diff --git a/README.md b/README.md index 8eb75681c256673df02fb8754151b688aa9f0fd3..a5ce4f1393d3f6e03619d8a3483ff8649cf54f73 100644 --- a/README.md +++ b/README.md @@ -2170,6 +2170,7 @@ configs: - split: box_cardboard_open_a01 path: - viewer/nv25-gpt-6.1-sol-high-pi-none-turn64-20261020/by_asset/box_cardboard_open_a01.parquet + - viewer/nv25-qwen38-27b-medium-pi-none-turn64-20261021/by_asset/box_cardboard_open_a01.parquet - split: bucket_utility_red_a01 path: - viewer/nv25-gpt-6.1-sol-high-pi-none-turn64-20261020/by_asset/bucket_utility_red_a01.parquet diff --git a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/asset.usd b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/asset.usd new file mode 100644 index 0000000000000000000000000000000000000000..49be8a62841f0b6cbda40ca1c1494c518cdaa4ee Binary files /dev/null and b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/asset.usd differ diff --git a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/manifest.json b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6596c26c72bac5693173a06e46e3f2da69de915b --- /dev/null +++ b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/manifest.json @@ -0,0 +1,24 @@ +{ + "copied_independently": true, + "entrypoint": "asset.usd", + "files": [ + { + "relative_path": "asset.usd", + "sha256": "402eff31f89f535216709a98ae2464d016eb5d0791da26bd149413695f567f5f", + "size_bytes": 492 + }, + { + "relative_path": "textures/wood_basecolor.png", + "sha256": "17b75143e72575a759f65da70714db653c9306eca9a80a96d7d01188d2057a04", + "size_bytes": 1458048 + }, + { + "relative_path": "textures/wood_roughness.png", + "sha256": "bb625e9db13ccf0524f6a35b11fc9f056d71a2bdb5ee973c5241e94dd39d396d", + "size_bytes": 647995 + } + ], + "schema": "simready-bench-submission-v1", + "size_bytes": 2106535, + "snapshot_sha256": "ae5b2f689754fc0fdc9ef0791006c2551e2c5d15cc6858f0aa7cf0f7b6a17774" +} diff --git a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/textures/wood_basecolor.png b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/textures/wood_basecolor.png new file mode 100644 index 0000000000000000000000000000000000000000..586033a4853db51170e31b8537d1068660102f56 --- /dev/null +++ b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/textures/wood_basecolor.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:17b75143e72575a759f65da70714db653c9306eca9a80a96d7d01188d2057a04 +size 1458048 diff --git a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/textures/wood_roughness.png b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/textures/wood_roughness.png new file mode 100644 index 0000000000000000000000000000000000000000..93a839b9b03cb4c5c701bb7878b3c5406d56194e --- /dev/null +++ b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-1/candidate/textures/wood_roughness.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb625e9db13ccf0524f6a35b11fc9f056d71a2bdb5ee973c5241e94dd39d396d +size 647995 diff --git a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/evidence/box_cardboard_open_a01/reconstruction-1/harbor-trajectory-4.json b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/evidence/box_cardboard_open_a01/reconstruction-1/harbor-trajectory-4.json new file mode 100644 index 0000000000000000000000000000000000000000..6304da8fd2c5075bdfe071cb345b3988e951c4c9 --- /dev/null +++ b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/evidence/box_cardboard_open_a01/reconstruction-1/harbor-trajectory-4.json @@ -0,0 +1,3524 @@ +{ + "schema_version": "ATIF-v1.7", + "session_id": "01a15f67-fe58-7236-83fd-b660f9b0916d", + "agent": { + "name": "pi", + "version": "0.85.1", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra" + }, + "steps": [ + { + "step_id": 1, + "timestamp": "2026-10-21T17:00:15.251Z", + "source": "user", + "message": [ + { + "type": "text", + "text": "\nReconstruct the supplied object as /output/asset.usd.\nNominal dimensions in canonical metres XYZ: [0.6357795000076294, 0.5118491649627686, 0.30237919092178345]\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": "611e84e0", + "pi_parent_id": "bea3add0" + } + }, + { + "step_id": 2, + "timestamp": "2026-10-21T17:00:20.193Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "I'll start by reading the task files and understanding the environment.", + "reasoning_content": "Let me start by reading the task files and understanding the environment.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9d1cc9cdcf6d48c6", + "function_name": "read", + "arguments": { + "path": "/task/contract.md" + } + }, + { + "tool_call_id": "chatcmpl-tool-91ecc6d9ec12b2a9", + "function_name": "read", + "arguments": { + "path": "/task/tool-docs.md" + } + }, + { + "tool_call_id": "chatcmpl-tool-b574150645cbeffe", + "function_name": "read", + "arguments": { + "path": "/task/scoring-policy.json" + } + }, + { + "tool_call_id": "chatcmpl-tool-bb8867e50b8e27ae", + "function_name": "bash", + "arguments": { + "command": "ls -la /output /task 2>/dev/null; python3 -c \"import pxr; print(pxr.__file__)\" 2>&1; which usdcat usdchecker 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9d1cc9cdcf6d48c6", + "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": "chatcmpl-tool-91ecc6d9ec12b2a9", + "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": "chatcmpl-tool-b574150645cbeffe", + "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": "chatcmpl-tool-bb8867e50b8e27ae", + "content": "/output:\ntotal 0\ndrwxrwxrwx 2 actor actor 40 Oct 21 17:00 .\ndrwxr-xr-x 1 root root 56 Oct 21 17:00 ..\n\n/task:\ntotal 776\ndr-xr-xr-x 2 actor actor 160 Oct 21 17:00 .\ndrwxr-xr-x 1 root root 56 Oct 21 17:00 ..\n-r--r--r-- 1 actor actor 1746 Oct 21 17:00 contract.md\n-r--r--r-- 1 actor actor 452 Oct 21 17:00 instruction.md\n-r--r--r-- 1 actor actor 3305 Oct 21 17:00 scoring-policy.json\n-r--r--r-- 1 actor actor 435 Oct 21 17:00 simready-bench-native-output-policy.ts\n-r--r--r-- 1 actor actor 772740 Oct 21 17:00 thumbnail.png\n-r--r--r-- 1 actor actor 572 Oct 21 17:00 tool-docs.md\n/usr/local/lib/python3.12/dist-packages/pxr/__init__.py\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 3205, + "completion_tokens": 183, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 3388 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "56ab740b", + "pi_parent_id": "611e84e0", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R0StS6jB0FWYxLCAJsygxGQY", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 3, + "timestamp": "2026-10-21T17:00:26.358Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me read the instruction file and understand the object. The object is a simple open box/tray - it looks like a cardboard/plywood shallow box (a tray/bin). It has 4 walls and a bottom, open on top. The dimensions are [0.6358, 0.5118, 0.3024] meters.\n\nThis is a rigid object - a box. No moving parts. So it should be a single rigid body (or fixed structure). Since it's rigid, no joints needed - but the contract says use fixed joints for genuine fixed attachments and keep rigid objects rigid.\n\nLet me read instruction.md and the output policy, then check the benchmark tools.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9acdb95acefbc5a3", + "function_name": "read", + "arguments": { + "path": "/task/instruction.md" + } + }, + { + "tool_call_id": "chatcmpl-tool-b08a0b67fba6f0af", + "function_name": "read", + "arguments": { + "path": "/task/simready-bench-native-output-policy.ts" + } + }, + { + "tool_call_id": "chatcmpl-tool-92129861e1f09153", + "function_name": "mcp", + "arguments": { + "action": "ui-messages" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9acdb95acefbc5a3", + "content": "Reconstruct the supplied object as /output/asset.usd.\nNominal dimensions in canonical metres XYZ: [0.6357795000076294, 0.5118491649627686, 0.30237919092178345]\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.", + "extra": { + "tool_name": "read", + "is_error": false + } + }, + { + "source_call_id": "chatcmpl-tool-b08a0b67fba6f0af", + "content": "export default function (pi) {\n pi.on(\"before_provider_request\", (event) => {\n if (!event.payload || typeof event.payload !== \"object\" || Array.isArray(event.payload)) {\n throw new Error(\"Native output policy requires an object payload\");\n }\n const payload = { ...event.payload };\n delete payload.max_tokens;\n delete payload.max_completion_tokens;\n delete payload.max_output_tokens;\n return payload;\n });\n}", + "extra": { + "tool_name": "read", + "is_error": false + } + }, + { + "source_call_id": "chatcmpl-tool-92129861e1f09153", + "content": "No UI session messages available.", + "extra": { + "tool_name": "mcp", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 5488, + "completion_tokens": 238, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 5726 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "d1425f03", + "pi_parent_id": "97b78907", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RTZFzttwY8hDIGv3niN6EzKD", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 4, + "timestamp": "2026-10-21T17:00:30.517Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "Let me explore the benchmark MCP tools.", + "reasoning_content": "Let me understand the benchmark tools available. Let me check the mcp server status and search for the relevant tools.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b94efb46f1d8eec6", + "function_name": "mcp", + "arguments": { + "action": "install", + "url": "benchmark" + } + }, + { + "tool_call_id": "chatcmpl-tool-95a7ab6339582e43", + "function_name": "mcp", + "arguments": { + "search": "validate" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b94efb46f1d8eec6", + "content": "Failed to install MCP server: MCP install URL must be an absolute URL", + "extra": { + "tool_name": "mcp", + "is_error": false + } + }, + { + "source_call_id": "chatcmpl-tool-95a7ab6339582e43", + "content": "Found 1 tool matching \"validate\":\n\nbenchmark_validate_candidate\n Validate a frozen candidate and return NVIDIA findings. Each new request consumes one validation call. Repeating a request ID returns its existing result.\n\n Shape:\n { request_id: string; }", + "extra": { + "tool_name": "mcp", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 6003, + "completion_tokens": 97, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 6100 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "b0503f76", + "pi_parent_id": "9ff3b25f", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RbgHSfPA9UKSffXuHiJkRdLY", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 5, + "timestamp": "2026-10-21T17:00:49.429Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "The benchmark server is already available. Let me list all its tools.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-93f6436ed1fd29ea", + "function_name": "mcp", + "arguments": { + "search": "submit" + } + }, + { + "tool_call_id": "chatcmpl-tool-8f757ee5c49ab20a", + "function_name": "mcp", + "arguments": { + "search": "report" + } + }, + { + "tool_call_id": "chatcmpl-tool-89aef59107c3c0a1", + "function_name": "mcp", + "arguments": { + "server": "benchmark" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-93f6436ed1fd29ea", + "content": "Found 1 tool matching \"submit\":\n\nbenchmark_submit_asset\n End authoring and freeze the submitted asset. Returns the submission hash.\n\n Shape:\n { request_id: string; }", + "extra": { + "tool_name": "mcp", + "is_error": false + } + }, + { + "source_call_id": "chatcmpl-tool-8f757ee5c49ab20a", + "content": "Found 1 tool matching \"report\":\n\nbenchmark_get_validation_report\n Read a requested range of a prior NVIDIA report. The complete report is already saved at the file_path returned by validation. Prefer rg and targeted local file reads. Read-only and free of validation quota.\n\n Shape:\n { report_id: string; offset?: number; limit?: number; }", + "extra": { + "tool_name": "mcp", + "is_error": false + } + }, + { + "source_call_id": "chatcmpl-tool-89aef59107c3c0a1", + "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": 6188, + "completion_tokens": 93, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 6281 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "d78b01f9", + "pi_parent_id": "771ace94", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RCaEBp13OMWyJPkW7Iqf40HG", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 6, + "timestamp": "2026-10-21T17:03:10.517Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "This is a rigid open-top box (tray) with no moving parts \u2014 a single rigid body. Let me check available Python libraries and the pxr version.", + "reasoning_content": "Let me understand the object and plan. The object is a shallow open-top box (a tray/bin) made of light wood/cardboard. It has 4 walls and a bottom. Dimensions X=0.6358 (length), Y=0.5118 (width), Z=0.3024 (height).\n\nWait, looking at the image: it's an open box. The box appears to be wider/longer. Let me consider the canonical XYZ. X = 0.6358, Y = 0.5118, Z = 0.3024. The Z is the smallest = height (0.3m). So the box is 0.636 x 0.512 x 0.302. That matches an open tray.\n\nThis is a rigid object - a single rigid body. No moving parts. So I should model it as one rigid mesh (or possibly fixed joints if I make it from separate panels). Since it's genuinely rigid, I'll keep it as one rigid body with one rigid body mesh/physics, no joints needed (or I can keep it a single mesh).\n\nThe scoring focuses on:\n- Formal fidelity: 46 mandatory SimReady requirements\n- Visual fidelity: shape, colors, textures\n- Kinematic fidelity: rigid parts, joints\n\nSince this is a rigid box with no moving parts, kinematic fidelity = single rigid body, no joints. I'll model a single rigid body.\n\nNow I need to satisfy the SimReady-Foundation Robotics-Prop 4.0.0 mandatory requirements. Let me look at what those 46 requirements are. The applicable requirement IDs are listed in scoring-policy.json. Let me decode them:\n\n- com.nvidia.simready.HI.010\n- com.nvidia.simready.NP.002, NP.003, NP.004, NP.005, NP.007, NP.008\n- com.nvidia.simready.RB.001, RB.007, RB.010, RB.COL.001, RB.COL.002, RB.COL.003\n- com.nvidia.simready.SL.001, SL.002, SL.003, SL.MAT.001, SL.QCODE.001, SL.TIME.001\n- com.nvidia.simready.SR.002, SR.004\n- com.nvidia.simready.UN.006, UN.007\n- com.nvidia.simready.VG.008, VG.025, VG.MESH.001\n- com.nvidia.simready.VM.MAT.001, VM.PS.002, VM.TEX.001, VM.TEX.003\n- com.nvidia.usd.AA.001, AA.002, HI.001, HI.003, HI.004, RB.003, RB.005, RB.009, RB.COL.004, VG.002, VG.014, VG.027, VG.028, VG.029, VM.BIND.001, VM.PS.001\n\nThese correspond to NVIDIA SimReady validation rules. Let me recall what these are. I know the SimReady validator (usdchecker/SimReady). Let me recall common ones:\n\nNamespace:\n- NP = NonPhysically? No. Actually in NVIDIA's checker, the categories:\n - UN = Universal\n - HI = Hierarchy / ... \n - VG = Visual Geometry (visual graph)\n - VM = Visual Material\n - RB = ? \n - SL = ?\n - SR = ?\n - NP = ?\n - COL = collision\n\nActually the SimReady spec namespaces: Let me recall the SimReady SLA spec structure. The check IDs like \"com.nvidia.simready.VG.001\" etc.\n\nLet me think about what each category is:\n- VG = Visual Geometry\n- VM = Visual Material\n- RB = Rigid Body (physics)\n- UN = Universal\n- HI = ?\n- SL = ?\n- SR = ?\n- NP = ?\n- AA = Animation & ...\n- COL = Collision\n\nI should try to just build a clean, standard SimReady-compliant asset and validate it, then iterate based on the report.\n\nLet me recall the SimReady requirements. I'll build based on best practices:\n\nKey SimReady-Foundation rules (from NVIDIA Omniverse SimReady):\n- AA.001: asset must not have any animation (no time-sampled data)? Actually AA.001 might be \"No animation\". Let me think.\n- The \"QCODE\" = quality code. SL.QCODE.001 - the asset must have a SimReady quality code / metadata.\n\nActually, I recall the SimReady validator uses these requirement IDs. Let me try to reconstruct from known NVIDIA SimReady SLA (SimReady Level of Assurance). The rules are grouped. Let me just build a proper asset and rely on validation reports to fix.\n\nLet me think about what a standard SimReady-compliant USD looks like:\n\n1. A root prim (Xform) named after the asset.\n2. Single mesh (or meshes) with a valid UsdPreviewSurface or a physical material (Mdl/SimplexMaterial).\n3. Materials bound to meshes.\n4. Physics: a RigidBodyAPI (or just a RigidBody). Actually for a static prop, SimReady Robotics-Prop likely wants a physics object: RigidBodyAPI with kinematicEnabled, and collision (PhysicsCollisionAPI with ConvexDecomposition or mesh).\n5. Textures: 1024x1024, correct format.\n6. Up axis: Y-up (metres).\n7. No hidden prims, proper hierarchy.\n\nThe metadata: SimReady requires certain metadata. QCODE - a quality code metadata. Let me think about SL.QCODE.001: \"Asset has a quality code\" - the metadata like `simReady:...`? \n\nActually, I recall the SimReady standard uses these metadata fields on the root:\n- `\"simReady\"` metadata? \n- The QCODE refers to a compliance code.\n\nHmm. Let me just build and validate. The report will tell me exactly what's missing. Let me be efficient given the budget.\n\nLet me recall the actual SimReady requirement definitions. This is the NVIDIA SimReady SLA. I'll reconstruct the key ones based on my knowledge:\n\nUN (Universal):\n- UN.001: Uses SI units\n- UN.006: ... \n- UN.007: ...\n\nActually let me just build a robust asset and iterate via validation. Let me first check what version of pxr and usd is available.\n\nLet me plan the geometry. The box:\n- Outer dims: X=0.6357795, Y=0.5118492, Z=0.3023792\n- Wall thickness t \u2248 0.012 (looks thin, maybe 10-15mm)\n- Bottom thickness t\n- Open top.\n\nI'll build the box as a single watertight-ish shell. Actually a simple approach: model it as a box with 4 walls + bottom, walls thickness t. I can generate a single mesh with these panels as one watertight solid (a \"tray\" solid). That's cleanest for collision.\n\nLet me generate the mesh with numpy/pxr. I'll create vertices for a tray solid (hollow box, open top, with wall and floor thickness).\n\nLet me define:\n- Lx = 0.6357795 (length along X)\n- Ly = 0.5118492 (width along Y)\n- Lz = 0.3023792 (height along Z)\n- t = wall thickness = 0.012\n\nThe box centered at origin? The dimensions are \"canonical metres\". I'll build with base at z=0 and centered in X,Y. Actually let me center it at origin in X and Y, and bottom at z=0 (so it sits on ground). Hmm, SimReady usually wants the asset... For a prop, center at origin is fine. Let me center X,Y and put the box from z=0 to z=Lz. Actually to be safe for a prop that sits on the ground, I'll put the bottom at z=0. But some rules want bounding box centered. Let me not overthink; center the whole thing at origin (x,y,z centered). Actually for a prop the convention is often that the object's base is at z=0. I'll go with base at z=0, centered in XY. That's most natural for a box on a table.\n\nHmm, but the physics/center of mass etc. Let me just center at origin fully - x in [-Lx/2, Lx/2], y in [-Ly/2, Ly/2], z in [0, Lz]. That keeps base on ground.\n\nWait, is Z up or Y up? Canonical XYZ given as [X, Y, Z] = [0.636, 0.512, 0.302]. The image shows a wide box. X=0.636 is the longest, Z=0.302 is the shortest (height). In the reference image, the box is long (horizontal), and the height is the smallest dimension. So Z is height = 0.302. Good, Z is up. USD default up-axis is Y but I'll set upAxis to Z if that's what's natural... Actually SimReady typically requires Y-up (metres, Y-up). Hmm. But the given dimensions put height in Z. \n\nThis is a key decision. The \"canonical metres XYZ\" \u2014 the height is in Z. If the validator expects Y-up, then I'd need height in Y. But the nominal dimensions explicitly are XYZ with Z being the smallest. This strongly implies Z-up (height along Z). Many of these SimReady robotics tasks use Z-up. Let me check: the requirement UN.x about up axis. I'll set upAxis Z and build with height along Z to match the given canonical dimensions. The bounding box must match the nominal dimensions in XYZ order.\n\nSo I'll make the mesh bounding box exactly [0.6357795, 0.5118492, 0.3023792] in X, Y, Z.\n\nLet me set upAxis = Z. Build box with:\n- X: [-Lx/2, Lx/2]\n- Y: [-Ly/2, Ly/2]\n- Z: [0, Lz]\n\nNow material: light wood/beige color. The walls outer face is whitish, inner face is light wood grain. The bottom is light wood. I'll use a simple material with a base color texture or just a constant color. For visual fidelity, a wood-grain texture would help but is complex. Let me use a reasonable base color (light beige wood) with preview surface. Actually to match: outer walls are white/cream, inner is wood. I could do a two-tone but that's hard with one material. \n\nFor visual fidelity (geometric mean of shape and appearance), shape matters most for the box. I'll give it a light wood color. Let me generate a procedural wood-grain texture and use it, or just use a constant color with appropriate roughness. I'll create a texture map to be safe for VM.TEX requirements (textures should be proper). Actually VM.TEX.001 and VM.TEX.003 might require textures to be square power-of-two, correct orientation, etc. If I use a constant color (no texture), that might fail a \"must use texture\" requirement, or might be fine. Let me check: VM.TEX.001 - \"Texture uses the correct orientation\" or \"Texture is square\". These apply \"if a texture is present\". Using no texture might be OK but appearance fidelity lower.\n\nLet me generate a wood texture image with PIL and a base color. Let me check if PIL is available. I'll generate a simple wood-grain texture (procedural stripes) and set it as baseColor. That gives better appearance and satisfies texture requirements.\n\nLet me now think about the SimReady metadata / requirements. Let me recall the actual list. I'll do my best and rely on validation.\n\nLet me recall NVIDIA SimReady SLA requirement descriptions (Robotics-Prop profile). From my knowledge of the NVIDIA SimReady spec:\n\nUniversal (UN):\n- UN.001: Single USD file (all self-contained)\n- UN.002: ... \n- UN.006: \"No use of relative paths / asset is self-contained\"? \n- UN.007: ...\n\nHmm, I'm not confident. Let me just build a clean standard asset and validate to get the real list of failures, then fix. That's the efficient path with 5 validation calls.\n\nLet me recall what I'm fairly confident about for a SimReady robotics prop:\n1. Root xform, single mesh under it.\n2. upAxis: \"Z\" or \"Y\". I'll pick Z to match dims.\n3. Units: metres.\n4. Mesh: valid (no NaN, proper normals, faces, points). VG.MESH.001 - watertight/valid mesh.\n5. Physics: RigidBodyAPI (for a prop it should have physics). RB.001 - has rigid body. RB.007, RB.010 - collision. RB.COL.xxx - collision API.\n6. Material: UsdPreviewSurface or Mdl/SimplexMaterial. VM.MAT.001.\n7. Texture: VM.TEX.xxx.\n8. QCODE: metadata for SimReady quality code.\n9. SL.001/002/003 - maybe \"single leaf\" / hierarchy.\n\nActually I recall now: SimReady SLA categories:\n- UN = Universal\n- SR = SimReady (top-level: file structure)\n- SL = ... maybe \"Scene/Layout\"? or \"Standard Level\"?\n- VG = Visual Geometry\n- VM = Visual Material\n- RB = Rigid Body / physics\n- NP = Non-Ph... \n- HI = Hierarchy / Instance\n- AA = Animation\n- VG.MESH = mesh-specific\n- COL = collision\n- MAT = material\n\nLet me just build and validate. Let me write the script.\n\nFor the asset, key SimReady foundation requirements I'm confident about:\n- File must be self-contained (textures relative, embedded or in same folder).\n- Up axis declared.\n- Units metres.\n- Exactly one top-level \"product\" prim.\n- Mesh must be a valid, oriented, triangulated or quad, no duplicate points.\n- Material must be bound, use UsdPreviewSurface (or Shading API), no textures that are too big, correct color space.\n- Physics: RigidBodyAPI with kinematic enabled (static prop), collision shape (mesh or convex).\n\nLet me write the builder. I'll create:\n- /output/textures/wood_basecolor.png (1024x1024)\n- /output/asset.usd\n\nStructure:\n```\n/Box (Xform, root) \n - RigidBodyAPI (kinematic, mass)\n - \"Mesh\" (Mesh) \n - points, faces, normals, texcoords\n - PhysicsCollisionAPI (approximation mesh / convex decomposition)\n - \"Material\" (Material) \n - UsdPreviewSurface\n```\n\nLet me handle the QCODE. I recall SimReady requires a metadata on root: \n```\nmetadata {\n string simReadyMetadata = \"...\" \n}\n```\nActually I think the QCODE requirement SL.QCODE.001 is about the asset having a valid \"quality code\" stored as metadata like `simReady:complianceCode`. I'll add a metadata field and adjust based on validation.\n\nLet me just start building and validate to see the actual requirements list, since I have 5 validation calls. Actually, validation is the reliable source. Let me build a first attempt that's reasonably complete, then validate and read the full report to see all 46 requirements and which fail.\n\nLet me write the Python builder now.\n\nLet me check available tools: PIL, numpy.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9c6f3ca415d40a93", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"import pxr; print('pxr', pxr.__version__)\"; python3 -c \"import PIL; print('PIL', PIL.__version__)\" 2>&1; python3 -c \"import numpy; print('numpy', numpy.__version__)\" 2>&1; echo \"---checker---\"; python3 -c \"import pxr.Gf, pxr.Usd, pxr.UsdGeom, pxr.UsdPhysics, pxr.UsdUtils; print('ok')\" 2>&1" + } + }, + { + "tool_call_id": "chatcmpl-tool-ba0d8131e3e80b96", + "function_name": "mcp", + "arguments": { + "tool": "benchmark_get_task_thumbnail" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9c6f3ca415d40a93", + "content": "Traceback (most recent call last):\n File \"\", line 1, in \nAttributeError: module 'pxr' has no attribute '__version__'\nPIL 13.0.0\nnumpy 2.2.6\n---checker---\nok", + "extra": { + "tool_name": "bash", + "is_error": false + } + }, + { + "source_call_id": "chatcmpl-tool-ba0d8131e3e80b96", + "content": [ + { + "type": "text", + "text": "{\"status\": \"completed\", \"task\": \"Reconstruct the supplied object as /output/asset.usd.\\nNominal dimensions in canonical metres XYZ: [0.6357795000076294, 0.5118491649627686, 0.30237919092178345]\\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\", \"thumbnail\": {\"height\": 1024, \"sha256\": \"20697a50b64e651599061e3589114f791da039fc20bb9e23d0b9820231324ae2\", \"width\": 1024}}" + }, + { + "type": "image", + "source": { + "media_type": "image/png", + "path": "images/pi_image_2.png" + } + } + ], + "extra": { + "tool_name": "mcp", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 6490, + "completion_tokens": 3825, + "cached_tokens": 5488, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 10315 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "d6e81aa7", + "pi_parent_id": "30a7a474", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RfD4NLCL3kDzD7y5g82C557M", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 7, + "timestamp": "2026-10-21T17:05:28.650Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "I understand the task. This is a rigid open-top box/tray (plywood storage bin) with **no moving parts** \u2014 a single rigid body, no joints. Let me author it with Python/PXR. First, the wood texture and the builder script.", + "reasoning_content": "This is round 6 of 64. Interesting - so previous rounds happened? Let me check /output - it was empty earlier. Wait, it said \"Authoring round 6 of 64\". Maybe the environment counts my initial calls. Let me just proceed.\n\nLet me build the asset. I'll write a comprehensive Python builder script in /tmp, run it, then validate.\n\nLet me write the builder. I'll generate a wood texture and build the tray mesh with physics and material.\n\nLet me carefully design the mesh as a tray (hollow box, open top). I'll build a solid tray with:\n- Floor: thickness t\n- 4 walls thickness t, up to height Lz\n\nVertices of the tray solid. Let me define outer box corners and inner cavity.\n\nOuter:\n- x from -a to a, a=Lx/2\n- y from -b to b, b=Ly/2\n- z from 0 to Lz\n\nInner cavity (top opening):\n- x from -(a-t) to (a-t)\n- y from -(b-t) to (b-t)\n- z from t to Lz (open top)\n\nThe tray solid is outer box minus inner cavity (but only top open, bottom closed by t).\n\nI'll build it as a set of quads. Let me construct faces. This is a standard \"cup\" shape. Let me enumerate:\n\nBottom outer face (z=0): quad at z=0, full footprint [-a,a]x[-b,b], normal -Z.\nFour outer wall faces: \n- front (y=-b): x[-a,a], z[0,Lz], normal -Y\n- back (y=b): normal +Y\n- left (x=-a): normal -X\n- right (x=a): normal +X\nInner faces:\n- top of floor (z=t): x[-(a-t),a-t], y[-(b-t),b-t], normal +Z\n- inner wall faces:\n - inner front (y=-(b-t)): x[-(a-t),a-t], z[t,Lz], normal +Y\n - inner back (y=b-t): normal -Y\n - inner left (x=-(a-t)): normal +X\n - inner right (x=a-t): normal -X\n- Top rim faces (the top edges of walls, thickness t rings):\n - front rim (y from -(b-t) to -b, z=Lz): quad, normal +Z\n - back rim\n - left rim\n - right rim\n\nThat's a closed watertight solid. Good.\n\nI'll build faces as lists of vertex indices. Let me create explicit vertex positions. To keep it simple and avoid shared-vertex issues, I'll create separate vertices per face corner (duplicated) so normals are clean per-face. SimReady VG.MESH.001 wants valid mesh; duplicated vertices per face is fine (it's a valid mesh). Actually better to use face vertex normals via per-face vertices so each face is flat. That's clean.\n\nLet me generate with each face having its own 4 vertices.\n\nFor texture coordinates: I'll just map a simple planar UV per face (0..1). It doesn't need to be perfect. I'll give reasonable UVs.\n\nLet me write it. I'll build points and faces (faces referencing duplicated points). And compute face normals from winding (I'll set winding CCW as seen from outside). Let me make sure winding produces outward normals.\n\nRather than hand-winding, I'll define each quad with an outward normal and order vertices CCW when viewed from outside. Let me just define and then verify by checking normal direction; if a face is flipped I'll fix. Easier: I'll write a helper that given 4 points and a desired outward normal, orders them CCW. Then normals are consistent.\n\nLet me code:\n\n```python\nimport numpy as np\ndef quad(pts, normal):\n # pts: 4 points, need to order CCW around normal\n c = np.mean(pts, axis=0)\n # project\n v = [p - c for p in pts]\n # angle in plane perpendicular to normal\n # build basis\n n = normal/np.linalg.norm(normal)\n up = np.array([0,0,1.0])\n if abs(np.dot(n, up))>0.9: up = np.array([1,0,0])\n x = np.cross(n, up); x/=np.linalg.norm(x)\n y = np.cross(n, x)\n ang = [np.arctan2(np.dot(vv, y), np.dot(vv, x)) for vv in v]\n order = np.argsort(ang)\n return [pts[i] for i in order]\n```\nThis gives CCW around n (since angle increases CCW in x-y basis where y = cross(n,x) => right-handed with n). Good, outward normal = n.\n\nNow assign texcoords. For each face I'll map based on its normal for a planar UV. Simpler: give each face UV (0,0),(1,0),(1,1),(0,1). Fine.\n\nLet me define the faces:\n\na=Lx/2, b=Ly/2, H=Lz, t=wall.\n\nBottom:\n pts = [(-a,-b,0),(a,-b,0),(a,b,0),(-a,b,0)], n=(0,0,-1)\nOuter walls:\n front (y=-b): [(-a,-b,0),(a,-b,0),(a,-b,H),(-a,-b,H)] n=(0,-1,0)\n back (y=b): [(-a,b,0),(a,b,0),(a,b,H),(-a,b,H)] n=(0,1,0)\n left (x=-a): [(-a,-b,0),(-a,b,0),(-a,b,H),(-a,-b,H)] n=(-1,0,0)\n right (x=a): [(a,-b,0),(a,b,0),(a,b,H),(a,-b,H)] n=(1,0,0)\nInner cavity: ai=a-t, bi=b-t, zt=t\n floor top (z=t): [(-ai,-bi,zt),(ai,-bi,zt),(ai,bi,zt),(-ai,bi,zt)] n=(0,0,1)\n inner front (y=-bi): [(-ai,-bi,zt),(ai,-bi,zt),(ai,-bi,H),(-ai,-bi,H)] n=(0,-1,0)\n inner back (y=bi): [(-ai,bi,zt),(ai,bi,zt),(ai,bi,H),(-ai,bi,H)] n=(0,1,0)\n inner left (x=-ai): [(-ai,-bi,zt),(-ai,bi,zt),(-ai,bi,H),(-ai,-bi,H)] n=(-1,0,0)\n inner right (x=ai): [(ai,-bi,zt),(ai,bi,zt),(ai,bi,H),(ai,-bi,H)] n=(1,0,0)\nRims (top, z=H):\n front rim (y -b..-bi): [(-a,-b,H),(a,-b,H),(a,-bi,H),(-a,-bi,H)] n=(0,0,1)\n back rim (y bi..b): [(-a,bi,H),(a,bi,H),(a,b,H),(-a,b,H)] n=(0,0,1)\n left rim (x -a..-ai): [(-a,-b,H),(-a,b,H),(-ai,b,H),(-ai,-b,H)] n=(0,0,1)\n right rim (x ai..a): [(ai,-b,H),(ai,b,H),(a,b,H),(a,-b,H)] n=(0,0,1)\n\nWait left rim should be the strip on top of the left wall: x from -a to -ai, y from -b to b. pts: [(-a,-b,H),(-a,b,H),(-ai,b,H),(-ai,-b,H)] n=(0,0,1). Good.\n\nThat's 4+4+... let me count: bottom(1) + 4 outer + 1 floor + 4 inner + 4 rim = 14 faces. Watertight. \n\nNow build mesh with duplicated vertices (4 per face = 56 points).\n\nLet me set t. Looking at image, walls are thin. t = 0.012 m = 12mm. Reasonable for plywood. I'll use t=0.012.\n\nNow physics. For a static prop:\n- RigidBodyAPI on the Mesh prim (or a dedicated). SimReady: RigidBodyAPI with kinematicEnabled=True (static prop) OR disabled. For a prop, static => kinematic or no gravity. I'll set kinematicEnabled=True, and mass.\n- Collision: PhysicsCollisionAPI on the mesh, with collisionAPI: approximation = \"convexDecomposition\" or \"mesh\". For a box, convex hull would be simplest but the box is not fully convex (it has a cavity). Convex decomposition is appropriate. I'll use \"convexDecomposition\" with maxConvexHulls.\n\nActually for SimReady robotics props, they often want the collision to be the mesh or convex decomposition. Let me use \"convexDecomposition\".\n\nHmm but convex decomposition of a thin-walled tray could be heavy. Alternatively use \"mesh\" approximation. Let me use \"convexDecomposition\" with a reasonable max hulls (e.g., 10). Or just \"mesh\". I'll use \"convexDecomposition\".\n\nWait, actually for visual + physics, SimReady wants:\n- RigidBodyAPI (RB.001)\n- PhysicsCollisionAPI (RB.COL.001/002/003)\n- Mass (RB.007?)\n\nLet me add:\n- UsdPhysics.RigidBodyAPI: kinematicEnabled=True\n- UsdPhysics.MassAPI: mass = density*volume (just set mass ~ 2.0 kg)\n- UsdPhysics.CollisionAPI: approximation=\"convexDecomposition\", convexDecompositionMaxConvexHulls=10, convexDecompositionMaxVertices=... \n\nNow material. UsdPreviewSurface:\n- baseColor = texture (wood) or constant\n- roughness = 0.6\n- metallic = 0.0\n\nI'll create a material prim with UsdPreviewSurface, bind to the mesh via MaterialBindingAPI (bind to mesh in \"inherit\" or \"over\" scope). VM.BIND.001 - material binding.\n\nTextures: I'll generate a 1024x1024 wood grain PNG for baseColor. Also a roughness map? VM.TEX.001/003 - I'll provide baseColor texture. Let me generate a plausible wood texture with PIL.\n\nUp axis: set UsdGeom.points... I'll set the stage upAxis to \"Z\" via UsdGeom.SetUpAxis? Actually upAxis is a stage metadata: `UsdGeom.SetUpAxis(stage, \"Z\")` and units: `UsdGeom.SetUnits(stage, UsdGeom.Tokens.meters)`.\n\nQCODE: I'll add metadata on root. Let me guess the field. I'll add:\n```\nmetadata {\n custom {\n string simReadyQCode = ...\n }\n}\n```\nHmm, I don't know the exact key. Let me build, validate, read the report to see the exact requirement, and add the right metadata. That's the reliable path.\n\nLet me also think about AA (animation) requirements - AA.001/002: no animation, no time sampling. I'll just not add any time-varying data. And SL.TIME.001 - \"no animation / single frame\". Fine.\n\nLet me write the builder and generate the wood texture. I'll make the texture a light beige wood with subtle grain, matching the reference (pale plywood).\n\nLet me write the texture generation: light cream wood color base ~ (0.90, 0.83, 0.68) with subtle vertical grain variation. The reference outer walls are near-white/cream and inner is slightly more yellow wood. I'll just do a light wood. Good enough.\n\nLet me write everything.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-912841aef0922c21", + "function_name": "write", + "arguments": { + "path": "/tmp/make_texture.py", + "content": "import numpy as np\nfrom PIL import Image\nimport os, math\n\nos.makedirs(\"/output/textures\", exist_ok=True)\nS = 1024\nrng = np.random.default_rng(7)\n\n# Light plywood / pale birch wood grain, matching the reference (cream / pale yellow).\n# Base pale-birch colour.\nbase = np.array([0.87, 0.79, 0.62])\n\n# vertical grain streaks (long planks) along X\nx = np.arange(S)\ngrain = np.zeros((S, S))\n# a few long grain lines\nfor _ in range(140):\n yy = rng.integers(0, S)\n amp = rng.uniform(0.02, 0.07)\n xx = np.linspace(0, S, S)\n phase = rng.uniform(0, 6.28)\n wave = amp * np.sin(xx * rng.uniform(0.01, 0.05) + phase) * 0.5\n # draw a thin band\n width = rng.integers(1, 4)\n band = (np.abs(np.arange(S) - yy) < width)\n grain[band, :] += wave + rng.uniform(-0.02, 0.02)\n\n# fine per-pixel noise (paper pulp)\nnoise = rng.normal(0, 0.015, size=(S, S))\n# fine cross hatching (laminate)\ncross = 0.01 * np.sin((x/ S) * 2*math.pi* 220)[None, :]\n\nimg = np.zeros((S, S, 3))\nfor c in range(3):\n layer = base[c] + grain*0.6 + noise + cross*0.3\n img[..., c] = np.clip(layer, 0, 1)\n\nim = Image.fromarray((img*255).astype(np.uint8), \"RGB\")\nim.save(\"/output/textures/wood_basecolor.png\")\n\n# Roughness map: mostly matte (~0.6) with slight variation\nr = np.clip(0.6 + grain*0.3 + rng.normal(0,0.02,(S,S)), 0.3, 0.95)\nrimg = (r*255).astype(np.uint8)\nImage.fromarray(rimg, \"L\").save(\"/output/textures/wood_roughness.png\")\n\nprint(\"textures done\", im.size)\n" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-912841aef0922c21", + "content": "Successfully wrote to /tmp/make_texture.py", + "extra": { + "tool_name": "write", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 11680, + "completion_tokens": 3396, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 15076 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "258ce537", + "pi_parent_id": "191b5ff4", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rsls0Kaj1f6Ss2MPXlektfdO", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 8, + "timestamp": "2026-10-21T17:06:15.901Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me continue. I'll write the main builder script.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a5b4ef222eaede71", + "function_name": "write", + "arguments": { + "content": "import numpy as np\nimport pxr\nfrom pxr import Usd, UsdGeom, UsdPhysics, UsdShade, Gf, Sdf, Vt\n\n# --- Nominal dimensions (canonical metres XYZ) ---\nLx = 0.6357795000076294 # length (X)\nLy = 0.5118491649627686 # width (Y)\nLz = 0.30237919092178345 # height (Z)\nt = 0.012 # wall / floor thickness (thin plywood tray)\n\na = Lx/2.0\nb = Ly/2.0\nH = Lz\nai = a - t\nbi = b - t\nzt = t\n\ndef quad(pts, normal):\n \"\"\"Return 4 points ordered CCW as seen from the outward `normal`.\"\"\"\n pts = [np.asarray(p, dtype=float) for p in pts]\n n = np.asarray(normal, dtype=float); n /= np.linalg.norm(n)\n c = np.mean(pts, axis=0)\n v = [p - c for p in pts]\n up = np.array([0.0,0.0,1.0])\n if abs(np.dot(n, up)) > 0.9:\n up = np.array([1.0,0.0,0.0])\n x = np.cross(n, up); x /= np.linalg.norm(x)\n y = np.cross(n, x)\n ang = [np.arctan2(np.dot(vv, y), np.dot(vv, x)) for vv in v]\n order = np.argsort(ang)\n return [pts[i] for i in order]\n\n# Build a watertight \"tray\" (hollow box, open top, closed bottom).\nfaces = []\ndef add(pts, n):\n faces.append(quad(pts, n))\n\n# bottom outer\nadd([(-a,-b,0),(a,-b,0),(a,b,0),(-a,b,0)], (0,0,-1))\n# outer walls\nadd([(-a,-b,0),(a,-b,0),(a,-b,H),(-a,-b,H)], (0,-1,0))\nadd([(-a,b,0),(a,b,0),(a,b,H),(-a,b,H)], (0,1,0))\nadd([(-a,-b,0),(-a,b,0),(-a,b,H),(-a,-b,H)], (-1,0,0))\nadd([(a,-b,0),(a,b,0),(a,b,H),(a,-b,H)], (1,0,0))\n# inner floor top\nadd([(-ai,-bi,zt),(ai,-bi,zt),(ai,bi,zt),(-ai,bi,zt)], (0,0,1))\n# inner walls\nadd([(-ai,-bi,zt),(ai,-bi,zt),(ai,-bi,H),(-ai,-bi,H)], (0,-1,0))\nadd([(-ai,bi,zt),(ai,bi,zt),(ai,bi,H),(-ai,bi,H)], (0,1,0))\nadd([(-ai,-bi,zt),(-ai,bi,zt),(-ai,bi,H),(-ai,-bi,H)], (-1,0,0))\nadd([(ai,-bi,zt),(ai,bi,zt),(ai,bi,H),(ai,-bi,H)], (1,0,0))\n# top rims\nadd([(-a,-b,H),(a,-b,H),(a,-bi,H),(-a,-bi,H)], (0,0,1))\nadd([(-a,bi,H),(a,bi,H),(a,b,H),(-a,b,H)], (0,0,1))\nadd([(-a,-b,H),(-a,b,H),(-ai,b,H),(-ai,-b,H)], (0,0,1))\nadd([(ai,-b,H),(ai,b,H),(a,b,H),(a,-b,H)], (0,0,1))\n\npoints = []\nface_verts = []\nuv = [(0.0,0.0),(1.0,0.0),(1.0,1.0),(0.0,1.0)]\nfor f in faces:\n base = len(points)\n for p in f:\n points.append((float(p[0]), float(p[1]), float(p[2])))\n face_verts.append([base, base+1, base+2, base+3])\n\npoints = Vt.Vec3fList(len(points), points)\nfverts = face_verts\ntexcoords = Vt.Vec2fList(len(points), [uv[i%4] for i in range(len(points))])\n\n# --- Build stage ---\nstage = Usd.Stage.CreateNew(\"/output/asset.usd\")\nUsdGeom.SetUnits(stage, UsdGeom.Tokens.meters)\nUsdGeom.SetUpAxis(stage, \"Z\")\n\nroot = UsdGeom.Xform.Define(stage, \"TrayBox\")\nroot.AddInvisibleHintRel() # removed below if unsupported\n\nmesh = UsdGeom.Mesh.Define(stage, \"TrayBox/Shell\")\nmesh.CreatePointsAttr(points)\nmesh.CreateFaceVertexIndicesAttr(fverts)\nmesh.CreateFaceVertexUVsAttr(texcoords)\n# compute per-face normals -> face-vertex normals\nfvnormals = []\nfor f in faces:\n p0,p1,p2 = f[0],f[1],f[2]\n nrm = np.cross(p1-p0, p2-p0)\n nrm = nrm/ (np.linalg.norm(nrm)+1e-12)\n for _ in range(4):\n fvnormals.append((float(nrm[0]),float(nrm[1]),float(nrm[2])))\nmesh.CreateFaceVertexNormalsAttr(Vt.Vec3fList(len(fvnormals), fvnormals))\n# smooth? keep flat shading\nmesh.CreateSubdivisionSchemeAttr(\"none\")\n\n# --- Physics: single rigid (static) body with mesh collision ---\nrba = UsdPhysics.RigidBodyAPI.Apply(mesh, \"RigidBodyAPI\")\nrba.CreateKinematicEnabledAttr(True)\nrba.CreateCollisionEnabledAttr(True)\nrba.CreateDisableGravityAttr(True)\n\nmass_api = UsdPhysics.MassAPI.Apply(mesh, \"MassAPI\")\nmass_api.CreateMassAttr(2.5)\n\ncol = UsdPhysics.CollisionAPI.Apply(mesh, \"CollisionAPI\")\ncol.CreateCollisionEnabledAttr(True)\ncol.CreateCollisionAPIAttr(\"convexDecomposition\")\ncol.CreateConvexDecompositionMaxConvexHullsAttr(24)\ncol.CreateConvexDecompositionMaxVerticesPerHullAttr(64)\ncol.CreateCollisionFramesAttr([UsdGeom.Tokens.identity])\n\n# --- Material ---\nmat = UsdShade.Material.Define(stage, \"TrayBox/TrayMaterial\")\nmat.CreateSurfaceAPI() if hasattr(mat, \"CreateSurfaceAPI\") else None\nms = UsdShade.MaterialBindingAPI(mat)\n# UsdPreviewSurface\nstage.GetDefaultPrim()\nsurf = UsdShade.Shader.Define(stage, \"TrayBox/TrayMaterial/PreviewSurface\")\nsurf.CreateInfoAttr(\"source\", \" UsdPreviewSurface\")\nsurf.CreateIdAttr(\"UsdPreviewSurface\")\nsurf.CreateInputAttr(\"baseColor:color3f\", Gf.Vec3f(0.87,0.79,0.62))\nsurf.CreateInputAttr(\"roughness:float\", 0.6)\nsurf.CreateInputAttr(\"metallic:float\", 0.0)\nsurf.CreateInputAttr(\"opacity:float\", 1.0)\n\n# baseColor texture\ntex_file = \"/output/textures/wood_basecolor.png\"\ntexprim = UsdShade.TextureFile.Define(stage, \"TrayBox/TrayMaterial/TexBaseColor\")\ntexprim.CreateFileAttr(tex_file)\ntexprim.CreateFormatAttr(\"openExr\")\ntexprim.CreateFormatAttr(\"png\") if False else None\ntexprim.CreateTextureCoordinateAttribute(\"st\")\ntexprim.CreateWrapSAttr(\"repeat\")\ntexprim.CreateWrapTAttr(\"repeat\")\nsurf.ConnectLookupsToSource? # placeholder - replaced below\n\n# wire texture -> baseColor.rgb\nsrc = surf.GetPrim()\ndst = surf.GetPrim()\nbaseColor_api = surf.GetPrim()\nUsdShade.ConnectAPI? # placeholder\n", + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a5b4ef222eaede71", + "content": "Successfully wrote to /tmp/build_asset.py", + "extra": { + "tool_name": "write", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 15099, + "completion_tokens": 1964, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 17063 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "a22c03dd", + "pi_parent_id": "cb1c459c", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RTbK1SYWN8Ysg6Gag01QvQf1", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 9, + "timestamp": "2026-10-21T17:07:02.513Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "My builder had placeholder junk. Let me rewrite it cleanly.", + "reasoning_content": "I made a mess with placeholders in the builder. Let me rewrite it cleanly. I got distracted with placeholder lines. Let me write a clean, correct version.\n\nFor the material, I'll use UsdShade.Material and UsdShade.Shader (UsdPreviewSurface). Connect texture output \"rgb\" to shader input \"baseColor\".\n\nLet me rewrite cleanly.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-abf532ab73c5457d", + "function_name": "write", + "arguments": { + "content": "import numpy as np\nimport pxr\nfrom pxr import Usd, UsdGeom, UsdPhysics, UsdShade, Gf, Vt, Sdf\n\n# --- Nominal dimensions (canonical metres XYZ) ---\nLx = 0.6357795000076294 # length (X)\nLy = 0.5118491649627686 # width (Y)\nLz = 0.30237919092178345 # height (Z)\nt = 0.012 # wall / floor thickness (thin plywood tray)\n\na = Lx/2.0\nb = Ly/2.0\nH = Lz\nai = a - t\nbi = b - t\nzt = t\n\ndef quad(pts, normal):\n pts = [np.asarray(p, dtype=float) for p in pts]\n n = np.asarray(normal, dtype=float); n /= np.linalg.norm(n)\n c = np.mean(pts, axis=0)\n v = [p - c for p in pts]\n up = np.array([0.0,0.0,1.0])\n if abs(np.dot(n, up)) > 0.9:\n up = np.array([1.0,0.0,0.0])\n x = np.cross(n, up); x /= np.linalg.norm(x)\n y = np.cross(n, x)\n ang = [np.arctan2(np.dot(vv, y), np.dot(vv, x)) for vv in v]\n order = np.argsort(ang)\n return [pts[i] for i in order]\n\nfaces = []\ndef add(pts, n):\n faces.append(quad(pts, n))\n\n# bottom outer\nadd([(-a,-b,0),(a,-b,0),(a,b,0),(-a,b,0)], (0,0,-1))\n# outer walls\nadd([(-a,-b,0),(a,-b,0),(a,-b,H),(-a,-b,H)], (0,-1,0))\nadd([(-a,b,0),(a,b,0),(a,b,H),(-a,b,H)], (0,1,0))\nadd([(-a,-b,0),(-a,b,0),(-a,b,H),(-a,-b,H)], (-1,0,0))\nadd([(a,-b,0),(a,b,0),(a,b,H),(a,-b,H)], (1,0,0))\n# inner floor top\nadd([(-ai,-bi,zt),(ai,-bi,zt),(ai,bi,zt),(-ai,bi,zt)], (0,0,1))\n# inner walls\nadd([(-ai,-bi,zt),(ai,-bi,zt),(ai,-bi,H),(-ai,-bi,H)], (0,-1,0))\nadd([(-ai,bi,zt),(ai,bi,zt),(ai,bi,H),(-ai,bi,H)], (0,1,0))\nadd([(-ai,-bi,zt),(-ai,bi,zt),(-ai,bi,H),(-ai,-bi,H)], (-1,0,0))\nadd([(ai,-bi,zt),(ai,bi,zt),(ai,bi,H),(ai,-bi,H)], (1,0,0))\n# top rims\nadd([(-a,-b,H),(a,-b,H),(a,-bi,H),(-a,-bi,H)], (0,0,1))\nadd([(-a,bi,H),(a,bi,H),(a,b,H),(-a,b,H)], (0,0,1))\nadd([(-a,-b,H),(-a,b,H),(-ai,b,H),(-ai,-b,H)], (0,0,1))\nadd([(ai,-b,H),(ai,b,H),(a,b,H),(a,-b,H)], (0,0,1))\n\npoints = []\nface_verts = []\nuv = [(0.0,0.0),(1.0,0.0),(1.0,1.0),(0.0,1.0)]\nfor f in faces:\n base = len(points)\n for p in f:\n points.append((float(p[0]), float(p[1]), float(p[2])))\n face_verts.append([base, base+1, base+2, base+3])\n\nfvert_pts = Vt.Vec3fList(len(points), points)\nfvert_uv = Vt.Vec2fList(len(points), [uv[i % 4] for i in range(len(points))])\n\n# --- Stage ---\nstage = Usd.Stage.CreateNew(\"/output/asset.usd\")\nUsdGeom.SetUnits(stage, UsdGeom.Tokens.meters)\nUsdGeom.SetUpAxis(stage, \"Z\")\n\nroot = UsdGeom.Xform.Define(stage, \"TrayBox\")\n\nmesh = UsdGeom.Mesh.Define(stage, \"TrayBox/Shell\")\nmesh.CreatePointsAttr(fvert_pts)\nmesh.CreateFaceVertexIndicesAttr(face_verts)\nmesh.CreateFaceVertexUVsAttr(fvert_uv)\nfvnormals = []\nfor f in faces:\n p0, p1, p2 = f[0], f[1], f[2]\n nrm = np.cross(p1 - p0, p2 - p0)\n nrm = nrm / (np.linalg.norm(nrm) + 1e-12)\n for _ in range(4):\n fvnormals.append((float(nrm[0]), float(nrm[1]), float(nrm[2])))\nmesh.CreateFaceVertexNormalsAttr(Vt.Vec3fList(len(fvnormals), fvnormals))\nmesh.CreateSubdivisionSchemeAttr(\"none\")\n# mesh axis\nmesh.SetDisplayColor(Gf.Vec3f(0.87, 0.79, 0.62))\n\n# --- Physics: single rigid (static) body with mesh collision ---\nrba = UsdPhysics.RigidBodyAPI.Apply(mesh, \"RigidBodyAPI\")\nrba.CreateKinematicEnabledAttr(True)\nrba.CreateCollisionEnabledAttr(True)\nrba.CreateDisableGravityAttr(True)\n\nmass_api = UsdPhysics.MassAPI.Apply(mesh, \"MassAPI\")\nmass_api.CreateMassAttr(2.5)\n\ncol = UsdPhysics.CollisionAPI.Apply(mesh, \"CollisionAPI\")\ncol.CreateCollisionEnabledAttr(True)\ncol.CreateCollisionAPIAttr(\"convexDecomposition\")\ncol.CreateConvexDecompositionMaxConvexHullsAttr(24)\ncol.CreateConvexDecompositionMaxVerticesPerHullAttr(64)\n\n# --- Material ---\nmat = UsdShade.Material.Define(stage, \"TrayBox/TrayMaterial\")\n\ntexprim = UsdShade.TextureFile.Define(stage, \"TrayBox/TrayMaterial/TexBaseColor\")\ntexprim.CreateFileAttr(\"/output/textures/wood_basecolor.png\")\ntexprim.CreateFormatAttr(\"png\")\ntexprim.CreateTextureCoordinateAttribute(\"st\")\ntexprim.CreateWrapSAttr(\"repeat\")\ntexprim.CreateWrapTAttr(\"repeat\")\n\nroughprim = UsdShade.TextureFile.Define(stage, \"TrayBox/TrayMaterial/TexRoughness\")\nroughprim.CreateFileAttr(\"/output/textures/wood_roughness.png\")\nroughprim.CreateFormatAttr(\"png\")\nroughprim.CreateTextureCoordinateAttribute(\"st\")\nroughprim.CreateWrapSAttr(\"repeat\")\nroughprim.CreateWrapTAttr(\"repeat\")\n\nsurf = UsdShade.Shader.Define(stage, \"TrayBox/TrayMaterial/PreviewSurface\")\nsurf.CreateInfoAttr(\"source\", \"UsdPreviewSurface\")\nsurf.CreateIdAttr(\"UsdPreviewSurface\")\nsurf.CreateInputAttr(\"baseColor:color3f\", Gf.Vec3f(0.87, 0.79, 0.62))\nsurf.CreateInputAttr(\"roughness:float\", 0.6)\nsurf.CreateInputAttr(\"metallic:float\", 0.0)\nsurf.CreateInputAttr(\"opacity:float\", 1.0)\n\n# wire base color texture -> baseColor.rgb\nUsd.CreateApproximateConnect? = None # placeholder, done via API below\n\n# Create the material surface output and bind.\nsurf_out = UsdShade.Tokens\nmat.CreateOutputAttr(\":surface\", UsdShade.Tokens.token)\n\n# connection via UsdShade\nbaseconn = surf.GetPrim().GetAttribute(\"baseColor:color3f\")\ntexconn = texprim.GetPrim().GetAttribute(\"rgb\")\nUsdShade.TexCoord\nUsdShade.ConnectAPI = None\n\n# Use UsdAPI to connect: texFile.rgb -> preview.baseColor.rgb\nUsdShade.Connect? = None\n\n# --- fallback: use simple constant via USD APIConnectionContext ---\nconn_ctx = None\n\nstage.GetRootLayer().Export(\"/output/asset.usd\")\nprint(\"built basic\")\n", + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-abf532ab73c5457d", + "content": "Successfully wrote to /tmp/build_asset.py", + "extra": { + "tool_name": "write", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 17093, + "completion_tokens": 2152, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 19245 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "76811214", + "pi_parent_id": "03c4931a", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R2wx31ERqkaC0CHOXGxJTmix", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 10, + "timestamp": "2026-10-21T17:08:08.815Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "I keep leaving placeholder junk. Let me write one clean, correct script.", + "reasoning_content": "I'm making a mess with the placeholder lines again. Let me be disciplined and write a clean, correct script. Let me recall the correct API for connecting and binding materials in USD via Python.\n\nFor binding: `mat.BindDirect(mesh.GetPrim(), UsdShade.Tokens.over)` or `BindInherited`. Actually the methods are on `UsdShade.Material`: `BindDirect(target, bindingApi, ...)` and `BindInherited`. Or use `UsdShade.MaterialBindingAPI(mat)` then `.Bind(target)`.\n\nSimplest reliable: \n```\nmat.BindDirect(mesh.GetPrim(), UsdShade.Tokens.over)\n```\n\nFor connecting texture to shader:\n```\nUsdShade.ConnectSourceAttrToDestAttr(tex_file_prim, \"rgb\", surf_prim, \"baseColor\")\n```\n`UsdShade.ConnectSourceAttrToDestAttr(sourcePrim, sourceAttrPath, destPrim, destAttrPath)` - yes this exists.\n\nLet me rewrite the whole file cleanly without any placeholder junk. Let me be careful.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-91a82798630b5852", + "function_name": "write", + "arguments": { + "content": "import numpy as np\nfrom pxr import Usd, UsdGeom, UsdPhysics, UsdShade, Gf, Vt, Sdf\n\n# --- Nominal dimensions (canonical metres XYZ) ---\nLx = 0.6357795000076294 # length (X)\nLy = 0.5118491649627686 # width (Y)\nLz = 0.30237919092178345 # height (Z)\nt = 0.012 # wall / floor thickness (thin plywood tray)\n\na = Lx / 2.0\nb = Ly / 2.0\nH = Lz\nai = a - t\nbi = b - t\nzt = t\n\ndef quad(pts, normal):\n pts = [np.asarray(p, dtype=float) for p in pts]\n n = np.asarray(normal, dtype=float); n /= np.linalg.norm(n)\n c = np.mean(pts, axis=0)\n v = [p - c for p in pts]\n up = np.array([0.0, 0.0, 1.0])\n if abs(np.dot(n, up)) > 0.9:\n up = np.array([1.0, 0.0, 0.0])\n x = np.cross(n, up); x /= np.linalg.norm(x)\n y = np.cross(n, x)\n ang = [np.arctan2(np.dot(vv, y), np.dot(vv, x)) for vv in v]\n order = np.argsort(ang)\n return [pts[i] for i in order]\n\nfaces = []\ndef add(pts, n):\n faces.append(quad(pts, n))\n\nadd([(-a,-b,0),(a,-b,0),(a,b,0),(-a,b,0)], (0,0,-1)) # bottom outer\nadd([(-a,-b,0),(a,-b,0),(a,-b,H),(-a,-b,H)], (0,-1,0)) # outer front\nadd([(-a,b,0),(a,b,0),(a,b,H),(-a,b,H)], (0,1,0)) # outer back\nadd([(-a,-b,0),(-a,b,0),(-a,b,H),(-a,-b,H)], (-1,0,0)) # outer left\nadd([(a,-b,0),(a,b,0),(a,b,H),(a,-b,H)], (1,0,0)) # outer right\nadd([(-ai,-bi,zt),(ai,-bi,zt),(ai,bi,zt),(-ai,bi,zt)], (0,0,1)) # inner floor\nadd([(-ai,-bi,zt),(ai,-bi,zt),(ai,-bi,H),(-ai,-bi,H)], (0,-1,0))\nadd([(-ai,bi,zt),(ai,bi,zt),(ai,bi,H),(-ai,bi,H)], (0,1,0))\nadd([(-ai,-bi,zt),(-ai,bi,zt),(-ai,bi,H),(-ai,-bi,H)], (-1,0,0))\nadd([(ai,-bi,zt),(ai,bi,zt),(ai,bi,H),(ai,-bi,H)], (1,0,0))\nadd([(-a,-b,H),(a,-b,H),(a,-bi,H),(-a,-bi,H)], (0,0,1)) # rims\nadd([(-a,bi,H),(a,bi,H),(a,b,H),(-a,b,H)], (0,0,1))\nadd([(-a,-b,H),(-a,b,H),(-ai,b,H),(-ai,-b,H)], (0,0,1))\nadd([(ai,-b,H),(ai,b,H),(a,b,H),(a,-b,H)], (0,0,1))\n\npoints, face_verts = [], []\nuv = [(0.0,0.0),(1.0,0.0),(1.0,1.0),(0.0,1.0)]\nfor f in faces:\n base = len(points)\n for p in f:\n points.append((float(p[0]), float(p[1]), float(p[2])))\n face_verts.append([base, base+1, base+2, base+3])\n\nfvert_pts = Vt.Vec3fList(len(points), points)\nfvert_uv = Vt.Vec2fList(len(points), [uv[i % 4] for i in range(len(points))])\n\nstage = Usd.Stage.CreateNew(\"/output/asset.usd\")\nUsdGeom.SetUnits(stage, UsdGeom.Tokens.meters)\nUsdGeom.SetUpAxis(stage, \"Z\")\n\nroot = UsdGeom.Xform.Define(stage, \"TrayBox\")\n\nmesh = UsdGeom.Mesh.Define(stage, \"TrayBox/Shell\")\nmesh.CreatePointsAttr(fvert_pts)\nmesh.CreateFaceVertexIndicesAttr(face_verts)\nmesh.CreateFaceVertexUVsAttr(fvert_uv)\nfvnormals = []\nfor f in faces:\n nrm = np.cross(f[1]-f[0], f[2]-f[0]); nrm /= (np.linalg.norm(nrm)+1e-12)\n for _ in range(4):\n fvnormals.append((float(nrm[0]), float(nrm[1]), float(nrm[2])))\nmesh.CreateFaceVertexNormalsAttr(Vt.Vec3fList(len(fvnormals), fvnormals))\nmesh.CreateSubdivisionSchemeAttr(\"none\")\nmesh.SetDisplayColor(Gf.Vec3f(0.87, 0.79, 0.62))\n\n# Physics: single rigid (static) body with mesh collision\nrba = UsdPhysics.RigidBodyAPI.Apply(mesh, \"RigidBodyAPI\")\nrba.CreateKinematicEnabledAttr(True)\nrba.CreateCollisionEnabledAttr(True)\nrba.CreateDisableGravityAttr(True)\nmass_api = UsdPhysics.MassAPI.Apply(mesh, \"MassAPI\")\nmass_api.CreateMassAttr(2.5)\ncol = UsdPhysics.CollisionAPI.Apply(mesh, \"CollisionAPI\")\ncol.CreateCollisionEnabledAttr(True)\ncol.CreateCollisionAPIAttr(\"convexDecomposition\")\ncol.CreateConvexDecompositionMaxConvexHullsAttr(24)\ncol.CreateConvexDecompositionMaxVerticesPerHullAttr(64)\n\n# Material\nmat = UsdShade.Material.Define(stage, \"TrayBox/TrayMaterial\")\n\ntexprim = UsdShade.TextureFile.Define(stage, \"TrayBox/TrayMaterial/TexBaseColor\")\ntexprim.CreateFileAttr(\"/output/textures/wood_basecolor.png\")\ntexprim.CreateFormatAttr(\"png\")\ntexprim.CreateTextureCoordinateAttribute(\"st\")\ntexprim.CreateWrapSAttr(\"repeat\")\ntexprim.CreateWrapTAttr(\"repeat\")\n\nroughprim = UsdShade.TextureFile.Define(stage, \"TrayBox/TrayMaterial/TexRoughness\")\nroughprim.CreateFileAttr(\"/output/textures/wood_roughness.png\")\nroughprim.CreateFormatAttr(\"png\")\nroughprim.CreateTextureCoordinateAttribute(\"st\")\nroughprim.CreateWrapSAttr(\"repeat\")\nroughprim.CreateWrapTAttr(\"repeat\")\n\nsurf = UsdShade.Shader.Define(stage, \"TrayBox/TrayMaterial/PreviewSurface\")\nsurf.CreateInfoAttr(\"source\", \"UsdPreviewSurface\")\nsurf.CreateIdAttr(\"UsdPreviewSurface\")\nsurf.CreateInputAttr(\"baseColor:color3f\", Gf.Vec3f(0.87, 0.79, 0.62))\nsurf.CreateInputAttr(\"roughness:float\", 0.6)\nsurf.CreateInputAttr(\"metallic:float\", 0.0)\nsurf.CreateInputAttr(\"opacity:float\", 1.0)\n\n# wire textures\nUsdShade.ConnectSourceAttrToDestAttr(texprim.GetPrim(), \"rgb\",\n surf.GetPrim(), \"baseColor\")\nUsdShade.ConnectSourceAttrToDestAttr(roughprim.GetPrim(), \"r\",\n surf.GetPrim(), \"roughness\")\n\n# bind material to mesh (direct/over)\nmat.BindDirect(mesh.GetPrim(), UsdShade.Tokens.over)\n\nstage.GetRootLayer().Export(\"/output/asset.usd\")\nprint(\"built ok\")\n", + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-91a82798630b5852", + "content": "Successfully wrote to /tmp/build_asset.py", + "extra": { + "tool_name": "write", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 19268, + "completion_tokens": 2192, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 21460 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "9579e828", + "pi_parent_id": "28c9a55d", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RZoElqYdKQcWlpVL0JpCcZHL", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 11, + "timestamp": "2026-10-21T17:08:15.655Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me run the texture generator and the builder.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-add7d405dda6eb04", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 make_texture.py && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-add7d405dda6eb04", + "content": "textures done (1024, 1024)\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 58, in \n fvert_pts = Vt.Vec3fList(len(points), points)\n ^^^^^^^^^^^^\nAttributeError: module 'pxr.Vt' has no attribute 'Vec3fList'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 21484, + "completion_tokens": 57, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 21541 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "fe1349b9", + "pi_parent_id": "7c067805", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R6KbNui0S9EX9lbqtXsAOlkm", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 12, + "timestamp": "2026-10-21T17:08:31.263Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "The `Vec3fList` constructor isn't available. I'll use plain lists of `Gf.Vec3f`.", + "reasoning_content": "The API for Vec3fList is different. I should just use a list of Gf.Vec3f directly. `CreatePointsAttr` accepts a list of tuples or Gf.Vec3f. Let me just pass a list of Gf.Vec3f. Similarly FaceVertexUVs accepts list of Gf.Vec2f. And FaceVertexNormals list of Gf.Vec3f.\n\nLet me fix those lines.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a7238ba16a1dcb9f", + "function_name": "edit", + "arguments": { + "path": "/tmp/build_asset.py", + "edits": [ + { + "oldText": "fvert_pts = Vt.Vec3fList(len(points), points)\nfvert_uv = Vt.Vec2fList(len(points), [uv[i % 4] for i in range(len(points))])", + "newText": "fvert_pts = [Gf.Vec3f(p) for p in points]\nfvert_uv = [Gf.Vec2f(uv[i % 4]) for i in range(len(points))]" + }, + { + "oldText": "mesh.CreateFaceVertexNormalsAttr(Vt.Vec3fList(len(fvnormals), fvnormals))", + "newText": "mesh.CreateFaceVertexNormalsAttr([Gf.Vec3f(n) for n in fvnormals])" + } + ] + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a7238ba16a1dcb9f", + "content": "Successfully replaced 2 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 21649, + "completion_tokens": 323, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 21972 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "56def2f3", + "pi_parent_id": "a3ad713f", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RwmQtvVjAe4wQTqG6W3yQGTh", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 13, + "timestamp": "2026-10-21T17:08:38.355Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me run the build again.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a77088a6195d8c4a", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a77088a6195d8c4a", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 62, in \n UsdGeom.SetUnits(stage, UsdGeom.Tokens.meters)\n ^^^^^^^^^^^^^^^^\nAttributeError: module 'pxr.UsdGeom' has no attribute 'SetUnits'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 22001, + "completion_tokens": 48, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 22049 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "f0c14a11", + "pi_parent_id": "994e6555", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RCSe0lpVqCMd8t6ZtDhyj7SO", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 14, + "timestamp": "2026-10-21T17:08:47.673Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`SetUnits` and `SetUpAxis` aren't module functions. They're on `UsdGeom.ModelAPI` applied to the stage root. Let me use:\n```\nUsdGeom.ModelAPI(stage.GetPseudoRoot()).CreateUnitsAttr(\"meters\")\nUsdGeom.ModelAPI(stage.GetPseudoRoot()).CreateUpAxisAttr(\"Z\")\n```\n\nLet me fix that.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-88adef333726c84c", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "UsdGeom.ModelAPI(stage.GetPseudoRoot()).CreateUnitsAttr(\"meters\")\nUsdGeom.ModelAPI(stage.GetPseudoRoot()).CreateUpAxisAttr(\"Z\")", + "oldText": "UsdGeom.SetUnits(stage, UsdGeom.Tokens.meters)\nUsdGeom.SetUpAxis(stage, \"Z\")" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-88adef333726c84c", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 22142, + "completion_tokens": 204, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 22346 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "7496cf7d", + "pi_parent_id": "369f96a9", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RhZlZiHsW576dq3LYUQLKXsn", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 15, + "timestamp": "2026-10-21T17:08:51.556Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me continue running the build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8b4da6c72127760c", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8b4da6c72127760c", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 62, in \n UsdGeom.ModelAPI(stage.GetPseudoRoot()).CreateUnitsAttr(\"meters\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'ModelAPI' object has no attribute 'CreateUnitsAttr'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 22375, + "completion_tokens": 48, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 22423 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "6683dad5", + "pi_parent_id": "ac88cf66", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Ru8pWgo4WeKWC5bWYtcXTmYw", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 16, + "timestamp": "2026-10-21T17:09:04.793Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me check the ModelAPI methods. Units and upAxis are actually stage-level metadata in USD (modelAPI). The method might be different. Let me check.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b00f92c4e9e2e89e", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"from pxr import UsdGeom; print([m for m in dir(UsdGeom.ModelAPI) if 'nit' in m or 'Axis' in m or 'pes' in m or 'Axis' in m])\"; python3 -c \"import pxr; print(pxr.UsdGeom.__file__)\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b00f92c4e9e2e89e", + "content": "['GetSchemaClassPrimDefinition', '__init__', '__init_subclass__']\nTraceback (most recent call last):\n File \"\", line 1, in \nAttributeError: module 'pxr' has no attribute 'UsdGeom'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 22525, + "completion_tokens": 134, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 22659 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "0b70c399", + "pi_parent_id": "271c0ff6", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RGLgZt64dV02ilI7P4xJYa1c", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 17, + "timestamp": "2026-10-21T17:09:23.834Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "ModelAPI seems to not expose CreateUnitsAttr. Let me check what methods it has. Actually ModelAPI might be a class that needs to be applied. Let me check its attributes.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8b08790c5e58dbc4", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"\nfrom pxr import UsdGeom, Usd\nstage = Usd.Stage.CreateInMemory()\nm = UsdGeom.ModelAPI(stage.GetPseudoRoot())\nprint([m2 for m2 in dir(m) if not m2.startswith('__')])\n\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8b08790c5e58dbc4", + "content": "['Apply', 'CanApply', 'ComputeExtentsHint', 'ComputeModelDrawMode', 'CreateConstraintTarget', 'CreateModelApplyDrawModeAttr', 'CreateModelCardGeometryAttr', 'CreateModelCardTextureXNegAttr', 'CreateModelCardTextureXPosAttr', 'CreateModelCardTextureYNegAttr', 'CreateModelCardTextureYPosAttr', 'CreateModelCardTextureZNegAttr', 'CreateModelCardTextureZPosAttr', 'CreateModelCardVisibilityAttr', 'CreateModelDrawModeAttr', 'CreateModelDrawModeColorAttr', 'Get', 'GetConstraintTarget', 'GetConstraintTargets', 'GetExtentsHint', 'GetExtentsHintAttr', 'GetModelApplyDrawModeAttr', 'GetModelCardGeometryAttr', 'GetModelCardTextureXNegAttr', 'GetModelCardTextureXPosAttr', 'GetModelCardTextureYNegAttr', 'GetModelCardTextureYPosAttr', 'GetModelCardTextureZNegAttr', 'GetModelCardTextureZPosAttr', 'GetModelCardVisibilityAttr', 'GetModelDrawModeAttr', 'GetModelDrawModeColorAttr', 'GetPath', 'GetPrim', 'GetSchemaAttributeNames', 'GetSchemaClassPrimDefinition', 'GetSchemaKind', 'IsAPISchema', 'IsAppliedAPISchema', 'IsConcrete', 'IsMultipleApplyAPISchema', 'IsTyped', 'SetExtentsHint', '_GetStaticTfType']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 22737, + "completion_tokens": 127, + "cached_tokens": 21952, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 22864 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "dc684da7", + "pi_parent_id": "02297295", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RKf6kdAa38MnznUyuWBVVQAM", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 18, + "timestamp": "2026-10-21T17:09:53.837Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "`upAxis`/`units` are layer metadata in this build. Let me set them via the SDF layer.", + "reasoning_content": "ModelAPI doesn't expose units/upAxis. In USD, `upAxis` and `units` are stage-level metadata set via `UsdGeom.ModelAPI`... actually they're set through the root layer metadata. The proper way: `UsdGeom.ModelAPI` doesn't have it in this build. \n\nThe canonical way to set upAxis/units is via `Sdf` metadata on the root layer, OR use `UsdGeom.ModelAPI` \u2014 but this build doesn't expose CreateUnitsAttr.\n\nActually, the standard approach: `UsdGeom.SetUnits` and `UsdGeom.SetUpAxis` do exist in full USD. This pxr build (maybe standalone) lacks them. \n\nI can set them via the root layer metadata directly:\n```\nlayer = stage.GetRootLayer()\nlayer.GetMetadata().upAxis = \"Z\"\nlayer.GetMetadata().units = \"meters\"\n```\nThe Sdf.Layer metadata has `upAxis` and `units` fields. Let me set them via `layer.SetMetadata`? Actually SDF metadata: `Sdf.Layer` has a `GetMetadata()` returning `Sdf.LayerMetadata` with fields `upAxis`, `units`, `defaultPrim`. Let me set them.\n\nLet me use:\n```\nmeta = layer.GetMetadata()\nmeta.upAxis = \"Z\"\nmeta.units = \"meters\"\nlayer.SetMetadata(meta)\n```\n\nLet me test that. Let me update the script.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9fda7873d4f682bf", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"\nfrom pxr import Usd, UsdGeom, Sdf\nstage = Usd.Stage.CreateInMemory()\nlayer = stage.GetRootLayer()\nmeta = layer.GetMetadata()\nmeta.upAxis = 'Z'\nmeta.units = 'meters'\nlayer.SetMetadata(meta)\nprint('meta ok', layer.GetMetadata().upAxis, layer.GetMetadata().units)\n\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9fda7873d4f682bf", + "content": "Traceback (most recent call last):\n File \"\", line 5, in \nAttributeError: 'Layer' object has no attribute 'GetMetadata'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 23179, + "completion_tokens": 451, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 23630 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "424b0093", + "pi_parent_id": "e3a2b91a", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RsXPrII7maNQvMdx5okBD402", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 19, + "timestamp": "2026-10-21T17:10:00.682Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me try the Sdf metadata approach. In this pxr build, `Sdf.Layer` might expose metadata differently. Let me check.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-aa316a9f95745667", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"\nfrom pxr import Sdf\nlayer = Sdf.Layer.CreateNew('/tmp/t.usd')\nprint([m for m in dir(layer) if 'eta' in m or 'pAxis' in m or 'nit' in m])\n\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-aa316a9f95745667", + "content": "['DetachedLayerRules', 'GetDetachedLayerRules', 'IsDetached', 'IsIncludedByDetachedLayerRules', 'SetDetachedLayerRules', '__getattribute__', '__init__', '__init_subclass__', '__setattr__']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 23689, + "completion_tokens": 115, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 23804 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "04e7997e", + "pi_parent_id": "706d9950", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RqrOdf2Oz7WPtVj4pjHAhf2Y", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 20, + "timestamp": "2026-10-21T17:10:31.311Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me try setting upAxis/units differently. In this build, I can add them as attributes on the prim, or use `UsdGeom.ModelAPI` for upAxis. Actually, let me check if there's a simpler path \u2014 maybe write the layer metadata via the text `.usda` or just set it through the Sdf metadata.\n\nActually the standard way in pxr is:\n```\nUsdGeom.ModelAPI(stage.GetPseudoRoot())\n```\nBut that API didn't have units/upAxis.\n\nLet me try `Sdf.AssetPath`... no. Let me try the `upAxis` via stage metadata. In newer USD, `upAxis` and `units` are stored in the root layer's Sdf metadata. Let me try to access via `layer` with a different method.\n\nLet me check what the Sdf layer metadata API is in this version.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-95e8659045a90e04", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"\nfrom pxr import Sdf\nprint([m for m in dir(Sdf.Layer) if not m.startswith('_')])\n\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-95e8659045a90e04", + "content": "['AddToMutedLayers', 'Apply', 'ApplyRootPrimOrder', 'CanApply', 'Clear', 'ClearColorConfiguration', 'ClearColorManagementSystem', 'ClearCustomLayerData', 'ClearDefaultPrim', 'ClearEndTimeCode', 'ClearExpressionVariables', 'ClearFramePrecision', 'ClearFramesPerSecond', 'ClearOwner', 'ClearRelocates', 'ClearSessionOwner', 'ClearStartTimeCode', 'ClearTimeCodesPerSecond', 'ColorConfigurationKey', 'ColorManagementSystemKey', 'CommentKey', 'ComputeAbsolutePath', 'ConvertDefaultPrimPathToToken', 'ConvertDefaultPrimTokenToPath', 'CreateAnonymous', 'CreateIdentifier', 'CreateNew', 'DetachedLayerRules', 'DocumentationKey', 'DumpLayerInfo', 'EndFrameKey', 'EndTimeCodeKey', 'EraseTimeSample', 'Export', 'ExportToString', 'Find', 'FindOrOpen', 'FindOrOpenRelativeToLayer', 'FindRelativeToLayer', 'FramePrecisionKey', 'FramesPerSecondKey', 'GetAssetInfo', 'GetAssetName', 'GetAttributeAtPath', 'GetBracketingTimeSamples', 'GetBracketingTimeSamplesForPath', 'GetCompositionAssetDependencies', 'GetDefaultPrimAsPath', 'GetDetachedLayerRules', 'GetDisplayName', 'GetDisplayNameFromIdentifier', 'GetExternalAssetDependencies', 'GetExternalReferences', 'GetFileFormat', 'GetFileFormatArguments', 'GetLoadedLayers', 'GetMutedLayers', 'GetNumTimeSamplesForPath', 'GetObjectAtPath', 'GetPreviousTimeSampleForPath', 'GetPrimAtPath', 'GetPropertyAtPath', 'GetRelationshipAtPath', 'HasColorConfiguration', 'HasColorManagementSystem', 'HasCustomLayerData', 'HasDefaultPrim', 'HasEndTimeCode', 'HasExpressionVariables', 'HasFramePrecision', 'HasFramesPerSecond', 'HasOwnedSubLayers', 'HasOwner', 'HasRelocates', 'HasSessionOwner', 'HasStartTimeCode', 'HasTimeCodesPerSecond', 'Import', 'ImportFromString', 'IsAnonymousLayerIdentifier', 'IsDetached', 'IsIncludedByDetachedLayerRules', 'IsMuted', 'LayerRelocatesKey', 'ListAllTimeSamples', 'ListTimeSamplesForPath', 'New', 'OpenAsAnonymous', 'OwnerKey', 'QueryTimeSample', 'Reload', 'ReloadLayers', 'RemoveFromMutedLayers', 'RemoveInertSceneDescription', 'RemovePropertyIfHasOnlyRequiredFields', 'Save', 'ScheduleRemoveIfInert', 'SessionOwnerKey', 'SetDetachedLayerRules', 'SetMuted', 'SetPermissionToEdit', 'SetPermissionToSave', 'SetTimeSample', 'SplitIdentifier', 'StartFrameKey', 'StartTimeCodeKey', 'StreamsData', 'TimeCodesPerSecondKey', 'TransferContent', 'Traverse', 'UpdateAssetInfo', 'UpdateCompositionAssetDependency', 'UpdateExternalReference', 'anonymous', 'colorConfiguration', 'colorManagementSystem', 'comment', 'customLayerData', 'defaultPrim', 'dirty', 'documentation', 'empty', 'endTimeCode', 'expired', 'expressionVariables', 'externalReferences', 'fileExtension', 'framePrecision', 'framesPerSecond', 'hasOwnedSubLayers', 'identifier', 'owner', 'permissionToEdit', 'permissionToSave', 'pseudoRoot', 'realPath', 'relocates', 'repositoryPath', 'resolvedPath', 'rootPrimOrder', 'rootPrims', 'sessionOwner', 'startTimeCode', 'subLayerOffsets', 'subLayerPaths', 'timeCodesPerSecond', 'version']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 23869, + "completion_tokens": 247, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 24116 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "c1f65a4a", + "pi_parent_id": "c9f00e2e", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RTPBIREJWuxPcB74nM4c9Sbz", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 21, + "timestamp": "2026-10-21T17:11:46.484Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "This build lacks direct `upAxis`/`units` setters. I'll round-trip through ASCII USD to inject the layer metadata. Let me fix the script.", + "reasoning_content": "This build of Sdf.Layer doesn't expose `upAxis`/`units` metadata directly. Let me instead just add `upAxis` and `units` as custom metadata via the layer. Actually, in USD the standard is that `upAxis` and `units` are SDF layer metadata. But this build might store them differently. \n\nActually, I recall the correct API is `UsdGeom.ModelAPI` \u2014 but the cleanest is to just set them in the USD file text. Since I export to `.usd` (binary) and re-import, I could just post-process. But easier: let me write the file as `.usda`? No, the target is `asset.usd`.\n\nAlternatively, I can set units and upAxis via the Sdf layer's custom data or just add them as layer metadata using `layer.SetTimeCodesPerSecond`? No.\n\nLet me reconsider. The upAxis in USD is stored as the SDF layer's `upAxis` metadata. In this pxr build, `Sdf.Layer` python bindings apparently don't expose it as a settable property. But I can edit the exported file: since asset is binary usd, I can't easily text-edit.\n\nOption: Create the stage, build, then use `stage.Export` to a `.usda`? No, target is .usd.\n\nBetter: Let me write the whole asset as USDA text content and save as .usd? The extension .usd triggers binary. Actually I can save the file with `.usd` extension but as ASCII? No, usdcat would read it; the format is determined by extension.\n\nSimplest robust approach: Build the USD in-memory, export to a temporary `.usda` (ASCII), then insert the `upAxis` and `units` into the `#usda` header metadata, then import that and re-export to `.usd` binary. Or just deliver the file as `.usda`? The contract says asset.usd. I should keep .usd.\n\nActually, let me reconsider \u2014 maybe I can set these through the Sdf layer using `Usd.Sdf` metadata via `Sdf.AssetIdentifier`... no.\n\nLet me try a cleaner approach: set units/upAxis via the root prim's... no, they're layer-level.\n\nActually wait \u2014 maybe the binding does support it but the method name differs. Let me check the `Sdf.Layer` \u2014 there's no `metadata`. But the standard `usdview`/Omniverse reads upAxis from the layer metadata `upAxis`. \n\nLet me test whether I can just build the file with a header that includes upAxis/units by writing USDA text then converting. I'll:\n1. Build in-memory.\n2. `UsdUtils.Export(stage, '/tmp/asset_ascii.usda')`\n3. Read that file, it starts with `#usda 1.0` and `( upAxis = \"Y\" ... )`? Actually it won't have upAxis unless set.\n4. I'll rewrite the header line to include `upAxis = \"Z\", units = \"meters\", defaultPrim = \"TrayBox\"`.\n5. Save to asset.usd? No, .usda is ascii. I can load the edited usda with Sdf and Export to .usd binary.\n\nLet me just do: edit the ascii to add upAxis/units in header, then `Usd.Stage.Open` the edited usda and `Export` to /output/asset.usd.\n\nActually even simpler \u2014 I can just add upAxis/units to the exported .usd by re-opening the ascii with the metadata parsed. When I open a .usda that has `upAxis=\"Z\"` in its `( ... )` metadata block, Sdf will parse it, and then Export to .usd preserves it. \n\nBut actually, can I just directly write the metadata? Let me try `stage.GetRootLayer()` then use `Sdf` metadata. Hmm the layer doesn't expose set.\n\nLet me go with the ASCII round-trip. Let me modify the build script:\n- Export to /tmp/asset_ascii.usda\n- read, replace the `(` metadata line to inject upAxis/units/defaultPrim\n- write back\n- Open and export to /output/asset.usd\n\nLet me check the UsdUtils import. `from pxr import UsdUtils`. `UsdUtils.Export(stage, path)`.\n\nLet me update the script. First, remove the failing SetUnits/SetUpAxis lines. Then add the post-processing.\n\nAlso I set `defaultPrim` \u2014 good practice for SimReady (SR.002 maybe \"has a default prim / single root\"). Let me set defaultPrim = \"TrayBox\".\n\nLet me also reconsider: do I even need upAxis? The requirement might be there. Let me set it to be safe.\n\nLet me rewrite the build script's stage section and add post-processing at the end.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-95fc2a7870c5f1ff", + "function_name": "edit", + "arguments": { + "path": "/tmp/build_asset.py", + "edits": [ + { + "oldText": "stage = Usd.Stage.CreateNew(\"/output/asset.usd\")\nUsdGeom.ModelAPI(stage.GetPseudoRoot()).CreateUnitsAttr(\"meters\")\nUsdGeom.ModelAPI(stage.GetPseudoRoot()).CreateUpAxisAttr(\"Z\")\n\nroot = UsdGeom.Xform.Define(stage, \"TrayBox\")", + "newText": "stage = Usd.Stage.CreateInMemory()\n\nroot = UsdGeom.Xform.Define(stage, \"TrayBox\")" + }, + { + "oldText": "mat.BindDirect(mesh.GetPrim(), UsdShade.Tokens.over)\n\nstage.GetRootLayer().Export(\"/output/asset.usd\")\nprint(\"built ok\")", + "newText": "mat.BindDirect(mesh.GetPrim(), UsdShade.Tokens.over)\n\nfrom pxr import UsdUtils\nascii_path = \"/tmp/asset_ascii.usda\"\nUsdUtils.Export(stage, ascii_path)\n\nwith open(ascii_path, \"r\") as fh:\n text = fh.read()\n\n# inject upAxis / units / defaultPrim into the #usda header metadata block\nimport re\nif text.startswith(\"#usda\"):\n header = text.split(\"#usda\", 1)[1]\n m = re.search(r\"\\(([^)]*)\\)\", text)\n if m:\n inner = m.group(1)\n additions = []\n if \"upAxis\" not in inner:\n additions.append('upAxis = \"Z\"')\n if \"units\" not in inner:\n additions.append('units = \"meters\"')\n if \"defaultPrim\" not in inner:\n additions.append('defaultPrim = \"TrayBox\"')\n newinner = \", \".join([x for x in [inner.strip()] if x] + additions)\n text = text.replace(m.group(0), \"(\" + newinner + \")\", 1)\nwith open(ascii_path, \"w\") as fh:\n fh.write(text)\n\nstage2 = Usd.Stage.Open(ascii_path)\nUsdUtils.Export(stage2, \"/output/asset.usd\")\nprint(\"built ok\")\nprint(\"meta:\", stage2.GetRootLayer().GetMetadata())" + } + ] + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-95fc2a7870c5f1ff", + "content": "Successfully replaced 2 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 24875, + "completion_tokens": 1647, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 26522 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "00330265", + "pi_parent_id": "91bc4c2f", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RqeGCFy5LXeYLcTWQxAThv43", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 22, + "timestamp": "2026-10-21T17:11:53.377Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "I'm at round 21, 43 remain. Let me be efficient. Run the build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b5f9995aadcffd78", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b5f9995aadcffd78", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 63, in \n root = UsdGeom.Xform.Define(stage, \"TrayBox\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\npxr.Tf.ErrorException: \n\tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdStage::_IsValidPathForCreatingPrim' at line 3775 in file /opt/USD/pxr/usd/usd/stage.cpp : 'Path must be an absolute path: '\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 26549, + "completion_tokens": 63, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 26612 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "5027838b", + "pi_parent_id": "7cccc066", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RH4qxo8xDqHND7dKN3rMugbr", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 23, + "timestamp": "2026-10-21T17:12:08.004Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "Paths must be absolute. Let me fix them.", + "reasoning_content": "Path must be absolute: \"/TrayBox\". Let me fix all Define paths to be absolute.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-bf7423e88f495eae", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && sed -i 's/\"TrayBox\"\\/\\?/\"\\/TrayBox\"\\/g' build_asset.py\n# simpler: replace all the prim path strings\npython3 - <<'EOF'\nimport re\ns = open('/tmp/build_asset.py').read()\ns = s.replace('\"TrayBox\")', '\"/TrayBox\")')\ns = s.replace('\"TrayBox/Shell\"', '\"/TrayBox/Shell\"')\ns = s.replace('\"TrayBox/TrayMaterial\"', '\"/TrayBox/TrayMaterial\"')\ns = s.replace('\"TrayBox/TrayMaterial/TexBaseColor\"', '\"/TrayBox/TrayMaterial/TexBaseColor\"')\ns = s.replace('\"TrayBox/TrayMaterial/TexRoughness\"', '\"/TrayBox/TrayMaterial/TexRoughness\"')\ns = s.replace('\"TrayBox/TrayMaterial/PreviewSurface\"', '\"/TrayBox/TrayMaterial/PreviewSurface\"')\ns = s.replace('defaultPrim = \"TrayBox\"', 'defaultPrim = \"/TrayBox\"')\nopen('/tmp/build_asset.py','w').write(s)\nprint(\"patched\")\nEOF\ngrep -n 'Define(stage' build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-bf7423e88f495eae", + "content": "sed: -e expression #1, char 30: unterminated `s' command\npatched\n63:root = UsdGeom.Xform.Define(stage, \"/TrayBox\")\n65:mesh = UsdGeom.Mesh.Define(stage, \"/TrayBox/Shell\")\n92:mat = UsdShade.Material.Define(stage, \"/TrayBox/TrayMaterial\")\n94:texprim = UsdShade.TextureFile.Define(stage, \"/TrayBox/TrayMaterial/TexBaseColor\")\n101:roughprim = UsdShade.TextureFile.Define(stage, \"/TrayBox/TrayMaterial/TexRoughness\")\n108:surf = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/PreviewSurface\")", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 26766, + "completion_tokens": 340, + "cached_tokens": 17248, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 27106 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "2fcfa221", + "pi_parent_id": "990afcd3", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RCzZb0MPG9pfPDulFREG53OH", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 24, + "timestamp": "2026-10-21T17:12:13.410Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Good, the python patch worked. Let me run the build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9800494a30118fce", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9800494a30118fce", + "content": "Warning: in GetType at line 379 of /opt/USD/pxr/base/vt/value.cpp -- Returning unknown type for VtValue with unregistered C++ type vector >\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 67, in \n mesh.CreateFaceVertexIndicesAttr(face_verts)\npxr.Tf.ErrorException: \n\tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdStage::_SetValue' at line 2045 in file /opt/USD/pxr/usd/usd/stage.cpp : 'Type mismatch for : expected 'VtArray', got 'vector >''\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 27299, + "completion_tokens": 54, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 27353 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "fc6f690e", + "pi_parent_id": "7a5e2b34", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RoQwt1tzoVgB4rjxRNpwljHg", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 25, + "timestamp": "2026-10-21T17:12:24.619Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "`faceVertexIndices` needs a flat int array with `-1` separators.", + "reasoning_content": "The faceVertexIndices needs a flat list of ints (not list of lists). In USD, `faceVertexIndices` is a flat int array where faces are terminated by a -1 separator. So I need to flatten: for each face, append the 4 indices then -1.\n\nLet me fix. face_verts currently is list of [i0,i1,i2,i3]. I need flat: [i0,i1,i2,i3,-1, j0,...,-1,...].", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b6f601e3b72f2d58", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "flat_idx = []\nfor f in face_verts:\n flat_idx.extend(f)\n flat_idx.append(-1)\nfvert_pts = [Gf.Vec3f(p) for p in points]\nfvert_uv = [Gf.Vec2f(uv[i % 4]) for i in range(len(points))]", + "oldText": "fvert_pts = [Gf.Vec3f(p) for p in points]\nfvert_uv = [Gf.Vec2f(uv[i % 4]) for i in range(len(points))]" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b6f601e3b72f2d58", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 27554, + "completion_tokens": 291, + "cached_tokens": 26656, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 27845 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "396eed3e", + "pi_parent_id": "ca4e48a5", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RHSfKPfnZI3XkHFRgwcR6Qm2", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 26, + "timestamp": "2026-10-21T17:12:32.135Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Now update the CreateFaceVertexIndicesAttr call to use flat_idx.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a29d502986280d0e", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "mesh.CreateFaceVertexIndicesAttr(flat_idx)", + "oldText": "mesh.CreateFaceVertexIndicesAttr(face_verts)" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a29d502986280d0e", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 27874, + "completion_tokens": 89, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 27963 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "439e3fa7", + "pi_parent_id": "45343554", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rv0Fb3DlIT0LiH9PR0fHcxj6", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 27, + "timestamp": "2026-10-21T17:12:37.552Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "I'm at 26, 38 remain. Let me run the build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-bacedf9682c551f0", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-bacedf9682c551f0", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 72, in \n mesh.CreateFaceVertexUVsAttr(fvert_uv)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Mesh' object has no attribute 'CreateFaceVertexUVsAttr'. Did you mean: 'CreateFaceVertexCountsAttr'?\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 27992, + "completion_tokens": 59, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 28051 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "8d7d2950", + "pi_parent_id": "265b636f", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RkvpDgcnMDbBfo21rzq1Agk6", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 28, + "timestamp": "2026-10-21T17:12:50.729Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "UVs is a primvar. Let me use `PrimvarsAPI`.", + "reasoning_content": "The UVs attribute in USD is `primvar:name` \u2014 it's a primvar, not a schema attribute. I need to create it via `UsdGeom.PrimvarsAPI` or `mesh.GetPrim().CreateAttribute(\"primvars:st\")`. Actually the standard is to use `UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePointPrimvarAttr(\"st\", Sdf.ValueTypeNames.Point2f)`. Let me use that.\n\nLet me replace the CreateFaceVertexUVsAttr call.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-86bd4e1d6770ad13", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "from pxr import Sdf\nUsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePointPrimvarAttr(\"st\", Sdf.ValueTypeNames.Point2f).Set(fvert_uv)", + "oldText": "mesh.CreateFaceVertexUVsAttr(fvert_uv)" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-86bd4e1d6770ad13", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 28153, + "completion_tokens": 231, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 28384 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "05c1367a", + "pi_parent_id": "df5299fb", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R4laqRmPjlrTANmAlByxcR6J", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 29, + "timestamp": "2026-10-21T17:12:55.487Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me run the build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a42751c8a36c5361", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a42751c8a36c5361", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 73, in \n UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePointPrimvarAttr(\"st\", Sdf.ValueTypeNames.Point2f).Set(fvert_uv)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'PrimvarsAPI' object has no attribute 'CreatePointPrimvarAttr'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 28413, + "completion_tokens": 47, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 28460 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "d5ae8ea5", + "pi_parent_id": "6e20c506", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RSew6cy9HyES8l6Sf7s46iuY", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 30, + "timestamp": "2026-10-21T17:13:03.915Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me check PrimvarsAPI methods.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-91e32adba7f61253", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"\nfrom pxr import UsdGeom\nprint([m for m in dir(UsdGeom.PrimvarsAPI) if 'reate' in m or 'rimvar' in m])\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-91e32adba7f61253", + "content": "['BlockPrimvar', 'CreateIndexedPrimvar', 'CreateNonIndexedPrimvar', 'CreatePrimvar', 'FindIncrementallyInheritablePrimvars', 'FindInheritablePrimvars', 'FindPrimvarWithInheritance', 'FindPrimvarsWithInheritance', 'GetAuthoredPrimvars', 'GetPrimvar', 'GetPrimvars', 'GetPrimvarsWithAuthoredValues', 'GetPrimvarsWithValues', 'HasPossiblyInheritedPrimvar', 'HasPrimvar', 'RemovePrimvar']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 28581, + "completion_tokens": 81, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 28662 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "d6ff4c76", + "pi_parent_id": "bdec759b", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RizytQmIrnG2fdUPN0hKq8Li", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 31, + "timestamp": "2026-10-21T17:13:14.949Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me use `CreatePrimvar`. `UsdGeom.PrimvarsAPI(prim).CreatePrimvar(\"st\", Sdf.ValueTypeNames.Point2f, True)` where True = indexed. Point2f is indexed by default (point primvar). Then Set with the list.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-99da8057a89cb130", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "from pxr import Sdf\npv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.Point2f, True)\npv.Set(fvert_uv)", + "oldText": "from pxr import Sdf\nUsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePointPrimvarAttr(\"st\", Sdf.ValueTypeNames.Point2f).Set(fvert_uv)" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-99da8057a89cb130", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 28786, + "completion_tokens": 199, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 28985 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "48f999a6", + "pi_parent_id": "6edf6197", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rol1dWQ04sAVRI9IXFSt5gZH", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 32, + "timestamp": "2026-10-21T17:13:21.927Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "33 rounds remain. Let me run the build now.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-87b22a1eff533a42", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-87b22a1eff533a42", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 73, in \n pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.Point2f, True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: type object 'ValueTypeNames' has no attribute 'Point2f'. Did you mean: 'Point3f'?\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 29014, + "completion_tokens": 53, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 29067 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "bc67bd69", + "pi_parent_id": "d106915c", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RWFwYKA7qG1NAaNUvUoVdrum", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 33, + "timestamp": "2026-10-21T17:13:31.707Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me check the correct type name. It's `Point3f`, `Vec2f`, `TfPoint2f`? In SDF, the type name for 2D float is `Point2f`. Let me check ValueTypeNames.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-87619da65446a65a", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"\nfrom pxr import Sdf\nprint([m for m in dir(Sdf.ValueTypeNames) if 'Point' in m or 'Vec2' in m])\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-87619da65446a65a", + "content": "['Point3d', 'Point3dArray', 'Point3f', 'Point3fArray', 'Point3h', 'Point3hArray']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 29184, + "completion_tokens": 121, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 29305 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "9174d441", + "pi_parent_id": "b4df5c75", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RyAlXRdTr17C04CZmda44ZA6", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 34, + "timestamp": "2026-10-21T17:13:44.972Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "The `Point2f` type name isn't available. But USD uses `point2f` for UVs. Actually in USD, UVs are stored as `point2f`. Let me check available 2D types.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a3589681401dfb7e", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"\nfrom pxr import Sdf\nnames = [m for m in dir(Sdf.ValueTypeNames) if '2f' in m or '2F' in m]\nprint(names)\nprint([m for m in dir(Sdf.ValueTypeNames) if 'Vec2' in m or 'vec2' in m.lower()])\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a3589681401dfb7e", + "content": "['TexCoord2f', 'TexCoord2fArray']\n[]", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 29353, + "completion_tokens": 153, + "cached_tokens": 17248, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 29506 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "ce19ad37", + "pi_parent_id": "6120d331", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RF3b6jgPFokJAazOoK3luNAB", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 35, + "timestamp": "2026-10-21T17:13:58.848Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "`TexCoord2f` is the type. Let me use it.", + "reasoning_content": "`TexCoord2f` exists. Let me use `Sdf.ValueTypeNames.TexCoord2f` with indexed=True.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-bc5827570a2975a5", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.TexCoord2f, True)", + "oldText": "pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.Point2f, True)" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-bc5827570a2975a5", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 29534, + "completion_tokens": 159, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 29693 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "f36efa4d", + "pi_parent_id": "14987167", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rc6depFAtAizGe5TJ9IRsl6w", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 36, + "timestamp": "2026-10-21T17:14:03.706Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "29 remain. Let me run the build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a66b385b89d5ab03", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a66b385b89d5ab03", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 73, in \n pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.TexCoord2f, True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nBoost.Python.ArgumentError: Python argument types in\n PrimvarsAPI.CreatePrimvar(PrimvarsAPI, str, ValueTypeName, bool)\ndid not match C++ signature:\n CreatePrimvar(pxrInternal_v0_26_8__pxrReserved__::UsdGeomPrimvarsAPI {lvalue}, pxrInternal_v0_26_8__pxrReserved__::TfToken name, pxrInternal_v0_26_8__pxrReserved__::SdfValueTypeName typeName, pxrInternal_v0_26_8__pxrReserved__::TfToken interpolation='', int elementSize=-1)\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 29722, + "completion_tokens": 51, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 29773 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "055d4ead", + "pi_parent_id": "f67885d7", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RvFozHmIYwsh09FspdYgJQL7", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 37, + "timestamp": "2026-10-21T17:14:17.190Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "Third arg is interpolation token, not a bool. Let me fix.", + "reasoning_content": "The third argument is interpolation (a TfToken), not a bool. Let me fix: `CreatePrimvar(\"st\", Sdf.ValueTypeNames.TexCoord2f, \"indexed\")`.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9d41cccfed68c018", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.TexCoord2f, \"indexed\")", + "oldText": "pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.TexCoord2f, True)" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9d41cccfed68c018", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 30022, + "completion_tokens": 177, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 30199 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "633f44f4", + "pi_parent_id": "61e19ca3", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RJBWUde8EFzOXmPW0dhoer00", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 38, + "timestamp": "2026-10-21T17:14:21.950Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "I'm running low on my budget with 27 rounds left, so I need to execute the build and validation process efficiently.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-af301f7f3530883f", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-af301f7f3530883f", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 73, in \n pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.TexCoord2f, \"indexed\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\npxr.Tf.ErrorException: \n\tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdGeomPrimvar::SetInterpolation' at line 155 in file /opt/USD/pxr/usd/usdGeom/primvar.cpp : 'Attempt to set invalid primvar interpolation \"indexed\" for attribute /TrayBox/Shell.primvars:st'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 30228, + "completion_tokens": 67, + "cached_tokens": 29792, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 30295 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "c1aaf8ef", + "pi_parent_id": "ca430399", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RcMACEfHjmsgRZ3olcswRZan", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 39, + "timestamp": "2026-10-21T17:14:33.631Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "UV interpolation should be \"varying\".", + "reasoning_content": "\"indexed\" is not a valid interpolation. Valid interpolations: constant, uniform, vertex, faceVtx, varying. UVs are \"varying\". Let me use \"varying\".", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-945d2590c9512719", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.TexCoord2f, \"varying\")", + "oldText": "pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.TexCoord2f, \"indexed\")" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-945d2590c9512719", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 30493, + "completion_tokens": 172, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 30665 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "b5817e74", + "pi_parent_id": "1a9ba81a", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RowXQENdzaNLKir34F5o2MF8", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 40, + "timestamp": "2026-10-21T17:14:42.258Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "I'm down to 25 model rounds. Let me be efficient. Let me run the build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-86b0633636c12784", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-86b0633636c12784", + "content": "Warning: in GetType at line 379 of /opt/USD/pxr/base/vt/value.cpp -- Returning unknown type for VtValue with unregistered C++ type vector >\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 74, in \n pv.Set(fvert_uv)\npxr.Tf.ErrorException: \n\tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdStage::_SetValue' at line 2045 in file /opt/USD/pxr/usd/usd/stage.cpp : 'Type mismatch for : expected 'GfVec2f', got 'vector >''\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 30694, + "completion_tokens": 62, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 30756 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "7c30c6ce", + "pi_parent_id": "a3632fb8", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RLFcZpTkWGsChK5MnIPo10A8", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 41, + "timestamp": "2026-10-21T17:15:10.150Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "UVs need an array type name. Let me use `TexCoord2fArray`.", + "reasoning_content": "The error: expected 'GfVec2f' singular? No \u2014 the attribute is indexed so it should expect an array. The warning about `vector` \u2014 the problem is my `fvert_uv` list contains `Gf.Vec2f` objects which when passed as a Python list of Gf.Vec2f should convert to VtArray. But it's complaining expected 'GfVec2f' (singular). That means the attribute was created as non-array (scalar) type? Because TexCoord2f is a scalar type name; to make it an array I need to pass as VtArray. Actually the Set is interpreting the python list of Vec2f as... hmm.\n\nActually the issue: `pv.Set(fvert_uv)` where fvert_uv is a Python list of Gf.Vec2f. USD should treat it as an array. But the error says expected 'GfVec2f' singular. That suggests the attribute's declared type is a scalar GfVec2f, not an array. When I created the primvar with TexCoord2f (scalar type name), the attribute default type might be scalar. I need to pass a VtArray explicitly, or use `Vt.Array`? \n\nIn this pxr, to set an array I should pass a list and it should infer. But it's treating single-element semantics. Actually the issue: maybe because fvert_uv is a list of Gf.Vec2f, and the attribute was created without array, the value type is GfVec2f.\n\nLet me instead create the primvar but set via a VtArray. There's no Vt.Vec2fList. I can build a VtArray via `Vt.Array`? Let me try passing a list of tuples? No.\n\nAlternative: Use `mesh.CreateFaceVertexUVsAttr`? Not available. \n\nBetter approach: create the primvar with an ARRAY type name \"texcoord2fArray\"? Not in the list (only Point3fArray, etc.). The only 2f array available is... none. TexCoord2fArray? Let me check \u2014 earlier list only had TexCoord2f and TexCoord2fArray! Yes: `['TexCoord2f', 'TexCoord2fArray']`. So I should use `Sdf.ValueTypeNames.TexCoord2fArray` for the type, then the attribute is an array and I can Set a list.\n\nLet me change type name to TexCoord2fArray and keep \"varying\". Actually for UVs the interpolation is \"varying\". Let me set type TexCoord2fArray.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b71bd773e248f64e", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.TexCoord2fArray, \"varying\")", + "oldText": "pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"st\", Sdf.ValueTypeNames.TexCoord2f, \"varying\")" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b71bd773e248f64e", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 30957, + "completion_tokens": 700, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 31657 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "42911969", + "pi_parent_id": "a0f080d7", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rtsww6rm4OGaByTPX5XvxnJQ", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 42, + "timestamp": "2026-10-21T17:15:15.749Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "I'm at round 41, 23 rounds remain. Let me run the build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-ac6445293136473d", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-ac6445293136473d", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 80, in \n mesh.CreateFaceVertexNormalsAttr([Gf.Vec3f(n) for n in fvnormals])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Mesh' object has no attribute 'CreateFaceVertexNormalsAttr'. Did you mean: 'CreateFaceVertexCountsAttr'?\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 31686, + "completion_tokens": 61, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 31747 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "5d059dd2", + "pi_parent_id": "21c49a44", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RCdcn3EZvEK4TampaSclYLj5", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 43, + "timestamp": "2026-10-21T17:15:24.851Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "Normals are a primvar too. Let me create it as `normals` with `Point3fArray`.", + "reasoning_content": "Normals are also a primvar (`normals`), not a schema attribute. Let me create it as a primvar `normals` with Point3fArray, interpolation \"faceVtx\". Since I have per-face duplicated vertices, faceVtx normals = per-vertex flat normals. Let me create the primvar.\n\nActually since vertices are duplicated per face, \"varying\" or \"faceVtx\" both work. Let me use \"faceVtx\".\n\nLet me replace the CreateFaceVertexNormalsAttr line with a primvar creation.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9d6c9897bee14a60", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "nrm_pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"normals\", Sdf.ValueTypeNames.Point3fArray, \"faceVtx\")\nnrm_pv.Set([Gf.Vec3f(n) for n in fvnormals])", + "oldText": "mesh.CreateFaceVertexNormalsAttr([Gf.Vec3f(n) for n in fvnormals])" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9d6c9897bee14a60", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 31860, + "completion_tokens": 274, + "cached_tokens": 30576, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 32134 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "abdcd73b", + "pi_parent_id": "6983c237", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RwmIz00XjkpFAADiOOACTFpX", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 44, + "timestamp": "2026-10-21T17:15:28.274Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "21 model rounds remain. Let me run the build now.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a0f0dc7843c35ce4", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a0f0dc7843c35ce4", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 80, in \n nrm_pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"normals\", Sdf.ValueTypeNames.Point3fArray, \"faceVtx\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\npxr.Tf.ErrorException: \n\tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdGeomPrimvar::SetInterpolation' at line 155 in file /opt/USD/pxr/usd/usdGeom/primvar.cpp : 'Attempt to set invalid primvar interpolation \"faceVtx\" for attribute /TrayBox/Shell.primvars:normals'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 32163, + "completion_tokens": 54, + "cached_tokens": 31360, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 32217 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "243972d9", + "pi_parent_id": "5e9166d5", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RqtjrvWhdV1NzrkkgHoweOqO", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 45, + "timestamp": "2026-10-21T17:15:37.026Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "\"faceVtx\" not accepted; since vertices are duplicated per face, \"vertex\" gives the same flat normals.", + "reasoning_content": "\"faceVtx\" interpolation name \u2014 maybe it's \"faceVertex\". Let me check valid interpolation tokens. USD interpolation tokens: constant, uniform, vertex, faceVtx (valid in full USD). This build might use \"faceVertex\"? Let me just use \"vertex\" since vertices are duplicated per face (each vertex belongs to one face), so vertex interpolation = per-vertex = flat normals. That works.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a04daf9f4993ecc8", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "nrm_pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"normals\", Sdf.ValueTypeNames.Point3fArray, \"vertex\")", + "oldText": "nrm_pv = UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"normals\", Sdf.ValueTypeNames.Point3fArray, \"faceVtx\")" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a04daf9f4993ecc8", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 32425, + "completion_tokens": 238, + "cached_tokens": 31360, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 32663 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "c7726774", + "pi_parent_id": "89e5bdca", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RckOO1XBwJrmYnZ70WErK3aT", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 46, + "timestamp": "2026-10-21T17:15:47.901Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "I'm at round 45 with 19 rounds left. Let me run the build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9bfec45bb73a4841", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9bfec45bb73a4841", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 83, in \n mesh.SetDisplayColor(Gf.Vec3f(0.87, 0.79, 0.62))\n ^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Mesh' object has no attribute 'SetDisplayColor'. Did you mean: 'GetDisplayColorAttr'?\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 32692, + "completion_tokens": 61, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 32753 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "62abdbe4", + "pi_parent_id": "4f0c60a3", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-ROEXc74rBqMhCqb834MjAgTP", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 47, + "timestamp": "2026-10-21T17:15:55.455Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "I'll drop the display-color line (the material handles appearance).", + "reasoning_content": "`SetDisplayColor` isn't available. Let me just remove that line \u2014 display color isn't required and I have a material. Let me remove it.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-bd369460afcc8560", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "mesh.CreateSubdivisionSchemeAttr(\"none\")\n", + "oldText": "mesh.CreateSubdivisionSchemeAttr(\"none\")\nmesh.SetDisplayColor(Gf.Vec3f(0.87, 0.79, 0.62))\n" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-bd369460afcc8560", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 32866, + "completion_tokens": 155, + "cached_tokens": 32144, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 33021 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "76f5b51a", + "pi_parent_id": "27e5e4a8", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RzwauE9mF3m3lS2oFDf5YaXq", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 48, + "timestamp": "2026-10-21T17:16:00.578Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me run the build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b8daf1db60ff4212", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b8daf1db60ff4212", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 85, in \n rba = UsdPhysics.RigidBodyAPI.Apply(mesh, \"RigidBodyAPI\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nBoost.Python.ArgumentError: Python argument types in\n RigidBodyAPI.Apply(Mesh, str)\ndid not match C++ signature:\n Apply(pxrInternal_v0_26_8__pxrReserved__::UsdPrim prim)\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 33050, + "completion_tokens": 47, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 33097 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "7401663e", + "pi_parent_id": "4716f277", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R3v600rni04oO81Qg5K2djxA", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 49, + "timestamp": "2026-10-21T17:16:15.081Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "`Apply` takes only the prim. Let me fix the three API apply calls.", + "reasoning_content": "`Apply` only takes a prim. Let me use `RigidBodyAPI.Apply(mesh.GetPrim())`. Same for MassAPI and CollisionAPI. Let me fix all three Apply calls.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b04717514e3dedd4", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\ns=open('build_asset.py').read()\ns=s.replace('UsdPhysics.RigidBodyAPI.Apply(mesh, \"RigidBodyAPI\")','UsdPhysics.RigidBodyAPI.Apply(mesh.GetPrim())')\ns=s.replace('UsdPhysics.MassAPI.Apply(mesh, \"MassAPI\")','UsdPhysics.MassAPI.Apply(mesh.GetPrim())')\ns=s.replace('UsdPhysics.CollisionAPI.Apply(mesh, \"CollisionAPI\")','UsdPhysics.CollisionAPI.Apply(mesh.GetPrim())')\nopen('build_asset.py','w').write(s)\nprint('ok')\nEOF\ncd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b04717514e3dedd4", + "content": "ok\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 87, in \n rba.CreateCollisionEnabledAttr(True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'RigidBodyAPI' object has no attribute 'CreateCollisionEnabledAttr'. Did you mean: 'CreateRigidBodyEnabledAttr'?\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 33234, + "completion_tokens": 231, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 33465 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "f29eac43", + "pi_parent_id": "1cfe4d96", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RNFq5srLq7gxgx4CxBUWH7QS", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 50, + "timestamp": "2026-10-21T17:16:24.559Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "`collisionEnabled` belongs on CollisionAPI, not RigidBodyAPI. Let me remove it.", + "reasoning_content": "I'm running low on model rounds (15 remain). Let me fix the RigidBodyAPI attributes. The correct attributes:\n- `kinematicEnabled`\n- `disableGravity`\n- `velocity`, `angularVelocity`\n\nThere's no `collisionEnabled` on RigidBodyAPI \u2014 collision is on CollisionAPI. Let me remove `rba.CreateCollisionEnabledAttr(True)`.\n\nLet me fix that line.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-99a6d8b582373099", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "rba.CreateKinematicEnabledAttr(True)\nrba.CreateDisableGravityAttr(True)", + "oldText": "rba.CreateKinematicEnabledAttr(True)\nrba.CreateCollisionEnabledAttr(True)\nrba.CreateDisableGravityAttr(True)" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-99a6d8b582373099", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 33566, + "completion_tokens": 204, + "cached_tokens": 32928, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 33770 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "dc405aec", + "pi_parent_id": "6fc677ef", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rj8JM3RU2Jd3TkVQxRzpQztd", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 51, + "timestamp": "2026-10-21T17:16:29.868Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "14 rounds remain. Let me run the build and if it works, validate. Let me run.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b3dc3f142cd450b5", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b3dc3f142cd450b5", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 87, in \n rba.CreateDisableGravityAttr(True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'RigidBodyAPI' object has no attribute 'CreateDisableGravityAttr'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 33799, + "completion_tokens": 62, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 33861 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "15c15b91", + "pi_parent_id": "9f209d9c", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rjru3IF5tf0cGgxI9Tsmaf0S", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 52, + "timestamp": "2026-10-21T17:16:34.898Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "Let me check RigidBodyAPI's actual attributes.", + "reasoning_content": "I'm running low on rounds, so I need to streamline the RigidBodyAPI implementation by keeping only the essential kinematic attributes and removing any optional ones to avoid unnecessary errors.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-87fc51c3c73d75d7", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"\nfrom pxr import UsdPhysics\nprint([m for m in dir(UsdPhysics.RigidBodyAPI) if 'Create' in m or 'Get' in m and 'Attr' in m])\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-87fc51c3c73d75d7", + "content": "['CreateAngularVelocityAttr', 'CreateKinematicEnabledAttr', 'CreateRigidBodyEnabledAttr', 'CreateSimulationOwnerRel', 'CreateStartsAsleepAttr', 'CreateVelocityAttr', 'GetAngularVelocityAttr', 'GetKinematicEnabledAttr', 'GetRigidBodyEnabledAttr', 'GetSchemaAttributeNames', 'GetStartsAsleepAttr', 'GetVelocityAttr']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 33949, + "completion_tokens": 123, + "cached_tokens": 32144, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 34072 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "dbc6c259", + "pi_parent_id": "001422ad", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R6eFfx3JxrDJwOIorzVjdydd", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 53, + "timestamp": "2026-10-21T17:16:49.293Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Rounds: 52 of 64. 12 remain. Let me fix: remove CreateDisableGravityAttr (not available). Keep kinematicEnabled=True. Then run and validate.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-94e0dc0ba649715d", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "rba.CreateKinematicEnabledAttr(True)", + "oldText": "rba.CreateKinematicEnabledAttr(True)\nrba.CreateDisableGravityAttr(True)" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-94e0dc0ba649715d", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 34165, + "completion_tokens": 122, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 34287 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "6c8d32ac", + "pi_parent_id": "5372475f", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rg4z7VXlSpAjEQgUIAvPsGxB", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 54, + "timestamp": "2026-10-21T17:16:54.287Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "11 rounds remain. Let me run the build. If it succeeds, I'll validate once and inspect the report.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b5a6d896ee0121c3", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b5a6d896ee0121c3", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 91, in \n col.CreateCollisionAPIAttr(\"convexDecomposition\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'CollisionAPI' object has no attribute 'CreateCollisionAPIAttr'. Did you mean: 'CreateCollisionEnabledAttr'?\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 34316, + "completion_tokens": 65, + "cached_tokens": 32928, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 34381 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "ceffa7b3", + "pi_parent_id": "96e15d6b", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RN066i19UX2I0k4iwrUKYRx1", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 55, + "timestamp": "2026-10-21T17:17:04.877Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me check CollisionAPI attributes.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-ab5d167a87b15fe5", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"\nfrom pxr import UsdPhysics\nprint([m for m in dir(UsdPhysics.CollisionAPI) if 'Create' in m or 'Get' in m])\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-ab5d167a87b15fe5", + "content": "['CreateCollisionEnabledAttr', 'CreateSimulationOwnerRel', 'Get', 'GetCollisionEnabledAttr', 'GetPath', 'GetPrim', 'GetSchemaAttributeNames', 'GetSchemaClassPrimDefinition', 'GetSchemaKind', 'GetSimulationOwnerRel', '_GetStaticTfType']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 34481, + "completion_tokens": 78, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 34559 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "b96c0c24", + "pi_parent_id": "32b408d9", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RPVBlLhjhxVPFML0ed3MQ9SN", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 56, + "timestamp": "2026-10-21T17:17:19.034Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "This pxr build's CollisionAPI is minimal. I'll keep just `collisionEnabled` on the mesh (the mesh itself is the collision shape). Let me fix and run.", + "reasoning_content": "This is round 55 of 64. Only 9 model rounds remain. I need to be efficient. The CollisionAPI in this pxr build only has CreateCollisionEnabledAttr. The approximation/convex decomposition attributes are on the mesh as `physics:collisionAPI` or via `UsdPhysics.MeshCollisionAPI`. Actually in USD, `PhysicsCollisionAPI` approximation is a relationship/attribute on the collision prim. But this build's CollisionAPI is minimal.\n\nGiven time constraints, let me just remove the collisionAPI/convex decomposition attributes and keep only collisionEnabled=True. The mesh itself with CollisionAPI applied acts as the collision shape. Actually, applying CollisionAPI to a Mesh makes the mesh a collidable. The approximation type can be set but the attr name differs. Let me check \u2014 the attribute might be \"collisionAPI\" accessed via GetAttribute directly, or it's on MeshCollisionAPI.\n\nActually, in newer USD, the collision approximation is a primvar `physics:approximation` or via `UsdPhysics.MeshCollisionAPI`. Let me just keep it simple: CollisionAPI with collisionEnabled=True on the mesh. That should satisfy RB.COL requirements for \"has collision\". Let me remove the approximation lines.\n\nLet me fix and run, then validate immediately.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8af58c9cfd243eca", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "col = UsdPhysics.CollisionAPI.Apply(mesh.GetPrim())\ncol.CreateCollisionEnabledAttr(True)\n# approximation via mesh collision API if available\ntry:\n mcol = UsdPhysics.MeshCollisionAPI.Apply(mesh.GetPrim())\n mcol.CreateCollisionAPIAttr(\"convexDecomposition\")\n mcol.CreateConvexDecompositionMaxConvexHullsAttr(24)\nexcept Exception as e:\n print(\"mesh collision approx skip:\", e)", + "oldText": "col = UsdPhysics.CollisionAPI.Apply(mesh.GetPrim())\ncol.CreateCollisionEnabledAttr(True)\ncol.CreateCollisionAPIAttr(\"convexDecomposition\")\ncol.CreateConvexDecompositionMaxConvexHullsAttr(24)\ncol.CreateConvexDecompositionMaxVerticesPerHullAttr(64)" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8af58c9cfd243eca", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 34633, + "completion_tokens": 532, + "cached_tokens": 33712, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 35165 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "ec149997", + "pi_parent_id": "b2d466b4", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R49DFyhmYRgMxHk0uUSx9w16", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 57, + "timestamp": "2026-10-21T17:17:22.528Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me run the build now.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9b2508160a9ab230", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9b2508160a9ab230", + "content": "mesh collision approx skip: 'MeshCollisionAPI' object has no attribute 'CreateCollisionAPIAttr'\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 102, in \n texprim = UsdShade.TextureFile.Define(stage, \"/TrayBox/TrayMaterial/TexBaseColor\")\n ^^^^^^^^^^^^^^^^^^^^\nAttributeError: module 'pxr.UsdShade' has no attribute 'TextureFile'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 35194, + "completion_tokens": 48, + "cached_tokens": 28224, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 35242 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "0545de9a", + "pi_parent_id": "6a26d796", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R5CJXQdxzWiCMD6KggzT77v7", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 58, + "timestamp": "2026-10-21T17:17:37.116Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "The mesh collision API applied but the approximation attr isn't available. Let me check MeshCollisionAPI attributes and also handle the missing TextureFile. I have 7 rounds left. Let me be strategic.\n\n`UsdShade.TextureFile` \u2014 in this build it might be under `UsdShade.Texture` or I need to use `UsdShade.Shader` with id \"UsdUVTexture\". Let me check.\n\nGiven the tight budget, let me simplify: skip textures (remove the texture file nodes) and just use constant base color. That reduces the chance of failures. But VM.TEX requirements might want textures. However visual fidelity would be lower. Given only 7 rounds, let me first get a VALID asset that builds, then validate, then decide.\n\nLet me quickly check what's available for textures and MeshCollisionAPI.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-ab6b884d4ceffd25", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"\nfrom pxr import UsdShade, UsdPhysics\nprint('TextureFile?', hasattr(UsdShade,'TextureFile'), 'Texture?', hasattr(UsdShade,'Texture'))\nprint([m for m in dir(UsdPhysics.MeshCollisionAPI) if 'Create' in m or 'Approx' in m.lower()])\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-ab6b884d4ceffd25", + "content": "TextureFile? False Texture? False\n['CreateApproximationAttr']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 35371, + "completion_tokens": 281, + "cached_tokens": 34496, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 35652 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "a5bc1e72", + "pi_parent_id": "8a30f4ac", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R2UrNlQwIKqoIFRkudLlhL5b", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 59, + "timestamp": "2026-10-21T17:18:47.928Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "6 rounds left \u2014 I'll fix the texture nodes and collision approximation, then build and validate.", + "reasoning_content": "Only 6 model rounds remain. I need to be very efficient. Let me use `UsdShade.Shader` with id \"UsdUVTexture\" for textures (or skip textures). And `MeshCollisionAPI.CreateApproximationAttr(\"convexDecomposition\")`.\n\nGiven the tight budget, let me:\n1. Fix the collision approximation to use CreateApproximationAttr.\n2. Replace TextureFile with a UsdShade.Shader of id \"UsdUVTexture\" (source \"ArTex:Texture\", but simpler: id \"UsdUVTexture\"). Actually let me just use the \"UsdUVTexture\" shader.\n\nLet me rewrite the material/texture section cleanly and finalize. Let me do it all in one edit to save rounds.\n\nFor texture via Shader with id \"UsdUVTexture\":\n```\ntex = UsdShade.Shader.Define(stage, \".../TexBaseColor\")\ntex.CreateIdAttr(\"UsdUVTexture\")\ntex.CreateInputAttr(\"file:string\", \"/output/textures/wood_basecolor.png\")\ntex.CreateInputAttr(\"format\", 0) # Raw\n```\nThen connect tex \"rgb\" -> surf \"baseColor\".\n\nFor roughness, use same with \"r\" output.\n\nLet me rewrite from the \"Material\" section to the end of the material part, and keep the post-processing export. Let me just view the current material section and rewrite it wholesale.\n\nActually, let me be safe and SIMPLE given budget: keep the material but use UsdUVTexture shader nodes. Let me do the edit.\n\nLet me replace:\n- the texprim and roughprim TextureFile blocks with Shader-based USDTex\n- the mesh collision approx to CreateApproximationAttr\n\nLet me do two edits.\n\nEdit 1: collision\nOld:\n```\ntry:\n mcol = UsdPhysics.MeshCollisionAPI.Apply(mesh.GetPrim())\n mcol.CreateCollisionAPIAttr(\"convexDecomposition\")\n mcol.CreateConvexDecompositionMaxConvexHullsAttr(24)\nexcept Exception as e:\n print(\"mesh collision approx skip:\", e)\n```\nNew:\n```\ntry:\n mcol = UsdPhysics.MeshCollisionAPI.Apply(mesh.GetPrim())\n mcol.CreateApproximationAttr(\"convexDecomposition\")\nexcept Exception as e:\n print(\"approx skip:\", e)\n```\n\nEdit 2: texture section. Current:\n```\ntexprim = UsdShade.TextureFile.Define(stage, \"/TrayBox/TrayMaterial/TexBaseColor\")\ntexprim.CreateFileAttr(\"/output/textures/wood_basecolor.png\")\ntexprim.CreateFormatAttr(\"png\")\ntexprim.CreateTextureCoordinateAttribute(\"st\")\ntexprim.CreateWrapSAttr(\"repeat\")\ntexprim.CreateWrapTAttr(\"repeat\")\n\nroughprim = UsdShade.TextureFile.Define(stage, \"/TrayBox/TrayMaterial/TexRoughness\")\nroughprim.CreateFileAttr(\"/output/textures/wood_roughness.png\")\nroughprim.CreateFormatAttr(\"png\")\nroughprim.CreateTextureCoordinateAttribute(\"st\")\nroughprim.CreateWrapSAttr(\"repeat\")\nroughprim.CreateWrapTAttr(\"repeat\")\n```\nNew (UsdUVTexture):\n```\ntexprim = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/TexBaseColor\")\ntexprim.CreateIdAttr(\"UsdUVTexture\")\ntexprim.CreateInputAttr(\"file:string\", \"/output/textures/wood_basecolor.png\")\ntexprim.CreateInputAttr(\"st\", \"st\")\n\nroughprim = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/TexRoughness\")\nroughprim.CreateIdAttr(\"UsdUVTexture\")\nroughprim.CreateInputAttr(\"file:string\", \"/output/textures/wood_roughness.png\")\nroughprim.CreateInputAttr(\"st\", \"st\")\n```\n\nThen the connect lines: \n```\nUsdShade.ConnectSourceAttrToDestAttr(texprim.GetPrim(), \"rgb\", surf.GetPrim(), \"baseColor\")\nUsdShade.ConnectSourceAttrToDestAttr(roughprim.GetPrim(), \"r\", surf.GetPrim(), \"roughness\")\n```\nThese should still work with UsdUVTexture outputs \"rgb\" and \"r\". Good.\n\nLet me apply both edits and run + validate in one bash call to save rounds. Actually I can't call validate from bash \u2014 it's an MCP tool. Let me build first, then call validate.\n\nLet me do the edits, then bash build, then MCP validate \u2014 I can do edits in one call, then build+ but validate is separate MCP. Let me do edits now.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b546f68de1680b58", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "try:\n mcol = UsdPhysics.MeshCollisionAPI.Apply(mesh.GetPrim())\n mcol.CreateApproximationAttr(\"convexDecomposition\")\nexcept Exception as e:\n print(\"approx skip:\", e)", + "oldText": "try:\n mcol = UsdPhysics.MeshCollisionAPI.Apply(mesh.GetPrim())\n mcol.CreateCollisionAPIAttr(\"convexDecomposition\")\n mcol.CreateConvexDecompositionMaxConvexHullsAttr(24)\nexcept Exception as e:\n print(\"mesh collision approx skip:\", e)" + }, + { + "newText": "texprim = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/TexBaseColor\")\ntexprim.CreateIdAttr(\"UsdUVTexture\")\ntexprim.CreateInputAttr(\"file:string\", \"/output/textures/wood_basecolor.png\")\ntexprim.CreateInputAttr(\"st\", \"st\")\n\nroughprim = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/TexRoughness\")\nroughprim.CreateIdAttr(\"UsdUVTexture\")\nroughprim.CreateInputAttr(\"file:string\", \"/output/textures/wood_roughness.png\")\nroughprim.CreateInputAttr(\"st\", \"st\")", + "oldText": "texprim = UsdShade.TextureFile.Define(stage, \"/TrayBox/TrayMaterial/TexBaseColor\")\ntexprim.CreateFileAttr(\"/output/textures/wood_basecolor.png\")\ntexprim.CreateFormatAttr(\"png\")\ntexprim.CreateTextureCoordinateAttribute(\"st\")\ntexprim.CreateWrapSAttr(\"repeat\")\ntexprim.CreateWrapTAttr(\"repeat\")\n\nroughprim = UsdShade.TextureFile.Define(stage, \"/TrayBox/TrayMaterial/TexRoughness\")\nroughprim.CreateFileAttr(\"/output/textures/wood_roughness.png\")\nroughprim.CreateFormatAttr(\"png\")\nroughprim.CreateTextureCoordinateAttribute(\"st\")\nroughprim.CreateWrapSAttr(\"repeat\")\nroughprim.CreateWrapTAttr(\"repeat\")" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b546f68de1680b58", + "content": "Successfully replaced 2 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 35681, + "completion_tokens": 1576, + "cached_tokens": 35280, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 37257 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "7b2cfb5c", + "pi_parent_id": "57497138", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RP6Yh9vZL9bWZMNUKIIHfWNZ", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 60, + "timestamp": "2026-10-21T17:18:58.768Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "5 model rounds remain after this. Let me build and validate in this round. Let me build first.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8f30e7b5b0246944", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8f30e7b5b0246944", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 103, in \n texprim.CreateInputAttr(\"file:string\", \"/output/textures/wood_basecolor.png\")\n ^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Shader' object has no attribute 'CreateInputAttr'. Did you mean: 'CreateIdAttr'?\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 37286, + "completion_tokens": 62, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 37348 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "9ddfcdda", + "pi_parent_id": "2c1d9de9", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RLJmPWlydKd8pqCnyp2Fsx9n", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 61, + "timestamp": "2026-10-21T17:19:18.998Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "Only 4 rounds left. Shader inputs must be created via the prim directly. Let me rewrite the whole material section.", + "reasoning_content": "Only 4 model rounds remain after this. `Shader.CreateInputAttr` isn't available \u2014 I need to use `GetAttribute` on the shader prim directly. Let me use `texprim.GetPrim().GetAttribute(\"file\")` \u2014 but the input must exist. In USD, shader inputs are discovered from the shader's definition. For a custom shader created via Define, inputs don't exist until created. I should create them via `UsdShade.Shader` \u2014 but CreateInputAttr isn't there. \n\nAlternative: create the input attributes directly on the prim: `texprim.GetPrim().CreateAttribute(\"file\", Sdf.ValueTypeNames.String).Set(...)`. Let me do that.\n\nFor the preview surface inputs (baseColor, roughness, metallic, opacity) \u2014 I used `surf.CreateInputAttr(...)` earlier and it didn't error (the error was at line 103, after surf was created at 108?). Wait, surf is created AFTER textures (line 108). The error is at line 103 which is the texprim input. So `surf.CreateInputAttr` might also fail but we haven't reached it. Actually earlier the preview surface CreateInputAttr... let me check \u2014 in the successful partial runs, did we ever reach surf creation? No, it always failed before. So CreateInputAttr likely doesn't exist on Shader at all.\n\nSo I need to create all shader inputs via GetPrim().CreateAttribute. Let me rewrite the whole material section to use GetPrim().CreateAttribute for inputs, and ConnectSourceAttrToDestAttr for wiring.\n\nLet me rewrite from \"mat = UsdShade.Material...\" to before \"stage2 = Usd.Stage.Open\" ... actually the export post-processing is at the end. Let me replace the texture + surface + connect section.\n\nLet me view current lines around material to do a precise edit.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-96c6056bde42c8fb", + "function_name": "read", + "arguments": { + "path": "/tmp/build_asset.py", + "offset": 96, + "limit": 40 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-96c6056bde42c8fb", + "content": "print(\"approx skip:\", e)\n\n# Material\nmat = UsdShade.Material.Define(stage, \"/TrayBox/TrayMaterial\")\n\ntexprim = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/TexBaseColor\")\ntexprim.CreateIdAttr(\"UsdUVTexture\")\ntexprim.CreateInputAttr(\"file:string\", \"/output/textures/wood_basecolor.png\")\ntexprim.CreateInputAttr(\"st\", \"st\")\n\nroughprim = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/TexRoughness\")\nroughprim.CreateIdAttr(\"UsdUVTexture\")\nroughprim.CreateInputAttr(\"file:string\", \"/output/textures/wood_roughness.png\")\nroughprim.CreateInputAttr(\"st\", \"st\")\n\nsurf = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/PreviewSurface\")\nsurf.CreateInfoAttr(\"source\", \"UsdPreviewSurface\")\nsurf.CreateIdAttr(\"UsdPreviewSurface\")\nsurf.CreateInputAttr(\"baseColor:color3f\", Gf.Vec3f(0.87, 0.79, 0.62))\nsurf.CreateInputAttr(\"roughness:float\", 0.6)\nsurf.CreateInputAttr(\"metallic:float\", 0.0)\nsurf.CreateInputAttr(\"opacity:float\", 1.0)\n\n# wire textures\nUsdShade.ConnectSourceAttrToDestAttr(texprim.GetPrim(), \"rgb\",\n surf.GetPrim(), \"baseColor\")\nUsdShade.ConnectSourceAttrToDestAttr(roughprim.GetPrim(), \"r\",\n surf.GetPrim(), \"roughness\")\n\n# bind material to mesh (direct/over)\nmat.BindDirect(mesh.GetPrim(), UsdShade.Tokens.over)\n\nfrom pxr import UsdUtils\nascii_path = \"/tmp/asset_ascii.usda\"\nUsdUtils.Export(stage, ascii_path)\n\nwith open(ascii_path, \"r\") as fh:\n text = fh.read()\n\n# inject upAxis / units / defaultPrim into the #usda header metadata block\n\n[23 more lines in file. Use offset=136 to continue.]", + "extra": { + "tool_name": "read", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 37453, + "completion_tokens": 478, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 37931 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "0f5b7db8", + "pi_parent_id": "dead7c02", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RRmy8kjxhKFhqRForFGnwHOx", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 62, + "timestamp": "2026-10-21T17:20:05.646Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "3 model rounds remain after this. Let me rewrite the material section to use GetPrim().CreateAttribute for inputs. For UsdUVTexture and UsdPreviewSurface, the inputs exist in the shader definition schema, so I can get them via GetPrim().GetAttribute(name). But for a Shader created via Define, the inputs may not be auto-created. Let me just create them via CreateAttribute with the right types.\n\nLet me replace the whole block.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-aaa97e6cd556970f", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "# Material\nmat = UsdShade.Material.Define(stage, \"/TrayBox/TrayMaterial\")\n\nfrom pxr import Sdf as _Sdf\ndef set_in(prim, name, val, vtype):\n a = prim.GetAttribute(name)\n if not a.IsValid():\n a = prim.CreateAttribute(name, vtype)\n a.Set(val)\n\ntexprim = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/TexBaseColor\")\ntexprim.CreateIdAttr(\"UsdUVTexture\")\ntexprim.CreateInfoAttr(\"source\", \"UsdUVTexture\")\nset_in(texprim.GetPrim(), \"file\", \"/output/textures/wood_basecolor.png\", _Sdf.ValueTypeNames.String)\nset_in(texprim.GetPrim(), \"st\", \"st\", _Sdf.ValueTypeNames.String)\n\nroughprim = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/TexRoughness\")\nroughprim.CreateIdAttr(\"UsdUVTexture\")\nroughprim.CreateInfoAttr(\"source\", \"UsdUVTexture\")\nset_in(roughprim.GetPrim(), \"file\", \"/output/textures/wood_roughness.png\", _Sdf.ValueTypeNames.String)\nset_in(roughprim.GetPrim(), \"st\", \"st\", _Sdf.ValueTypeNames.String)\n\nsurf = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/PreviewSurface\")\nsurf.CreateInfoAttr(\"source\", \"UsdPreviewSurface\")\nsurf.CreateIdAttr(\"UsdPreviewSurface\")\nset_in(surf.GetPrim(), \"baseColor\", Gf.Vec3f(0.87,0.79,0.62), _Sdf.ValueTypeNames.Color3f)\nset_in(surf.GetPrim(), \"roughness\", 0.6, _Sdf.ValueTypeNames.Float)\nset_in(surf.GetPrim(), \"metallic\", 0.0, _Sdf.ValueTypeNames.Float)\nset_in(surf.GetPrim(), \"opacity\", 1.0, _Sdf.ValueTypeNames.Float)\n\n# wire textures\nUsdShade.ConnectSourceAttrToDestAttr(texprim.GetPrim(), \"rgb\", surf.GetPrim(), \"baseColor\")\nUsdShade.ConnectSourceAttrToDestAttr(roughprim.GetPrim(), \"r\", surf.GetPrim(), \"roughness\")", + "oldText": "# Material\nmat = UsdShade.Material.Define(stage, \"/TrayBox/TrayMaterial\")\n\ntexprim = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/TexBaseColor\")\ntexprim.CreateIdAttr(\"UsdUVTexture\")\ntexprim.CreateInputAttr(\"file:string\", \"/output/textures/wood_basecolor.png\")\ntexprim.CreateInputAttr(\"st\", \"st\")\n\nroughprim = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/TexRoughness\")\nroughprim.CreateIdAttr(\"UsdUVTexture\")\nroughprim.CreateInputAttr(\"file:string\", \"/output/textures/wood_roughness.png\")\nroughprim.CreateInputAttr(\"st\", \"st\")\n\nsurf = UsdShade.Shader.Define(stage, \"/TrayBox/TrayMaterial/PreviewSurface\")\nsurf.CreateInfoAttr(\"source\", \"UsdPreviewSurface\")\nsurf.CreateIdAttr(\"UsdPreviewSurface\")\nsurf.CreateInputAttr(\"baseColor:color3f\", Gf.Vec3f(0.87, 0.79, 0.62))\nsurf.CreateInputAttr(\"roughness:float\", 0.6)\nsurf.CreateInputAttr(\"metallic:float\", 0.0)\nsurf.CreateInputAttr(\"opacity:float\", 1.0)\n\n# wire textures\nUsdShade.ConnectSourceAttrToDestAttr(texprim.GetPrim(), \"rgb\",\n surf.GetPrim(), \"baseColor\")\nUsdShade.ConnectSourceAttrToDestAttr(roughprim.GetPrim(), \"r\",\n surf.GetPrim(), \"roughness\")" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-aaa97e6cd556970f", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 38436, + "completion_tokens": 1085, + "cached_tokens": 32928, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 39521 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "120c6755", + "pi_parent_id": "936ef5fc", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rh4p3Dyf7bGRVg3qZv1w0AUH", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 63, + "timestamp": "2026-10-21T17:20:14.481Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "2 model rounds remain after this. Let me build and validate in this round. Let me build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a9043c1ed0f7642d", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py 2>&1 && echo \"=== BUILT ===\" && ls -la /output /output/textures" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a9043c1ed0f7642d", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 110, in \n texprim.CreateInfoAttr(\"source\", \"UsdUVTexture\")\n ^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Shader' object has no attribute 'CreateInfoAttr'. Did you mean: 'CreateIdAttr'?\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 39553, + "completion_tokens": 79, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 39632 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "e6f22264", + "pi_parent_id": "bad05af0", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RnjrLMuPT0TemLyjDRBEHMOu", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 64, + "timestamp": "2026-10-21T17:20:47.339Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "This is my final stretch. Let me patch out the unavailable `CreateInfoAttr` and build in one command.", + "reasoning_content": "1 model round remains after this. I need to finalize. `CreateInfoAttr` isn't available \u2014 use GetPrim().CreateAttribute(\"info:id\"...). Actually `CreateIdAttr` works. The `info:source` \u2014 let me create it via prim.CreateAttribute. Let me fix by removing CreateInfoAttr calls and using set_in-style for \"source\". Actually \"source\" is an info attribute; `texprim.GetPrim().CreateAttribute(\"info:source\", _Sdf.ValueTypeNames.String).Set(\"UsdUVTexture\")`. But wait, for UsdUVTexture, the id \"UsdUVTexture\" should be enough; source isn't required. Let me just remove the CreateInfoAttr(\"source\",...) lines and keep CreateIdAttr.\n\nBut this is the last model round after. I need to build successfully AND validate in the same remaining capacity. Actually \"1 model round remains after this round\" \u2014 so THIS round I can make tool calls, and then one final round. Let me fix and build this round, and validate in the final round. Actually I should build now (this round) and then in the final round call validate_candidate. But if build fails again I won't have budget.\n\nLet me fix all CreateInfoAttr usages. There are: texprim, roughprim (I removed their source lines? No \u2014 I added `texprim.CreateInfoAttr(\"source\", \"UsdUVTexture\")` for both, and surf has `surf.CreateInfoAttr(\"source\",\"UsdPreviewSurface\")`). Let me replace all CreateInfoAttr with prim.CreateAttribute approach via a helper, and run build in the same bash. Then final round = validate.\n\nLet me do it in one edit + bash. Actually I can do the edit and bash in the same round (multiple tool calls). Let me do the edit and the build together \u2014 but the build depends on the edit, so they can't be parallel; however they're in the same \"round\"? The tool-calling: I can issue edit then bash sequentially within my response? The instructions say make independent calls in same block, dependent ones must wait. Edit then bash is dependent. But I can still do edit in this turn, and bash... I need them sequential. \n\nLet me just do the edit now (this round includes multiple tool calls as I've been doing). Actually each of my assistant turns can contain multiple tool calls executed sequentially? No \u2014 parallel calls in one block run together; sequential dependent calls need separate turns. But I've been doing one tool call per turn mostly.\n\nGiven \"1 model round remains after this round,\" I should do the edit AND build in this round via two sequential... no. Let me just make this turn contain the edit, and the NEXT turn build+validate. That's 2 more turns but I only have 1. \n\nHmm. Let me be aggressive: put everything in this turn. I'll do the edit first. Then I realize I can't also run bash in the same turn reliably (they'd be parallel). \n\nActually, let me reconsider: I can write a corrected build via a bash heredoc that patches and runs \u2014 combining edit and run in a single bash command. Let me do a bash that fixes the file with sed/python and runs it. That's ONE tool call, this turn. Then final round = validate.\n\nLet me do a bash that: patches out the three CreateInfoAttr lines (replace with prim create attribute), then runs build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a60ea73bab5ee1f8", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\ns=open('build_asset.py').read()\n# Replace CreateInfoAttr(\"source\", X) with a prim-based info attribute\ns=s.replace('texprim.CreateInfoAttr(\"source\", \"UsdUVTexture\")',\n 'set_in(texprim.GetPrim(), \"info:source\", \"UsdUVTexture\", _Sdf.ValueTypeNames.String)')\ns=s.replace('roughprim.CreateInfoAttr(\"source\", \"UsdUVTexture\")',\n 'set_in(roughprim.GetPrim(), \"info:source\", \"UsdUVTexture\", _Sdf.ValueTypeNames.String)')\ns=s.replace('surf.CreateInfoAttr(\"source\", \"UsdPreviewSurface\")',\n 'set_in(surf.GetPrim(), \"info:source\", \"UsdPreviewSurface\", _Sdf.ValueTypeNames.String)')\nopen('build_asset.py','w').write(s)\nprint(\"patched\")\nEOF\ncd /tmp && python3 build_asset.py 2>&1 && echo \"=== BUILT OK ===\" && ls -la /output /output/textures" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a60ea73bab5ee1f8", + "content": "patched\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 129, in \n UsdShade.ConnectSourceAttrToDestAttr(texprim.GetPrim(), \"rgb\", surf.GetPrim(), \"baseColor\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: module 'pxr.UsdShade' has no attribute 'ConnectSourceAttrToDestAttr'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 39731, + "completion_tokens": 1014, + "cached_tokens": 39200, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 40745 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "8a1738a9", + "pi_parent_id": "7c50aec2", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RLeH8AnBWup0uNPxu8hCMU5S", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 65, + "timestamp": "2026-10-21T17:21:52.845Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "This is my final round. I'll drop the (unavailable) texture wiring \u2014 the material keeps a valid constant wood base color \u2014 build the asset, verify it, then submit.", + "reasoning_content": "This is my final model round. I must save the asset and submit. The build failed at ConnectSourceAttrToDestAttr \u2014 not available. I need to:\n1. Fix the connection (or remove it \u2014 but then no texture wiring; the material would just use constant baseColor which is fine and simpler).\n2. Build the asset.\n3. Submit.\n\nI have only this one round, and it's a single bash + mcp submit. I need to do build (bash) then submit (mcp). But submit should come after build succeeds. In one round I can do multiple tool calls. But they're dependent (submit after build). However, I can chain in a single bash for the build, and then call submit in the same turn? Tool calls in a turn \u2014 if I put bash and mcp submit in the same block, they run in parallel (bad). If sequential, I can't within one turn per the rules... Actually I can issue multiple tool calls in one assistant message; they may run in parallel. That's risky for submit before build.\n\nGiven it's the final round, safest: do ONE bash that fixes the connect (just remove the connect lines, keep constant baseColor so material still valid), builds the asset, AND I cannot call mcp submit from bash. Submit is an MCP tool.\n\nSo I need: bash (build) THEN mcp submit. To guarantee order, I should do them sequentially. But in one model round with 0 remaining after \u2014 I can make multiple tool calls in this single turn. Let me make the build robust first.\n\nActually, the cleanest: write a final bash that:\n- edits build_asset.py to remove the two ConnectSourceAttrToDestAttr lines (so material uses constant baseColor \u2014 still valid, texture not wired but that's acceptable; or wire via a different API).\n- runs build_asset.py\n- confirms asset exists.\n\nThen in the SAME turn, call benchmark_submit_asset. The concern: order. Since submit just freezes whatever is at /output/asset.usd, and if build and submit run in parallel, submit might read an incomplete file. \n\nBetter: I'll do the bash build this turn, and since it's the final round with 0 remaining, I must also submit this turn. Let me combine into a single bash that does build, then invokes submit via... I can't call MCP from bash.\n\nAlternative: Let me just make the bash build the asset correctly, and separately issue the submit MCP call. To ensure ordering within a single assistant turn, I'll issue them as separate tool calls. The harness may run them sequentially in listed order. I'll list bash first, then mcp submit. If the harness runs sequentially, great. If parallel, risky. \n\nGiven the constraints, I'll do: first a bash to build (fixing the connect), and then the mcp submit \u2014 as two calls in this turn, bash first. Most harnesses execute tool calls in order. Let me go.\n\nActually, the safest given \"0 model rounds remain\" is that ALL my actions must be in THIS single assistant turn. Let me issue:\n1. bash: patch (remove connect lines OR use available connect API) + build + verify\n2. mcp submit_asset\n\nLet me first check: is there an alternative connect function? `UsdShade.ConnectSourceAttrToDestAttr` missing. There might be `UsdShade.ConnectSourceAttributeToDestAttribute`? Let me not risk it \u2014 I'll just drop the wiring and rely on the constant baseColor (0.87,0.79,0.62) which is a good wood color. The material will still be valid and bound. Textures present in folder but unwired \u2014 that's fine for a valid asset; visual fidelity uses the baseColor. Actually to improve visual, better to wire. But reliability > texture. Let me drop wiring.\n\nActually, let me try to wire using the lower-level API that's more likely present: `UsdShade.ConnectAPI`? Not sure. There's `Sdf`/`Usd` connection via `Usd.CreateAttribute` ... The reliable primitive: \n```\nUsdShade.ConnectSourceAttrToDestAttr # missing\n```\nAlternative reliable approach: set the baseColor input to reference the texture via... no.\n\nLet me just drop the connects. The material with constant baseColor is valid. Good enough for a final submit.\n\nLet me write the final bash: patch to comment out the two connect lines, run build, verify the .usd exists and is non-trivial. Then submit.\n\nLet me also make sure the export post-processing works (the ascii round-trip). Earlier it never got past line 129, so the export code (lines ~132+) was never tested. Risk: the ascii header injection regex or UsdUtils.Export could fail. If it fails, asset.usd might be from a previous partial? No \u2014 CreateNew was replaced with CreateInMemory, so no file written until UsdUtils.Export. If export fails, /output/asset.usd won't exist.\n\nGiven it's my final round, let me make the bash defensive: after build, if /output/asset.usd missing, create a minimal valid one as fallback. But that's complex. Let me just run and hope the export works (UsdUtils.Export is standard).\n\nLet me also double check: does `UsdUtils.Export(stage, ascii_path)` to .usda work? Yes. And re-open and export to .usd works.\n\nLet me finalize. I'll:\n- patch connect lines out\n- run build\n- ls verify\n- then submit via mcp in the same turn (second call).\n\nLet me put build in bash, and the submit as a separate mcp call. I'll issue both in this turn, bash first.\n\nWait \u2014 if I issue two tool calls in one turn and they run in parallel, submit could race. To be safe, let me ONLY do the bash build this... but then I can't submit and I have 0 rounds left. That's fatal.\n\nI'll risk issuing bash then mcp submit as sequential calls in one turn. Many pi harnesses run tool calls sequentially in the order given. Let me do it.\n\nLet me write the final build patch + run.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-bb499443173ee839", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\ns=open('build_asset.py').read()\n# Remove the two ConnectSourceAttrToDestAttr lines (API unavailable in this build);\n# material keeps constant baseColor so it remains valid and bound.\nimport re\ns=re.sub(r'UsdShade\\.ConnectSourceAttrToDestAttr\\(texprim\\.GetPrim\\(\\), \"rgb\", surf\\.GetPrim\\(\\), \"baseColor\"\\)\\n','',s)\ns=re.sub(r'UsdShade\\.ConnectSourceAttrToDestAttr\\(roughprim\\.GetPrim\\(\\), \"r\",\\n\\s*surf\\.GetPrim\\(\\), \"roughness\"\\)\\n','',s)\nopen('build_asset.py','w').write(s)\nprint(\"connect lines removed\")\nEOF\ncd /tmp && python3 build_asset.py 2>&1\necho \"=== RESULT ===\"\nls -la /output /output/textures 2>&1\necho \"=== usd size ===\"\nwc -c /output/asset.usd 2>&1\necho \"=== ascii meta check ===\"\nhead -c 400 /tmp/asset_ascii.usda 2>&1" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-bb499443173ee839", + "content": "connect lines removed\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 129, in \n UsdShade.ConnectSourceAttrToDestAttr(roughprim.GetPrim(), \"r\", surf.GetPrim(), \"roughness\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: module 'pxr.UsdShade' has no attribute 'ConnectSourceAttrToDestAttr'\n=== RESULT ===\n/output:\ntotal 4\ndrwxrwxrwx 3 actor actor 80 Oct 21 17:08 .\ndrwxr-xr-x 1 root root 56 Oct 21 17:08 ..\n-rw-r--r-- 1 actor actor 492 Oct 21 17:08 asset.usd\ndrwxr-xr-x 2 actor actor 80 Oct 21 17:08 textures\n\n/output/textures:\ntotal 2060\ndrwxr-xr-x 2 actor actor 80 Oct 21 17:08 .\ndrwxrwxrwx 3 actor actor 80 Oct 21 17:08 ..\n-rw-r--r-- 1 actor actor 1458048 Oct 21 17:08 wood_basecolor.png\n-rw-r--r-- 1 actor actor 647995 Oct 21 17:08 wood_roughness.png\n=== usd size ===\n492 /output/asset.usd\n=== ascii meta check ===\nhead: cannot open '/tmp/asset_ascii.usda' for reading: No such file or directory\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 40891, + "completion_tokens": 1659, + "cached_tokens": 39200, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 42550 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "2da608ff", + "pi_parent_id": "9e6307ae", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R5djeGenvGaFG7KH6odFAELK", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + } + ], + "final_metrics": { + "total_prompt_tokens": 1756688, + "total_completion_tokens": 29299, + "total_cached_tokens": 646016, + "total_steps": 65, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 1785987 + } + } +} \ No newline at end of file diff --git a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/evidence/box_cardboard_open_a01/reconstruction-1/nvidia-validator.json b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/evidence/box_cardboard_open_a01/reconstruction-1/nvidia-validator.json new file mode 100644 index 0000000000000000000000000000000000000000..7679d38531aa312367051cef8f109bf1c4c39f34 --- /dev/null +++ b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/evidence/box_cardboard_open_a01/reconstruction-1/nvidia-validator.json @@ -0,0 +1,3710 @@ +{ + "asset_path": "/output/asset.usd", + "asset_sha256": "402eff31f89f535216709a98ae2464d016eb5d0791da26bd149413695f567f5f", + "candidate_invalid_reason": "empty_composed_stage", + "engine_dispatch_completed": true, + "features_summary": { + "FET_000_MUJOCO": { + "blocking_failure": false, + "dependencies": "[{'FET_000_STANDARD': {'version': '0.1.0'}}]", + "optional requirements": "['com.nvidia.simready.NP.006', 'com.nvidia.simready.RV.007', 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'com.nvidia.simready.UN.007', 'com.nvidia.simready.VG.MESH.001', 'com.nvidia.usd.HI.001', 'com.nvidia.usd.HI.004']", + "passed": false, + "requirement_messages": "{\"com.nvidia.simready.UN.006\": [\"Stage has upAxis of 'Y', not 'Z'.\"], \"com.nvidia.simready.UN.007\": [\"Stage has metersPerUnit of 0.01, not 1.0.\"], \"com.nvidia.simready.VG.MESH.001\": [\"Stage does not contain any meshes.\"], \"com.nvidia.usd.HI.001\": [\"Prim hierarchy must have at least one root prim. 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feature 'FET_003_MUJOCO' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_003_MUJOCO-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_003_NEWTON' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_003_NEWTON-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_003_PHYSX' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_003_PHYSX-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_003_PHYSX' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_003_PHYSX-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 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"message": "Registering feature 'FET_004_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_004_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_004_STANDARD' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_004_STANDARD-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_005_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_005_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_006_MDL' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_006_MDL-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + 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"message": "Registering feature 'FET_007_STANDARD' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_007_STANDARD-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_010_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_010_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_011_RTX' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_011_RTX-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_011_STANDARD' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_011_STANDARD-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_021_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_021_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_021_ISAAC' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_021_ISAAC-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_021_ISAAC' version '0.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_021_ISAAC-0.3.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_022_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": 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feature 'FET_022_PHYSX' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_PHYSX-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_022_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_022_STANDARD' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_STANDARD-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_023_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_023_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_024_MUJOCO' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_024_MUJOCO-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_024_NEWTON' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_024_NEWTON-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_024_PHYSX' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_024_PHYSX-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_024_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_024_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_025_ROS' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_025_ROS-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_028_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_028_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_028_MUJOCO' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_028_MUJOCO-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_028_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_028_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_030_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_030_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_031_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_031_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_032_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_032_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_033_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_033_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_033_STANDARD' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_033_STANDARD-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_033_STANDARD' version '0.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_033_STANDARD-0.3.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_033_STANDARD' version '0.4.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_033_STANDARD-0.4.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_040_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_040_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_041_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_041_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_042_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_042_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_043_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_043_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_044_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_044_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_045_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_045_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_046_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_046_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_100_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_100_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_100_ISAAC' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_100_ISAAC-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_100_ISAAC' version '0.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_100_ISAAC-0.3.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_100_ISAAC' version '0.4.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_100_ISAAC-0.4.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_101_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_101_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-COCO' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-Cityscapes' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-ADE20K' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-PascalVOC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-SUNRGBD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-ImageNet1K' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package' version '1.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package-NoBOM' version '1.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package-Candidate' version '1.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package-Candidate' version '1.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package-Candidate' version '1.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package-Candidate' version '1.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Body' version '2.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_body.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Body' version '2.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_body.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Body' version '2.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_body.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Body' version '2.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_body.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Body' version '3.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_body.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Gripper' version '2.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_gripper.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Gripper' version '2.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_gripper.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Gripper' version '2.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_gripper.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Gripper' version '3.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_gripper.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robotics-Prop' version '3.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robotics_prop.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robotics-Prop' version '3.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robotics_prop.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robotics-Prop' version '3.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robotics_prop.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robotics-Prop' version '3.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robotics_prop.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robotics-Prop' version '4.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robotics_prop.toml" + }, + { + "level": "WARNING", + "logger": "root", + "message": "DeprecationWarning: Use find() instead" + }, + { + "level": "WARNING", + "logger": "root", + "message": "File /opt/simready/venv/lib/python3.12/site-packages/simready/validate/api.py, line 485, in _build_features_validation_summary" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.ISA.001'] did not pass for feature FET_100_ISAAC." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.NP.006', 'com.nvidia.simready.RV.004', 'com.nvidia.simready.SR.001'] did not pass for feature FET_000_NEWTON." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.NP.006', 'com.nvidia.simready.SR.001'] did not pass for feature FET_000_STANDARD." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.MUJOCO.COL.001'] did not pass for feature FET_004_MUJOCO." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.MUJOCO.COL.001'] did not pass for feature FET_003_MUJOCO." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Failed requirements ['com.nvidia.simready.SR.002', 'com.nvidia.simready.SR.004'] preclude feature FET_033_STANDARD." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.NP.006', 'com.nvidia.simready.RV.001', 'com.nvidia.simready.SR.001'] did not pass for feature FET_000_PHYSX." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.NEWTON.COL.001'] did not pass for feature FET_004_NEWTON." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.NEWTON.COL.001'] did not pass for feature FET_003_NEWTON." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Failed requirements ['com.nvidia.simready.UN.006', 'com.nvidia.simready.UN.007', 'com.nvidia.simready.VG.MESH.001', 'com.nvidia.usd.HI.001', 'com.nvidia.usd.HI.004'] preclude feature FET_001_STANDARD." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.NP.006', 'com.nvidia.simready.RV.007', 'com.nvidia.simready.SR.001'] did not pass for feature FET_000_MUJOCO." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Clearing requirements registry" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Clearing features registry" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Clearing profiles registry" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Clearing rules registry" + } + ], + "outcome": "invalid_submission", + "passed": false, + "profile_id": "Robotics-Prop", + "profile_version": "4.0.0", + "rule_execution": { + "buckets": [ + { + "bucket": "neutral", + "declared_requirement_codes": [ + "com.nvidia.simready.DISP.001", + "com.nvidia.simready.DISP.002", + "com.nvidia.simready.DISP.003", + "com.nvidia.simready.GSP.001", + "com.nvidia.simready.HI.010", + "com.nvidia.simready.ISA.001", + "com.nvidia.simready.JT.001", + "com.nvidia.simready.JT.ART.003", + "com.nvidia.simready.NP.002", + "com.nvidia.simready.NP.003", + "com.nvidia.simready.NP.004", + "com.nvidia.simready.NP.005", + "com.nvidia.simready.NP.006", + "com.nvidia.simready.NP.007", + "com.nvidia.simready.NP.008", + "com.nvidia.simready.NVM.001", + "com.nvidia.simready.NVM.002", + "com.nvidia.simready.NVM.003", + "com.nvidia.simready.NVM.004", + "com.nvidia.simready.NVM.005", + "com.nvidia.simready.NVM.006", + "com.nvidia.simready.PMT.001", + "com.nvidia.simready.RB.001", + "com.nvidia.simready.RB.007", + "com.nvidia.simready.RB.010", + "com.nvidia.simready.RB.011", + "com.nvidia.simready.RB.012", + "com.nvidia.simready.RB.COL.001", + "com.nvidia.simready.RB.COL.002", + "com.nvidia.simready.RB.COL.003", + "com.nvidia.simready.RB.MB.001", + "com.nvidia.simready.RV.010", + "com.nvidia.simready.RV.011", + "com.nvidia.simready.SL.001", + "com.nvidia.simready.SL.002", + "com.nvidia.simready.SL.003", + "com.nvidia.simready.SL.MAT.001", + "com.nvidia.simready.SL.QCODE.001", + "com.nvidia.simready.SL.TIME.001", + "com.nvidia.simready.SR.001", + "com.nvidia.simready.SR.002", + "com.nvidia.simready.SR.004", + "com.nvidia.simready.UN.006", + "com.nvidia.simready.UN.007", + "com.nvidia.simready.VG.008", + "com.nvidia.simready.VG.025", + "com.nvidia.simready.VG.MESH.001", + "com.nvidia.simready.VM.BIND.002", + "com.nvidia.simready.VM.MAT.001", + "com.nvidia.simready.VM.MDL.001", + 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"150c9ae2b37243f8b87b20128cf779e61e0ff99c3b5daacdef709604fc6cee52", + "usd_validation_nvidia.rules._atomic_asset": "03e990beb9ebced42b91f15eaead85c4b3afa9ee982287a715d692c8bfab6fb9", + "usd_validation_nvidia.rules._basic": "70016f8467dc2d2a2ab566f8e259eb0080c58f009aab5d6d95d2aaabe65ed988", + "usd_validation_nvidia.rules._geometry": "50c0ad5afa724488c77f692c258931a005d97b8927c46798874bcf3cf5a3e0b9", + "usd_validation_nvidia.rules._hierarchy": "2aefdff685d077d3bb2211a80935ee2da32a58811be24b864e011609507ba0b8", + "usd_validation_nvidia.rules._layout": "7c6dc30848900f27a222052696a90acedf75a01c5edd500ff56b95db4763ae26", + "usd_validation_nvidia.rules._materials": "5282c326d392685addc53324bdbc2a50130f0e7790d667f82b573114b9eff629", + "usd_validation_nvidia.rules._physics": "204349b532ddf5837933b007c56f7e34c8482e32cb1be3e1053d8d5c7440fc93" + } + }, + "soft_issues": [ + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "message": "Asset 'asset' has no metadata. Metadata must be stored in custom layer data as 'SimReady_Metadata' or a sidecar JSON file.", + "prim_path": null, + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.core.naming_paths.validation.MetadataLocationChecker", + "rule_id": "com.nvidia.simready.NP.006", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "message": "Required metadata fields are missing: SimReady_Metadata, asset_name, asset_type, source_file, usd_date_generated. Fields must be authored directly in the root layer's customLayerData.", + "prim_path": null, + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.core.sim_ready.validation.SimReadyCapabilityChecker", + "rule_id": "com.nvidia.simready.SR.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "message": "Stage has no default prim. Unable to validate.", + "prim_path": null, + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.isaac_sim.composition.validation.IsaacCompositionCapabilityChecker", + "rule_id": "com.nvidia.simready.ISA.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "message": "Stage has no valid default prim to host the 'MuJoCo' physics runtime variant set.", + "prim_path": null, + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.core.runtime_variants.validation.MuJoCoVariantSetChecker", + "rule_id": "com.nvidia.simready.RV.007", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "message": "Stage has no default prim. Unable to validate.", + "prim_path": null, + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.physics_bodies.physics_rigid_bodies.validation.MuJoCoColliderAPIChecker", + "rule_id": "com.nvidia.simready.MUJOCO.COL.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "message": "Stage has no default prim. Unable to validate.", + "prim_path": null, + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.physics_bodies.physics_rigid_bodies.validation.NewtonColliderAPIChecker", + "rule_id": "com.nvidia.simready.NEWTON.COL.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "message": "Stage has no valid default prim to host the 'Newton' physics runtime variant set.", + "prim_path": null, + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.core.runtime_variants.validation.NewtonVariantSetChecker", + "rule_id": "com.nvidia.simready.RV.004", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "message": "Stage has no valid default prim to host the 'PhysX' physics runtime variant set.", + "prim_path": null, + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.core.runtime_variants.validation.PhysXVariantSetChecker", + "rule_id": "com.nvidia.simready.RV.001", + "severity": "failure", + "suggestions": [] + } + ], + "warnings": [] +} diff --git a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/report.html b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/report.html index dd54a3663aa2862560cb51273913378e3608efd1..a55cc39c86a60702c8225cd572b49f49291657e4 100644 --- a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/report.html +++ b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/report.html @@ -1 +1 @@ -SimReadyBench results

SimReadyBench results

Formal fidelity measures SimReady Robotics-Prop requirements. Visual fidelity combines shape and appearance materials. Kinematic fidelity measures joint recovery and motion correspondence. Each score is reported independently.

6 of 100 reconstruction attempts completed.

Scores CSV | Detailed results JSON

Results by asset

AssetModelHarnessSkillsRepetitionStatusFormal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
cleaning_cloth_a01Qwen/Qwen3.8-27B:deepinfrapinone1completed0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/14336)64001030.06987.451421893233701445263Not measuredNot measuredNot measured
container_bottle_glass_a01Qwen/Qwen3.8-27B:deepinfrapinone1completed80.54% (39/46)0% (0/2)N/A66.67% (34/51)0% (0/1)0% (0/1)N/A0% (0/16384)6410945.33906.411487863219111509774Not measuredNot measuredNot measured
food_produce_banana_a01Qwen/Qwen3.8-27B:deepinfrapinone1completed82.61% (40/46)0% (0/2)N/A78.43% (40/51)0% (0/1)0% (0/1)N/A0% (0/20480)6400738.21698.781152527169651169492Not measuredNot measuredNot measured
food_produce_broccoli_b01Qwen/Qwen3.8-27B:deepinfrapinone1completed0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/20480)64001367.491323.31550016347451584761Not measuredNot measuredNot measured
kitchenware_pan_j01Qwen/Qwen3.8-27B:deepinfrapinone1completed0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/20480)64301112.591073.511725299247671750066Not measuredNot measuredNot measured
pallet_wood_recycled_a05Qwen/Qwen3.8-27B:deepinfrapinone1completed0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/14336)57301044.861013.721206189228031228992Not measuredNot measuredNot measured

cleaning_cloth_a01 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/14336)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
64001030.06987.451421893233701445263Not measuredNot measuredNot measured

0 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/14336143361433658.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
0020484.5Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/cleaningCloth_01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count1/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

container_bottle_glass_a01 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
80.54% (39/46)0% (0/2)N/A66.67% (34/51)0% (0/1)0% (0/1)N/A0% (0/16384)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
6410945.33906.411487863219111509774Not measuredNot measuredNot measured

39 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/16384163841638466.67

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
0020484.51Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/bottle_01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count1/11/1
joint_inventory_count1/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:metallic0/20480
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

food_produce_banana_a01 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
82.61% (40/46)0% (0/2)N/A78.43% (40/51)0% (0/1)0% (0/1)N/A0% (0/20480)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
6400738.21698.781152527169651169492Not measuredNot measuredNot measured

40 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/20480204802048083.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
0020483.02Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_food_produce_banana_a01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count1/11/1
joint_inventory_count1/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:diffuseColor0/20480
visual:metallic0/20480
visual:opacity0/20480
visual:roughness0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

food_produce_broccoli_b01 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/20480)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
64001367.491323.31550016347451584761Not measuredNot measuredNot measured

0 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/20480204802048083.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
0020483.68Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_food_produce_broccoli_b01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count1/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:diffuseColor0/20480
visual:metallic0/20480
visual:opacity0/20480
visual:roughness0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

kitchenware_pan_j01 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/20480)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
64301112.591073.511725299247671750066Not measuredNot measuredNot measured

0 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/20480204802048083.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
0020485.27Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_kitchenware_pan_j01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:diffuseColor0/20480
visual:metallic0/20480
visual:opacity0/20480
visual:roughness0/20480

Generated USD | Asset package manifest | Harbor trajectory 1

pallet_wood_recycled_a05 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/14336)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
57301044.861013.721206189228031228992Not measuredNot measuredNot measured

0 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/14336143361433658.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
00204816.03Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_pallet_wood_recycled_a05_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:opacity0/20480

Generated USD | Asset package manifest | Harbor trajectory 1

\ No newline at end of file +SimReadyBench results

SimReadyBench results

Formal fidelity measures SimReady Robotics-Prop requirements. Visual fidelity combines shape and appearance materials. Kinematic fidelity measures joint recovery and motion correspondence. Each score is reported independently.

7 of 100 reconstruction attempts completed.

Scores CSV | Detailed results JSON

Results by asset

AssetModelHarnessSkillsRepetitionStatusFormal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
box_cardboard_open_a01Qwen/Qwen3.8-27B:deepinfrapinone1completed0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/14336)64001330.31279.971756688292991785987Not measuredNot measuredNot measured
cleaning_cloth_a01Qwen/Qwen3.8-27B:deepinfrapinone1completed0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/14336)64001030.06987.451421893233701445263Not measuredNot measuredNot measured
container_bottle_glass_a01Qwen/Qwen3.8-27B:deepinfrapinone1completed80.54% (39/46)0% (0/2)N/A66.67% (34/51)0% (0/1)0% (0/1)N/A0% (0/16384)6410945.33906.411487863219111509774Not measuredNot measuredNot measured
food_produce_banana_a01Qwen/Qwen3.8-27B:deepinfrapinone1completed82.61% (40/46)0% (0/2)N/A78.43% (40/51)0% (0/1)0% (0/1)N/A0% (0/20480)6400738.21698.781152527169651169492Not measuredNot measuredNot measured
food_produce_broccoli_b01Qwen/Qwen3.8-27B:deepinfrapinone1completed0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/20480)64001367.491323.31550016347451584761Not measuredNot measuredNot measured
kitchenware_pan_j01Qwen/Qwen3.8-27B:deepinfrapinone1completed0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/20480)64301112.591073.511725299247671750066Not measuredNot measuredNot measured
pallet_wood_recycled_a05Qwen/Qwen3.8-27B:deepinfrapinone1completed0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/14336)57301044.861013.721206189228031228992Not measuredNot measuredNot measured

box_cardboard_open_a01 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/14336)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
64001330.31279.971756688292991785987Not measuredNot measuredNot measured

0 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/14336143361433658.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
0020489.7Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_box_a01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count1/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

cleaning_cloth_a01 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/14336)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
64001030.06987.451421893233701445263Not measuredNot measuredNot measured

0 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/14336143361433658.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
0020484.5Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/cleaningCloth_01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count1/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

container_bottle_glass_a01 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
80.54% (39/46)0% (0/2)N/A66.67% (34/51)0% (0/1)0% (0/1)N/A0% (0/16384)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
6410945.33906.411487863219111509774Not measuredNot measuredNot measured

39 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/16384163841638466.67

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
0020484.51Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/bottle_01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count1/11/1
joint_inventory_count1/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:metallic0/20480
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

food_produce_banana_a01 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
82.61% (40/46)0% (0/2)N/A78.43% (40/51)0% (0/1)0% (0/1)N/A0% (0/20480)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
6400738.21698.781152527169651169492Not measuredNot measuredNot measured

40 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/20480204802048083.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
0020483.02Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_food_produce_banana_a01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count1/11/1
joint_inventory_count1/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:diffuseColor0/20480
visual:metallic0/20480
visual:opacity0/20480
visual:roughness0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

food_produce_broccoli_b01 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/20480)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
64001367.491323.31550016347451584761Not measuredNot measuredNot measured

0 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/20480204802048083.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
0020483.68Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_food_produce_broccoli_b01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count1/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:diffuseColor0/20480
visual:metallic0/20480
visual:opacity0/20480
visual:roughness0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

kitchenware_pan_j01 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/20480)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
64301112.591073.511725299247671750066Not measuredNot measuredNot measured

0 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/20480204802048083.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
0020485.27Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_kitchenware_pan_j01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:diffuseColor0/20480
visual:metallic0/20480
visual:opacity0/20480
visual:roughness0/20480

Generated USD | Asset package manifest | Harbor trajectory 1

pallet_wood_recycled_a05 / Qwen/Qwen3.8-27B:deepinfra / repetition 1

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
0% (0/46)0% (0/2)N/ANot measured (0/51), 51 unmeasured, bounds 0%-100%0% (0/1)0% (0/1)N/A0% (0/14336)

Requests, timing and costs

Model requestsValidation callsRequest errorsElapsed time (s)Model request time (s)Input tokensOutput tokensTotal tokensInference cost (USD)Compute cost (USD)Total cost (USD)
57301044.861013.721206189228031228992Not measuredNot measuredNot measured

0 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/111100
JointsNot applicable0/0000
Materials00/14336143361433658.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
00204816.03Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_pallet_wood_recycled_a05_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled0/11/1
body_kinematic0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment0/20480
visual:opacity0/20480

Generated USD | Asset package manifest | Harbor trajectory 1

\ No newline at end of file diff --git a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/results.json b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/results.json index 256a74fef4c2b8375e92a766ee0ca93e391856c0..d2a5c81027aa99338fc801f79e1829f9bf9ff8dc 100644 --- a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/results.json +++ b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/results.json @@ -1,7 +1,7 @@ { "schema": "simready-bench-portable-report-v1", "planned_conditions": 100, - "completed_conditions": 6, + "completed_conditions": 7, "partial": true, "settings": { "reference_assets": [ @@ -96,6 +96,1895 @@ } }, "results": [ + { + "condition_id": "condition-dbfb93effb3e99ff93025d1a717781f58bafe420cd122a0cb156cabe2cf40793", + "asset_id": "box_cardboard_open_a01", + "requested_model": "Qwen/Qwen3.8-27B:deepinfra", + "harness_id": "pi", + "skill_set_id": "none", + "repetition": 0, + "status": "completed", + "formal_fidelity_percent": 0.0, + "visual_fidelity_percent": 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"visual_appearance_upper_bound_percent": 0.0, + "kinematic_reference_joint_count": 0, + "kinematic_candidate_joint_count": 0, + "kinematic_matched_joint_count": 0, + "kinematic_missing_joint_count": 0, + "kinematic_extra_joint_count": 0, + "kinematic_moving_joints_percent": null, + "kinematic_moving_joints_status": "not_applicable", + "kinematic_moving_joints_earned": 0, + "kinematic_moving_joints_eligible": 0, + "kinematic_moving_joints_measured": 0, + "kinematic_moving_joints_coverage": 0, + "kinematic_moving_joints_lower_bound_percent": null, + "kinematic_moving_joints_upper_bound_percent": null, + "kinematic_fixed_constraints_percent": null, + "kinematic_fixed_constraints_status": "not_applicable", + "kinematic_fixed_constraints_earned": 0, + "kinematic_fixed_constraints_eligible": 0, + "kinematic_fixed_constraints_measured": 0, + "kinematic_fixed_constraints_coverage": 0, + "kinematic_fixed_constraints_lower_bound_percent": null, + "kinematic_fixed_constraints_upper_bound_percent": null, + "fidelity_shape_earned": 0.0, + "fidelity_shape_eligible": 1, + "fidelity_shape_measured": 1, + "fidelity_shape_status": "measured", + "fidelity_rigid_parts_earned": 0.0, + "fidelity_rigid_parts_eligible": 1, + "fidelity_rigid_parts_measured": 1, + "fidelity_rigid_parts_status": "measured", + "fidelity_joints_earned": 0, + "fidelity_joints_eligible": 0, + "fidelity_joints_measured": 0, + "fidelity_joints_status": "not_applicable", + "fidelity_materials_earned": 0, + "fidelity_materials_eligible": 14336, + "fidelity_materials_measured": 14336, + "fidelity_materials_status": "measured", + "fidelity_shape_precision": 0.0, + "fidelity_shape_recall": 0.0, + "fidelity_shape_samples_each_direction": 2048, + "fidelity_rigid_parts_geometry_percent": 0.0, + "fidelity_rigid_parts_structure_percent": 25.0, + "fidelity_rigid_parts_structure_earned": 1, + "fidelity_rigid_parts_structure_eligible": 4, + "fidelity_joints_reference_count": 0, + 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"reasoning_effort": "medium" + }, + "authoring_protocol": "authoring-64-bounded-output-8192-v1" + }, { "condition_id": "condition-71970d198e6bea6b8e3301a1e50956b487dee3b7f5cea89e44cee1c8d36b82e6", "asset_id": "cleaning_cloth_a01", diff --git a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/scores.csv b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/scores.csv index 260e195adf1dc7d3e214dc31ae6ec3720d5f191a..a2c8f13f37e871f296f27ecfdd81bf15a2634722 100644 --- a/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/scores.csv +++ b/runs/nv25-qwen38-27b-medium-pi-none-turn64-20261021/scores.csv @@ -1,4 +1,5 @@ condition_id,asset_id,requested_model,harness_id,skill_set_id,repetition,status,formal_fidelity_percent,visual_fidelity_percent,kinematic_fidelity_percent,simready_reward,mandatory_passed,mandatory_total,mandatory_failed,mandatory_unknown,mandatory_lower_bound_percent,mandatory_upper_bound_percent,completion_bonus,optional_score_percent,optional_passed,optional_failed,optional_unknown,optional_total,optional_measured,optional_coverage,optional_lower_bound_percent,optional_upper_bound_percent,fidelity_shape_percent,fidelity_rigid_parts_percent,fidelity_joints_percent,fidelity_materials_percent,fidelity_shape_coverage,fidelity_rigid_parts_coverage,fidelity_joints_coverage,fidelity_materials_coverage,fidelity_reference_geometry_status,formal_fidelity_status,formal_fidelity_earned,formal_fidelity_eligible,formal_fidelity_measured,formal_fidelity_coverage,formal_fidelity_lower_bound_percent,formal_fidelity_upper_bound_percent,visual_fidelity_status,visual_fidelity_earned,visual_fidelity_eligible,visual_fidelity_measured,visual_fidelity_coverage,visual_fidelity_lower_bound_percent,visual_fidelity_upper_bound_percent,kinematic_fidelity_status,kinematic_fidelity_earned,kinematic_fidelity_eligible,kinematic_fidelity_measured,kinematic_fidelity_coverage,kinematic_fidelity_lower_bound_percent,kinematic_fidelity_upper_bound_percent,visual_appearance_percent,visual_appearance_status,visual_appearance_earned,visual_appearance_eligible,visual_appearance_measured,visual_appearance_coverage,visual_appearance_lower_bound_percent,visual_appearance_upper_bound_percent,kinematic_reference_joint_count,kinematic_candidate_joint_count,kinematic_matched_joint_count,kinematic_missing_joint_count,kinematic_extra_joint_count,kinematic_moving_joints_percent,kinematic_moving_joints_status,kinematic_moving_joints_earned,kinematic_moving_joints_eligible,kinematic_moving_joints_measured,kinematic_moving_joints_coverage,kinematic_moving_joints_lower_bound_percent,kinematic_moving_joints_upper_bound_percent,kinematic_fixed_constraints_percent,kinematic_fixed_constraints_status,kinematic_fixed_constraints_earned,kinematic_fixed_constraints_eligible,kinematic_fixed_constraints_measured,kinematic_fixed_constraints_coverage,kinematic_fixed_constraints_lower_bound_percent,kinematic_fixed_constraints_upper_bound_percent,fidelity_shape_earned,fidelity_shape_eligible,fidelity_shape_measured,fidelity_shape_status,fidelity_rigid_parts_earned,fidelity_rigid_parts_eligible,fidelity_rigid_parts_measured,fidelity_rigid_parts_status,fidelity_joints_earned,fidelity_joints_eligible,fidelity_joints_measured,fidelity_joints_status,fidelity_materials_earned,fidelity_materials_eligible,fidelity_materials_measured,fidelity_materials_status,fidelity_shape_precision,fidelity_shape_recall,fidelity_shape_samples_each_direction,fidelity_rigid_parts_geometry_percent,fidelity_rigid_parts_structure_percent,fidelity_rigid_parts_structure_earned,fidelity_rigid_parts_structure_eligible,fidelity_joints_reference_count,fidelity_joints_candidate_count,inference_calls_used,validation_calls_used,model_request_errors,elapsed_seconds,model_request_seconds,input_tokens,output_tokens,total_tokens,inference_cost_usd,compute_cost_usd,total_cost_usd +condition-dbfb93effb3e99ff93025d1a717781f58bafe420cd122a0cb156cabe2cf40793,box_cardboard_open_a01,Qwen/Qwen3.8-27B:deepinfra,pi,none,1,completed,0.0,0.0,,0.0,0,46,46,0,0.0,0.0,0,,0,0,51,51,0,0.0,0.0,100.0,0.0,0.0,,0.0,1.0,1.0,0,0.5833333333333334,complete,measured,0,46,46,1.0,0.0,0.0,measured,0.0,2,2,1.0,0.0,0.0,not_applicable,0,0,0,0,,,0.0,measured,0.0,2048,2048,1.0,0.0,0.0,0,0,0,0,0,,not_applicable,0,0,0,0,,,,not_applicable,0,0,0,0,,,0.0,1,1,measured,0.0,1,1,measured,0,0,0,not_applicable,0,14336,14336,measured,0.0,0.0,2048,0.0,25.0,1,4,0,0,64,0,0,1330.2977931164205,1279.9719720110297,1756688,29299,1785987,,, condition-71970d198e6bea6b8e3301a1e50956b487dee3b7f5cea89e44cee1c8d36b82e6,cleaning_cloth_a01,Qwen/Qwen3.8-27B:deepinfra,pi,none,1,completed,0.0,0.0,,0.0,0,46,46,0,0.0,0.0,0,,0,0,51,51,0,0.0,0.0,100.0,0.0,0.0,,0.0,1.0,1.0,0,0.5833333333333334,complete,measured,0,46,46,1.0,0.0,0.0,measured,0.0,2,2,1.0,0.0,0.0,not_applicable,0,0,0,0,,,0.0,measured,0.0,2048,2048,1.0,0.0,0.0,0,0,0,0,0,,not_applicable,0,0,0,0,,,,not_applicable,0,0,0,0,,,0.0,1,1,measured,0.0,1,1,measured,0,0,0,not_applicable,0,14336,14336,measured,0.0,0.0,2048,0.0,25.0,1,4,0,0,64,0,0,1030.064731374383,987.4483679123223,1421893,23370,1445263,,, condition-f0237999d0f3869061985cad42d0fc7d8dca5cde2bc122b7ad38f2a943ce35f1,container_bottle_glass_a01,Qwen/Qwen3.8-27B:deepinfra,pi,none,1,completed,80.54347826086956,0.0,,80.54347826086956,39,46,7,0,80.54347826086956,80.54347826086956,0,66.66666666666667,34,17,0,51,51,1.0,66.66666666666667,66.66666666666667,0.0,0.0,,0.0,1.0,1.0,0,0.6666666666666666,complete,measured,39,46,46,1.0,80.54347826086956,80.54347826086956,measured,0.0,2,2,1.0,0.0,0.0,not_applicable,0,0,0,0,,,0.0,measured,0.0,2048,2048,1.0,0.0,0.0,0,0,0,0,0,,not_applicable,0,0,0,0,,,,not_applicable,0,0,0,0,,,0.0,1,1,measured,0.0,1,1,measured,0,0,0,not_applicable,0,16384,16384,measured,0.0,0.0,2048,0.0,50.0,2,4,0,0,64,1,0,945.3274005204439,906.4118125811219,1487863,21911,1509774,,, condition-d35579adccdd9be0db995225fe9fe1d1fdf955b58d5847ceff846b8084cd9083,food_produce_banana_a01,Qwen/Qwen3.8-27B:deepinfra,pi,none,1,completed,82.6086956521739,0.0,,82.6086956521739,40,46,6,0,82.6086956521739,82.6086956521739,0,78.43137254901961,40,11,0,51,51,1.0,78.43137254901961,78.43137254901961,0.0,0.0,,0.0,1.0,1.0,0,0.8333333333333334,complete,measured,40,46,46,1.0,82.6086956521739,82.6086956521739,measured,0.0,2,2,1.0,0.0,0.0,not_applicable,0,0,0,0,,,0.0,measured,0.0,2048,2048,1.0,0.0,0.0,0,0,0,0,0,,not_applicable,0,0,0,0,,,,not_applicable,0,0,0,0,,,0.0,1,1,measured,0.0,1,1,measured,0,0,0,not_applicable,0,20480,20480,measured,0.0,0.0,2048,0.0,50.0,2,4,0,0,64,0,0,738.2069523409009,698.7784188082442,1152527,16965,1169492,,, diff --git a/viewer-index.json b/viewer-index.json index 4b048b82a648e4a3f47eb343b8d2ae85237dafee..5f44721de239748dbab5d53b649535a9a4946405 100644 --- a/viewer-index.json +++ b/viewer-index.json @@ -186,6 +186,10 @@ } ], "by_asset": [ + { + "split": 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