"com.nvidia.simready.UN.006\",\n \"com.nvidia.simready.UN.007\",\n \"com.nvidia.simready.VG.008\",\n \"com.nvidia.simready.VG.025\",\n \"com.nvidia.simready.VG.MESH.001\",\n \"com.nvidia.simready.VM.MAT.001\",\n \"com.nvidia.simready.VM.PS.002\",\n \"com.nvidia.simready.VM.TEX.001\",\n \"com.nvidia.simready.VM.TEX.003\",\n \"com.nvidia.usd.AA.001\",\n \"com.nvidia.usd.AA.002\",\n \"com.nvidia.usd.HI.001\",\n \"com.nvidia.usd.HI.003\",\n \"com.nvidia.usd.HI.004\",\n \"com.nvidia.usd.RB.003\",\n \"com.nvidia.usd.RB.005\",\n \"com.nvidia.usd.RB.009\",\n \"com.nvidia.usd.RB.COL.004\",\n \"com.nvidia.usd.VG.002\",\n \"com.nvidia.usd.VG.014\",\n \"com.nvidia.usd.VG.027\",\n \"com.nvidia.usd.VG.028\",\n \"com.nvidia.usd.VG.029\",\n \"com.nvidia.usd.VM.BIND.001\",\n \"com.nvidia.usd.VM.PS.001\"\n ],\n \"base_credit_percent\": 95,\n \"early_stop_penalty_percent\": 0,\n \"optional_requirements\": \"report_only\",\n \"policy_id\": \"NVIDIA-Robotics-Prop-4.0.0-mandatory-95-plus-5-v1\",\n \"process_shaping_percent\": 0,\n \"profile\": {\n \"id\": \"Robotics-Prop\",\n \"version\": \"4.0.0\"\n },\n \"profile_inventory_sha256\": \"5a8dccd559cd3625025cedd08f7e2c174df32b8bbe0b17da9c94a588c86da13c\",\n \"report_only_mandatory_ids\": [],\n \"schema\": \"simready-validator-scoring-policy-v1\",\n \"scope\": \"all_mandatory_profile\",\n \"scoring_unit\": \"one_mandatory_requirement_ID\",\n \"stop_bonus_percent\": 0,\n \"unknown\": \"null_score_fixed_denominator\"\n }\n}", + "extra": { + "tool_name": "read", + "is_error": false + } + }, + { + "source_call_id": "call_7gkYWvKEShmvdwKnXyUrCt85|fc_079820ae2e3eaa02016ad7f8bfce1087d2a32cef87be5e4256", + "content": "total 0\ndrwxrwxrwx 2 actor actor 40 Oct 20 23:26 .\ndrwxr-xr-x 1 root root 56 Oct 20 23:26 ..\ntotal 948\ndr-xr-xr-x 2 actor actor 160 Oct 20 23:26 .\ndrwxr-xr-x 1 root root 56 Oct 20 23:26 ..\n-r--r--r-- 1 actor actor 1746 Oct 20 23:26 contract.md\n-r--r--r-- 1 actor actor 454 Oct 20 23:26 instruction.md\n-r--r--r-- 1 actor actor 3305 Oct 20 23:26 scoring-policy.json\n-r--r--r-- 1 actor actor 435 Oct 20 23:26 simready-bench-native-output-policy.ts\n-r--r--r-- 1 actor actor 949210 Oct 20 23:26 thumbnail.png\n-r--r--r-- 1 actor actor 572 Oct 20 23:26 tool-docs.md", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 2668, + "completion_tokens": 128, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 2665, + "reasoning_tokens": 0, + "pi_total_tokens": 2796 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "b4296d26", + "pi_parent_id": "6a9672a7", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_079820ae2e3eaa02016ad7f8bcf12c87d2ad468e80117bedef", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 3, + "timestamp": "2026-10-20T23:26:59.497Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "reasoning_content": "**Recreating a Coffee Cup**\n\nI\u2019m thinking about recreating a coffee cup and that I need to gather some information about the tools I'll need. It's important to make sure I have everything ready before I get started. I wonder what materials might provide the best results and whether I should focus on specific design features. Getting this right will be essential for ensuring a great outcome. So, let's dig into what tools will work best for this project!", + "tool_calls": [ + { + "tool_call_id": "call_vdds8iC2wmcWcuBSZYgy5xO8|fc_079820ae2e3eaa02016ad7f8c3240887d292b5413b76c1e07f", + "function_name": "mcp", + "arguments": { + "server": "benchmark" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_vdds8iC2wmcWcuBSZYgy5xO8|fc_079820ae2e3eaa02016ad7f8c3240887d292b5413b76c1e07f", + "content": "benchmark (4 tools):\n\n- benchmark_get_task_thumbnail - Read the supplied task thumbnail and public brief.\n- benchmark_validate_candidate - Validate a frozen candidate and return NVIDIA...\n- benchmark_submit_asset - End authoring and freeze the submitted asset....\n- benchmark_get_validation_report - Read a requested range of a prior NVIDIA report....", + "extra": { + "tool_name": "mcp", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 4574, + "completion_tokens": 33, + "cached_tokens": 2665, + "extra": { + "cache_write_tokens": 1906, + "reasoning_tokens": 13, + "pi_total_tokens": 4607 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "525a6e6a", + "pi_parent_id": "eb35fa3b", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_079820ae2e3eaa02016ad7f8c0b2e487d2af6987ed33b37bd3", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 4, + "timestamp": "2026-10-20T23:27:03.805Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_L3Ij1BciDgoD4bJg2T1MxhtA|fc_079820ae2e3eaa02016ad7f8c590d887d29aa0e9da69549d23", + "function_name": "bash", + "arguments": { + "command": "python - <<'PY'\nfrom pxr import Usd, UsdGeom, UsdPhysics, UsdShade\nprint('USD available')\nimport importlib.util\nfor m in ['PIL','numpy','scipy','cv2']:\n print(m,importlib.util.find_spec(m))\nPY\nls -la /; which usdchecker; find / -iname '*simready*' -o -iname '*validator*' 2>/dev/null | head -35", + "timeout": 20 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_L3Ij1BciDgoD4bJg2T1MxhtA|fc_079820ae2e3eaa02016ad7f8c590d887d29aa0e9da69549d23", + "content": "USD available\nPIL ModuleSpec(name='PIL', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7d4733e7c200>, origin='/usr/local/lib/python3.12/dist-packages/PIL/__init__.py', submodule_search_locations=['/usr/local/lib/python3.12/dist-packages/PIL'])\nnumpy ModuleSpec(name='numpy', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7d474e14d0a0>, origin='/usr/local/lib/python3.12/dist-packages/numpy/__init__.py', submodule_search_locations=['/usr/local/lib/python3.12/dist-packages/numpy'])\nscipy None\ncv2 None\ntotal 0\ndrwxr-xr-x 1 root root 56 Oct 20 23:26 .\ndrwxr-xr-x 1 root root 56 Oct 20 23:26 ..\n-rwxr-xr-x 1 root root 0 Oct 20 23:26 .dockerenv\nlrwxrwxrwx 1 root root 7 Apr 22 2024 bin -> usr/bin\ndrwxr-xr-x 2 root root 6 Apr 22 2024 boot\ndrwxr-xr-x 5 root root 340 Oct 20 23:26 dev\ndrwxr-xr-x 1 root root 86 Oct 20 23:26 etc\ndrwxr-xr-x 1 root root 27 Oct 20 22:14 home\ndrwxr-xr-x 1 actor actor 6 Oct 20 22:14 installed-agent\nlrwxrwxrwx 1 root root 7 Apr 22 2024 lib -> usr/lib\ndrwxr-xr-x 2 root root 10 Oct 2 2024 lib.usr-is-merged\nlrwxrwxrwx 1 root root 9 Apr 22 2024 lib64 -> usr/lib64\ndrwxr-xr-x 4 root root 48 Oct 20 23:26 logs\ndrwxr-xr-x 2 root root 6 Sep 30 14:05 media\ndrwxr-xr-x 2 root root 6 Sep 30 14:05 mnt\ndrwxr-xr-x 1 root root 30 Oct 20 22:14 opt\ndrwxrwxrwx 2 actor actor 40 Oct 20 23:26 output\ndr-xr-xr-x 18121 root root 0 Oct 20 23:26 proc\ndrwx------ 1 root root 26 Oct 20 22:14 root\ndrwxr-xr-x 1 root root 39 Oct 20 23:26 run\nlrwxrwxrwx 1 root root 8 Apr 22 2024 sbin -> usr/sbin\ndrwxr-xr-x 2 root root 6 Sep 30 14:05 srv\ndr-xr-xr-x 13 nobody nogroup 0 Jun 18 02:45 sys\ndr-xr-xr-x 2 actor actor 160 Oct 20 23:26 task\ndrwxrwxrwt 1 root root 115 Oct 20 23:26 tmp\ndrwxr-xr-x 1 root root 19 Sep 30 14:05 usr\ndrwxr-xr-x 1 root root 57 Sep 30 14:12 var\n/opt/simready\n/opt/simready/runtime/simready_bench\n/opt/simready/pi/node_modules/@earendil-works/pi-coding-agent/node_modules/fast-xml-parser/lib/fxvalidator.min.js\n/opt/simready/pi/node_modules/@earendil-works/pi-coding-agent/node_modules/fast-xml-parser/lib/fxvalidator.min.js.map\n/opt/simready/pi/node_modules/@earendil-works/pi-coding-agent/node_modules/fast-xml-parser/src/validator.js\n/opt/simready/pi/node_modules/@earendil-works/pi-coding-agent/node_modules/json-schema-to-ts/lib/cjs/utils/type-guards/validator.js\n/opt/simready/pi/node_modules/@earendil-works/pi-coding-agent/node_modules/json-schema-to-ts/lib/cjs/utils/type-guards/validator.js.map\n/opt/simready/pi/node_modules/@earendil-works/pi-coding-agent/node_modules/json-schema-to-ts/lib/esm/utils/type-guards/validator.js\n/opt/simready/pi/node_modules/@earendil-works/pi-coding-agent/node_modules/json-schema-to-ts/lib/esm/utils/type-guards/validator.js.map\n/opt/simready/pi/node_modules/@earendil-works/pi-coding-agent/node_modules/json-schema-to-ts/lib/types/utils/type-guards/validator.d.ts\n/opt/simready/pi/node_modules/@earendil-works/pi-coding-agent/node_modules/json-schema-to-ts/lib/types/utils/type-guards/validator.js\n/opt/simready/pi/node_modules/@earendil-works/pi-coding-agent/node_modules/typebox/build/compile/validator.d.mts\n/opt/simready/pi/node_modules/@earendil-works/pi-coding-agent/node_modules/typebox/build/compile/validator.mjs\n/opt/simready/pi/node_modules/json-schema-to-ts/lib/cjs/utils/type-guards/validator.js\n/opt/simready/pi/node_modules/json-schema-to-ts/lib/cjs/utils/type-guards/validator.js.map\n/opt/simready/pi/node_modules/json-schema-to-ts/lib/esm/utils/type-guards/validator.js\n/opt/simready/pi/node_modules/json-schema-to-ts/lib/esm/utils/type-guards/validator.js.map\n/opt/simready/pi/node_modules/json-schema-to-ts/lib/types/utils/type-guards/validator.d.ts\n/opt/simready/pi/node_modules/json-schema-to-ts/lib/types/utils/type-guards/validator.js\n/opt/simready/pi/node_modules/typebox/build/compile/validator.d.mts\n/opt/simready/pi/node_modules/typebox/build/compile/validator.mjs\n/opt/nvm/versions/node/v22.23.3/lib/node_modules/npm/node_modules/@pkgjs/parseargs/internal/validators.js\n/run/simready-services\n/tmp/simready-relay.ready\n/tmp/jiti/task-simready-bench-native-output-policy.131690ad.mjs\n/tmp/jiti/pi-mcp-adapter-json-schema-validator.33d7a706.mjs\n/tmp/harbor-pi-mcp-adapter/node_modules/@modelcontextprotocol/client/dist/validators\n/tmp/harbor-pi-mcp-adapter/node_modules/hono/dist/cjs/validator\n/tmp/harbor-pi-mcp-adapter/node_modules/hono/dist/cjs/validator/validator.js\n/tmp/harbor-pi-mcp-adapter/node_modules/hono/dist/types/validator\n/tmp/harbor-pi-mcp-adapter/node_modules/hono/dist/types/validator/validator.d.ts\n/tmp/harbor-pi-mcp-adapter/node_modules/hono/dist/validator\n/tmp/harbor-pi-mcp-adapter/node_modules/hono/dist/validator/validator.js\n/tmp/harbor-pi-mcp-adapter/node_modules/json-schema-to-ts/lib/cjs/utils/type-guards/validator.js\n/tmp/harbor-pi-mcp-adapter/node_modules/json-schema-to-ts/lib/cjs/utils/type-guards/validator.js.map", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 4681, + "completion_tokens": 125, + "cached_tokens": 4571, + "extra": { + "cache_write_tokens": 107, + "reasoning_tokens": 0, + "pi_total_tokens": 4806 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "85e0ad0a", + "pi_parent_id": "9631f72a", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_079820ae2e3eaa02016ad7f8c47a2c87d2bc804054e205679b", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 5, + "timestamp": "2026-10-20T23:27:36.917Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "The object is a tapered paper coffee cup with a kraft-paper sleeve and a black snap-on drinking lid. I\u2019ll model the cup and sleeve as one rigid assembly, with the lid as a separate rigid part fixed at its snap-fit seat. There are no visible hinges or sliding mechanisms.", + "reasoning_content": "**Designing a pattern**\n\nI'm focusing on creating a strong, textured appearance for coffee bags, cups, and beans, in brown and cream. I think I can create a hand-drawn or procedural seamless pattern that captures this idea. I wonder if I can leverage the supplied thumbnail to extract the actual label texture. The process may involve using inverse projection techniques and mapping\u2014sounds a bit technical, but it should help create the desired effect!\n**Exploring texture extraction for design**\n\nI'm considering using photo texture extraction to capture the cream pattern on a cup and the sleeve independently. I need to avoid baked shading, even though there\u2019s a slight presence in the image. The idea is to generate a detailed repeating pattern featuring coffee beans and cups while correcting the geometry. I'll also look into camera mapping for rectification, using the 949kb PNG as my texture source. Plus, I want to model the camera fit and project known radii with perspective.\n\n**Estimating camera perspective and elevation**\n\nI'm estimating an orthographic elevation of about 42 degrees, which makes the top of the cup appear larger than the bottom\u2014this is partly due to perspective. Considering dimensions versus pixel shape, the overall pixel height is 921 and width is 620, resulting in a height-to-diameter ratio of about 1.38. I found that the projection width and ratios suggest a height reduction from the viewpoint. Later, I'll calculate the ellipse details to understand the cup\u2019s base and outer ring dimensions better, determining how the camera's distance changes the elevation.\n\n**Fitting camera and frustum radii**\n\nI\u2019m thinking about using a manual landmarks numerical optimizer to fit the camera and frustum radii, possibly by implementing gradient descent or using SciPy. I have access to NumPy, so I wonder if I should install SciPy, or if I even need to use Torch for this. I\u2019ll likely consider using a least squares method for a custom approach. The goal is to project the cup parameters for unwrapping and visual text effectively.", + "tool_calls": [ + { + "tool_call_id": "call_o7WbrzuvTyQW8NkodILdTciI|fc_079820ae2e3eaa02016ad7f8e8989087d284f815bb226c7668", + "function_name": "mcp", + "arguments": { + "describe": "benchmark_validate_candidate" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_o7WbrzuvTyQW8NkodILdTciI|fc_079820ae2e3eaa02016ad7f8e8989087d284f815bb226c7668", + "content": "benchmark_validate_candidate\nServer: benchmark\n\nValidate a frozen candidate and return NVIDIA findings. Each new request consumes one validation call. Repeating a request ID returns its existing result.\n\nShape:\n{ request_id: string; }", + "extra": { + "tool_name": "mcp", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 6489, + "completion_tokens": 1120, + "cached_tokens": 4678, + "extra": { + "cache_write_tokens": 1808, + "reasoning_tokens": 1034, + "pi_total_tokens": 7609 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "fe763786", + "pi_parent_id": "68b1c0c3", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_079820ae2e3eaa02016ad7f8cb76a087d292d8e8f190655c79", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 6, + "timestamp": "2026-10-20T23:27:50.989Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "reasoning_content": "**Examining texture extraction**\n\nI need to gather metadata right after the initial validation to build a robust stage. I'll explore extracting textures from references while ensuring no difference in the image. We can fully unwarp the cup while maintaining the texture and label visibility. I also need to fit the camera so the procedural structure aligns properly. Then, I\u2019ll create a PNG texture by mapping the body and sleeve. The camera coordinates will help with the fitting process for accurate results.\n\n**Analyzing cup dimensions**\n\nI\u2019m looking at the cup's geometry, focusing on the body and its center axis, which appears around x=512. The silhouette bottom is estimated at y=954, indicating it's below the center. The top ring might be at (512, 245), while the bottom could be around (512, 874). I've made various estimates for locations and silhouettes, including points for the sleeve, lid, and different edges. Overall, I'm trying to fit these visible points accurately for a complete understanding of the cup's structure.", + "tool_calls": [ + { + "tool_call_id": "call_vPuLJ3DeMEYNYBNfkHyFwbME|fc_079820ae2e3eaa02016ad7f8f69e1887d2865ca22f2b3cd4a6", + "function_name": "bash", + "arguments": { + "command": "pip -q install scipy", + "timeout": 120 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_vPuLJ3DeMEYNYBNfkHyFwbME|fc_079820ae2e3eaa02016ad7f8f69e1887d2865ca22f2b3cd4a6", + "content": "/bin/bash: line 1: pip: command not found\n\n\nCommand exited with code 127", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 7664, + "completion_tokens": 544, + "cached_tokens": 6486, + "extra": { + "cache_write_tokens": 1175, + "reasoning_tokens": 516, + "pi_total_tokens": 8208 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "a2023c04", + "pi_parent_id": "b479b20a", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_079820ae2e3eaa02016ad7f8e9953487d2ab09e7700b4d04ac", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 7, + "timestamp": "2026-10-20T23:28:07.629Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_gFZ0gibi8Ev2aNhpmT9LTnyV|fc_079820ae2e3eaa02016ad7f900f6c487d2b4f060b845da79bb", + "function_name": "write", + "arguments": { + "path": "/tmp/fit.py", + "content": "import numpy as np\nfrom math import sin,cos\n# camera aimed along z plane; cy is projected origin\nt=np.linspace(0,2*np.pi,1440)\ndef project(p,r,z):\n e,d,f,cy,rb,rt=p\n x=r*np.sin(t);y=-r*np.cos(t); D=d+y*np.cos(e)-z*np.sin(e)\n return 512+f*x/D,cy-f*(y*np.sin(e)+z*np.cos(e))/D\n\ndef residual(p):\n e,d,f,cy,rb,rt=p\n if not(.3.13 and .02 /tmp/fitfixed.py <<'PY'\nexec(open('/tmp/fit.py').read().split('p=np.array')[0])\nfor d in [.2,.3,.5,1,10]:\n p=np.array([.8,d,d*6600,-.05,.03,.035])\n inds=[0,2,3,4,5]\n for it in range(100):\n r=residual(p);eps=np.array([1e-5,1e-5,.01,1e-6,1e-6,1e-6]);J=np.stack([(residual(p+np.eye(6)[j]*eps[j])-r)/eps[j] for j in inds],axis=1)\n de=np.zeros(6);de[inds]=np.linalg.lstsq(J,-r,rcond=None)[0]\n for s in [1,.5,.2,.1,.01]:\n pn=p+de*s\n if np.linalg.norm(residual(pn)).0765)[...,None],up,mid))\ncol=np.clip(col*1.09+3,0,255).astype('uint8')\nImage.fromarray(col).save(out/'coffee_print.png')\n# Cardboard sleeve front in a wider undistorted cylindrical panel. Mirror on\n# the unseen reverse rather than introducing a discontinuity in the design.\nW,H=2048,1024\nu=(np.arange(W)+.5)/W;v=1-(np.arange(H)+.5)/H\nth=(u-.5)*2*np.pi\n# front 128 degrees, reverse receives a repeat, with seams under the back edge\nang=(th+np.pi/2)%np.pi-np.pi/2\nang=np.where(ang>1.14,2.28-ang,ang);ang=np.where(ang< -1.14,-2.28-ang,ang)\nA=np.tile(ang,(H,1));T=np.tile(v[:,None],(1,W))\nR=216+(258-216)*T;E=117+1*T\nfront=850-230*T\ncol=sample(512+R*np.sin(A),front-E*(1-np.cos(A)))\n# Lessen source directional illumination, preserving real printed fibres.\ncol=np.clip(col*1.09+2,0,255).astype('uint8')\nImage.fromarray(col).save(out/'kraft_label.png')\n# tiny isotropic paper-fibre normal microtexture represented as roughness\nrng=np.random.default_rng(143)\nnoise=np.clip(rng.normal(220,7,(512,512)),0,255).astype('uint8')\nImage.fromarray(noise,'L').save(out/'paper_roughness.png')\n# contact sheet for author inspection\nImage.open(out/'kraft_label.png').resize((1024,512)).save('/tmp/label_preview.jpg')\nImage.open(out/'coffee_print.png').resize((1024,512)).save('/tmp/print_preview.jpg')\n" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_vps0Gc5AzOI2B1CpUPZDxryi|fc_079820ae2e3eaa02016ad7f9a664b487d2b286cfe809cde93e", + "content": "Successfully wrote to /tmp/make_textures.py", + "extra": { + "tool_name": "write", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 17862, + "completion_tokens": 1244, + "cached_tokens": 17577, + "extra": { + "cache_write_tokens": 282, + "reasoning_tokens": 281, + "pi_total_tokens": 19106 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "114a5a1a", + "pi_parent_id": "71350ee2", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_079820ae2e3eaa02016ad7f99d814087d2ab93be3a10766a70", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + 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"path": "/tmp/build_asset.py", + "content": "from pxr import Usd, UsdGeom, UsdShade, UsdPhysics, UsdSemantics, Sdf, Gf, Vt, Kind\nimport numpy as np, math, os\nOUT='/output/asset.usd'\nstage=Usd.Stage.CreateNew(OUT)\nUsdGeom.SetStageUpAxis(stage,UsdGeom.Tokens.z);UsdGeom.SetStageMetersPerUnit(stage,1.0)\nstage.SetStartTimeCode(0);stage.SetEndTimeCode(0);stage.SetTimeCodesPerSecond(24)\nroot=UsdGeom.Xform.Define(stage,'/CoffeeCup').GetPrim();stage.SetDefaultPrim(root)\nUsd.ModelAPI(root).SetKind(Kind.Tokens.component)\nroot.SetAssetInfoByKey('name','Printed takeaway coffee cup')\nroot.SetAssetInfoByKey('identifier',Sdf.AssetPath('./asset.usd'))\nroot.SetCustomDataByKey('description','Tapered printed paper cup, fitted kraft sleeve, and snap-on polypropylene drinking lid. SI metres; assembled pose.')\nroot.SetCustomDataByKey('mechanicalStructure','Paper cup and sleeve form one rigid assembly. The removable snap-fit lid is fixed at its rim in the assembled state; no actuated or hinged degrees of freedom.')\n\ndef labels(prim,lab,material=None):\n UsdSemantics.LabelsAPI.Apply(prim,'class').CreateLabelsAttr([lab])\n if material: UsdSemantics.LabelsAPI.Apply(prim,'material').CreateLabelsAttr([material])\nlabels(root,'coffee_cup')\nlooks=UsdGeom.Scope.Define(stage,'/CoffeeCup/Looks')\ndef material(name,color,roughness,tex=None,metal=0):\n m=UsdShade.Material.Define(stage,'/CoffeeCup/Looks/'+name)\n s=UsdShade.Shader.Define(stage,m.GetPath().AppendChild('Surface'));s.CreateIdAttr('UsdPreviewSurface')\n s.CreateInput('diffuseColor',Sdf.ValueTypeNames.Color3f).Set(Gf.Vec3f(*color));s.CreateInput('roughness',Sdf.ValueTypeNames.Float).Set(roughness)\n s.CreateInput('metallic',Sdf.ValueTypeNames.Float).Set(metal)\n s.CreateInput('ior',Sdf.ValueTypeNames.Float).Set(1.46)\n s.CreateInput('opacity',Sdf.ValueTypeNames.Float).Set(1.)\n s.CreateOutput('surface',Sdf.ValueTypeNames.Token)\n if tex:\n uv=UsdShade.Shader.Define(stage,m.GetPath().AppendChild('UV'));uv.CreateIdAttr('UsdPrimvarReader_float2');uv.CreateInput('varname',Sdf.ValueTypeNames.Token).Set('st');uv.CreateOutput('result',Sdf.ValueTypeNames.Float2)\n t=UsdShade.Shader.Define(stage,m.GetPath().AppendChild('Print'));t.CreateIdAttr('UsdUVTexture');t.CreateInput('file',Sdf.ValueTypeNames.Asset).Set(Sdf.AssetPath('./textures/'+tex));t.CreateInput('st',Sdf.ValueTypeNames.Float2).ConnectToSource(uv.ConnectableAPI(),'result')\n t.CreateInput('sourceColorSpace',Sdf.ValueTypeNames.Token).Set('sRGB');t.CreateInput('wrapS',Sdf.ValueTypeNames.Token).Set('repeat');t.CreateInput('wrapT',Sdf.ValueTypeNames.Token).Set('clamp');t.CreateInput('fallback',Sdf.ValueTypeNames.Float4).Set(Gf.Vec4f(*color,1))\n t.CreateOutput('rgb',Sdf.ValueTypeNames.Float3);s.GetInput('diffuseColor').ConnectToSource(t.ConnectableAPI(),'rgb')\n m.CreateSurfaceOutput().ConnectToSource(s.ConnectableAPI(),'surface')\n return m\npaper=material('PrintedPaper',(0.72,.55,.32),.77,'coffee_print.png')\nkraft=material('PrintedKraft',(0.64,.43,.22),.87,'kraft_label.png')\nedge=material('KraftCutEdge',(.44,.29,.14),.88)\nivory=material('PaperInterior',(.85,.78,.61),.76)\nblack=material('BlackPolypropylene',(.012,.013,.014),.33)\nblackmatte=material('BlackRecess',(.010,.011,.012),.46)\nholemat=material('ApertureInterior',(.0004,.0004,.0004),.72)\n\ndef lathe(path,profile,mat,n=256,uvz=None,offset=0,skip=None):\n # profile follows exterior bottom -> upper -> interior -> bottom, for outward normals.\n pts=[];uv=[]\n for r,z in profile:\n for i in range(n+1):\n th=-math.pi+2*math.pi*i/n\n pts.append((r*math.sin(th),-r*math.cos(th),z-offset))\n uv.append((i/n,(z-uvz[0])/(uvz[1]-uvz[0]) if uvz else z/.11700878057696776))\n counts=[];indices=[]\n for j in range(len(profile)-1):\n for i in range(n):\n if skip and skip(j,i):continue\n a=j*(n+1)+i;b=a+1;c=b+n+1;d=a+n+1\n counts.append(4);indices.extend([a,b,c,d])\n mesh=UsdGeom.Mesh.Define(stage,path);mesh.CreatePointsAttr(pts);mesh.CreateFaceVertexCountsAttr(counts);mesh.CreateFaceVertexIndicesAttr(indices);mesh.CreateSubdivisionSchemeAttr('none');mesh.CreateOrientationAttr('rightHanded');mesh.CreateDoubleSidedAttr(False)\n mesh.CreateExtentAttr(UsdGeom.PointBased(mesh.GetPrim()).ComputeExtent(pts))\n pv=UsdGeom.PrimvarsAPI(mesh).CreatePrimvar('st',Sdf.ValueTypeNames.TexCoord2fArray,UsdGeom.Tokens.vertex);pv.Set(uv)\n # Analytic ring-smoothed normals, with profile rounding authored explicitly.\n normals=[]\n for j,(r,z) in enumerate(profile):\n i0=max(0,j-1);i1=min(len(profile)-1,j+1)\n dr=profile[i1][0]-profile[i0][0];dz=profile[i1][1]-profile[i0][1]\n norm=math.hypot(dr,dz) or 1\n for i in range(n+1):\n th=-math.pi+2*math.pi*i/n\n normals.append((dz*math.sin(th)/norm,-dz*math.cos(th)/norm,-dr/norm))\n mesh.CreateNormalsAttr(normals);mesh.SetNormalsInterpolation('vertex')\n if mat:UsdShade.MaterialBindingAPI.Apply(mesh.GetPrim()).Bind(mat)\n return mesh\n\ndef body(path,mass,com,inertia,z=0):\n x=UsdGeom.Xform.Define(stage,path)\n if z:x.AddTranslateOp().Set((0,0,z))\n p=x.GetPrim();r=UsdPhysics.RigidBodyAPI.Apply(p);r.CreateRigidBodyEnabledAttr(True);r.CreateKinematicEnabledAttr(False)\n m=UsdPhysics.MassAPI.Apply(p);m.CreateMassAttr(mass);m.CreateCenterOfMassAttr(Gf.Vec3f(0,0,com));m.CreateDiagonalInertiaAttr(Gf.Vec3f(*inertia));m.CreatePrincipalAxesAttr(Gf.Quatf(1))\n return p\nB=body('/CoffeeCup/Body',.019,.050,(2.3e-5,2.3e-5,1.85e-5));labels(B,'paper_cup','paper')\nL=body('/CoffeeCup/Lid',.004,.007,(1.9e-6,1.9e-6,3.6e-6),.103);labels(L,'drinking_lid','polypropylene')\n# Continuous thin cup shell, inset bottom, and rolled mouth.\nprof=[(.00001,.0014),(.0248,.0014),(.0266,.0001),(.0271,0),(.02745,.0004),(.02765,.0012),(.02775,.0022),(.0368,.1009),(.03715,.1013),(.03735,.1019),(.03735,.1025),(.0370,.1031),(.0365,.1034),(.0360,.1032),(.03565,.1027),(.03565,.1019),(.0266,.0032),(.0261,.0028),(.00001,.0028)]\ncup=lathe('/CoffeeCup/Body/PrintedWall',prof,paper,uvz=(0,.1035))\n# Distinct, thin conical insulating sleeve follows the taper closely.\ndef cupR(z):return .02755+(.0368-.02755)*(z-.0012)/(.1009-.0012)\nz0,z1=.027,.0775;r0,r1=cupR(z0)+.00050,cupR(z1)+.0005\nsleeveprof=[(r0-.00035,z0+.00015),(r0-.00012,z0),(r0,z0+.00015),(r0,z0+.0005),(r1,z1-.00035),(r1-.00002,z1),(r1-.00020,z1+.00004),(r1-.00040,z1-.00015),(r0-.00035,z0+.00015)]\nsleeve=lathe('/CoffeeCup/Body/KraftSleeve',sleeveprof,kraft,uvz=(z0,z1));labels(sleeve.GetPrim(),'cup_sleeve','kraft_paper')\n# A fine visible cardboard edge beneath and above the sleeve.\nfor name,z,r in [('SleeveUpperEdge',z1,r1),('SleeveLowerEdge',z0,r0)]:\n lathe('/CoffeeCup/Body/'+name,[(r-.00033,z-.00004),(r-.0001,z-.00004),(r,z+.00002),(r-.00028,z+.00010)],edge,uvz=(0,.1035))\n# Rolled, stepped injection-moulded lid. It is hollow underneath.\nP=[(.0375,.1010),(.0403,.1010),(.0415,.1014),(.04215,.1021),(.04245800525,.1028),(.04245800525,.10345),(.04225,.1041),(.0418,.10465),(.0407,.1050),(.0392,.1050),(.0388,.10545),(.03845,.1063),(.0372,.1127),(.0368,.1140),(.03605,.1152),(.0352,.1162),(.0344,.1168),(.0335,.11700878058),(.0312,.11700878058),(.0294,.1169),(.0288,.1166),(.02825,.1160),(.02810,.1153),(.02795,.1146),(.02765,.11430),(.0268,.1142),(.00001,.1142),(.00001,.1135),(.0268,.1135),(.0270,.11355),(.0273,.1138),(.0275,.1145),(.0277,.1158),(.0294,.1161),(.0333,.1162),(.0341,.1160),(.0354,.1145),(.0364,.1125),(.0378,.1054),(.0381,.1044),(.0375,.1035),(.0375,.1010)]\nN=256\n# Drinking aperture is cut through the raised annulus, not merely painted.\nimin=round((-2.08+math.pi)/(2*math.pi)*N);imax=round((-1.83+math.pi)/(2*math.pi)*N)\ndef skip(j,i):return (j==17 and imin<=i