diff --git a/README.md b/README.md index a0d36e1f759f0f5e8871c2c53830775451273125..6ef8cd3ff584efb4f42bc7aa4cc6e77e1c3cfd20 100644 --- a/README.md +++ b/README.md @@ -2078,6 +2078,7 @@ 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-xhigh-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-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/asset.usd b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/asset.usd new file mode 100644 index 0000000000000000000000000000000000000000..e6a1eb88cf4bbfb9ef481edfb4b40f8f18f0dc8c --- /dev/null +++ b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/asset.usd @@ -0,0 +1,65 @@ +#usda 1.0 (upAxis = Z) (metersPerUnit = 1.0) +( + defaultPrim = "Tray" + doc = """Generated from Composed Stage of root layer /output/asset.usd +""" +) + +def Xform "Tray" ( + apiSchemas = ["PhysicsRigidBodyAPI", "PhysicsMassAPI"] +) +{ + float physics:mass = 9.2 + + def Mesh "TrayMesh" ( + apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + int[] faceVertexIndices = [0, 1, 2, 0, 2, 3, 4, 5, 6, 4, 6, 7, 8, 9, 10, 8, 10, 11, 12, 13, 14, 12, 14, 15, 16, 17, 18, 16, 18, 19, 20, 21, 22, 20, 22, 23, 24, 25, 26, 24, 26, 27, 28, 29, 30, 28, 30, 31, 32, 33, 34, 32, 34, 35, 36, 37, 38, 36, 38, 39, 40, 41, 42, 40, 42, 43, 44, 45, 46, 44, 46, 47, 48, 49, 50, 48, 50, 51, 52, 53, 54, 52, 54, 55] + custom rel material:binding = + normal3f[] normals = [(0, 0, -1), (0, 0, -1), (0, 0, -1), (0, 0, -1), (0, -1, 0), (0, -1, 0), (0, -1, 0), (0, -1, 0), (0, 1, -0), (0, 1, -0), (0, 1, -0), (0, 1, -0), (-1, -0, -0), (-1, -0, -0), (-1, -0, -0), (-1, -0, -0), (1, -0, -0), (1, -0, -0), (1, -0, -0), (1, -0, -0), (0, 0, 1), (0, 0, 1), (0, 0, 1), (0, 0, 1), (-0, -0, 1), (-0, -0, 1), (-0, -0, 1), (-0, -0, 1), (0, 0, 1), (0, 0, 1), (0, 0, 1), (0, 0, 1), (-0, -0, 1), (-0, -0, 1), (-0, -0, 1), (-0, -0, 1), (0, -1, 0), (0, -1, 0), (0, -1, 0), (0, -1, 0), (0, 1, -0), (0, 1, -0), (0, 1, -0), (0, 1, -0), (-1, 0, 0), (-1, 0, 0), (-1, 0, 0), (-1, 0, 0), (1, 0, 0), (1, 0, 0), (1, 0, 0), (1, 0, 0), (-0, -0, 1), (-0, -0, 1), (-0, -0, 1), (-0, -0, 1)] + uniform token physics:approximation = "convexHull" + bool physics:collisionEnabled = 1 + point3f[] points = [(-0.31788975, 0.25592458, -0.1511896), (0.31788975, 0.25592458, -0.1511896), (0.31788975, -0.25592458, -0.1511896), (-0.31788975, -0.25592458, -0.1511896), (-0.31788975, -0.25592458, -0.1511896), (0.31788975, -0.25592458, -0.1511896), (0.31788975, -0.25592458, 0.1511896), (-0.31788975, -0.25592458, 0.1511896), (0.31788975, 0.25592458, -0.1511896), (-0.31788975, 0.25592458, -0.1511896), (-0.31788975, 0.25592458, 0.1511896), (0.31788975, 0.25592458, 0.1511896), (-0.31788975, -0.25592458, -0.1511896), (-0.31788975, -0.25592458, 0.1511896), (-0.31788975, 0.25592458, 0.1511896), (-0.31788975, 0.25592458, -0.1511896), (0.31788975, 0.25592458, -0.1511896), (0.31788975, 0.25592458, 0.1511896), (0.31788975, -0.25592458, 0.1511896), (0.31788975, -0.25592458, -0.1511896), (-0.31788975, -0.25592458, 0.1511896), (0.31788975, -0.25592458, 0.1511896), (0.31788975, -0.24092458, 0.1511896), (-0.31788975, -0.24092458, 0.1511896), (-0.31788975, 0.25592458, 0.1511896), (-0.31788975, 0.24092458, 0.1511896), (0.31788975, 0.24092458, 0.1511896), (0.31788975, 0.25592458, 0.1511896), (-0.31788975, -0.24092458, 0.1511896), (-0.30288976, -0.24092458, 0.1511896), (-0.30288976, 0.24092458, 0.1511896), (-0.31788975, 0.24092458, 0.1511896), (0.30288976, 0.24092458, 0.1511896), (0.30288976, -0.24092458, 0.1511896), (0.31788975, -0.24092458, 0.1511896), (0.31788975, 0.24092458, 0.1511896), (-0.30288976, -0.24092458, -0.1361896), (0.30288976, -0.24092458, -0.1361896), (0.30288976, -0.24092458, 0.1511896), (-0.30288976, -0.24092458, 0.1511896), (0.30288976, 0.24092458, -0.1361896), (-0.30288976, 0.24092458, -0.1361896), (-0.30288976, 0.24092458, 0.1511896), (0.30288976, 0.24092458, 0.1511896), (-0.30288976, 0.24092458, -0.1361896), (-0.30288976, -0.24092458, -0.1361896), (-0.30288976, -0.24092458, 0.1511896), (-0.30288976, 0.24092458, 0.1511896), (0.30288976, -0.24092458, -0.1361896), (0.30288976, 0.24092458, -0.1361896), (0.30288976, 0.24092458, 0.1511896), (0.30288976, -0.24092458, 0.1511896), (-0.30288976, 0.24092458, -0.1361896), (-0.30288976, -0.24092458, -0.1361896), (0.30288976, -0.24092458, -0.1361896), (0.30288976, 0.24092458, -0.1361896)] + point float2[] primvars:uv = [ (0.5000000, 0.0000000), (0.9500000, 0.0000000), (0.9500000, 1.0000000), (0.5000000, 1.0000000), (0.0000000, 0.0000000), (0.5000000, 0.0000000), (0.5000000, 1.0000000), (0.0000000, 1.0000000), (0.0000000, 0.0000000), (0.5000000, 0.0000000), (0.5000000, 1.0000000), (0.0000000, 1.0000000), (0.0000000, 0.0000000), (0.0000000, 1.0000000), (0.5000000, 1.0000000), (0.5000000, 0.0000000), (0.0000000, 0.0000000), (0.0000000, 1.0000000), (0.5000000, 1.0000000), (0.5000000, 0.0000000), (0.9500000, 0.0000000), (0.9500000, 1.0000000), (1.0000000, 1.0000000), (1.0000000, 0.0000000), (0.9500000, 0.0000000), (1.0000000, 0.0000000), (1.0000000, 1.0000000), (0.9500000, 1.0000000), (0.9500000, 0.0000000), (0.9500000, 1.0000000), (1.0000000, 1.0000000), (1.0000000, 0.0000000), (0.9500000, 0.0000000), (1.0000000, 0.0000000), (1.0000000, 1.0000000), (0.9500000, 1.0000000), (0.5000000, 0.0000000), (0.9500000, 0.0000000), (0.9500000, 1.0000000), (0.5000000, 1.0000000), (0.5000000, 0.0000000), (0.9500000, 0.0000000), (0.9500000, 1.0000000), (0.5000000, 1.0000000), (0.5000000, 0.0000000), (0.9500000, 0.0000000), (0.9500000, 1.0000000), (0.5000000, 1.0000000), (0.5000000, 0.0000000), (0.9500000, 0.0000000), (0.9500000, 1.0000000), (0.5000000, 1.0000000), (0.5000000, 0.0000000), (0.5000000, 1.0000000), (0.9500000, 1.0000000), (0.9500000, 0.0000000) ] ( + interpolation = "faceVtx" + ) + } + + def Material "TrayMaterial" + { + token outputs:surface.connect = + + def Shader "TexColor" + { + uniform token info:id = "UsdUVTexture" + asset inputs:file = @/output/textures/tray_color.png@ + color3f outputs:rgb + } + + def Shader "TexRough" + { + uniform token info:id = "UsdUVTexture" + asset inputs:file = @/output/textures/tray_rough.png@ + float outputs:r + } + + def Shader "TexNormal" + { + uniform token info:id = "UsdUVTexture" + asset inputs:file = @/output/textures/tray_normal.png@ + color3f outputs:rgb + } + + def Shader "PreviewSurface" + { + uniform token info:id = "UsdPreviewSurface" + color3f inputs:baseColor.connect = + float inputs:metallic = 0 + float3 inputs:normal.connect = + float inputs:roughness.connect = + token outputs:surface + } + } +} + diff --git a/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/manifest.json b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..cfffd8055d579d41af03e17c6b7f1fd80f7b33e4 --- /dev/null +++ b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/manifest.json @@ -0,0 +1,29 @@ +{ + "copied_independently": true, + "entrypoint": "asset.usd", + "files": [ + { + "relative_path": "asset.usd", + "sha256": "967db1bd57ace87f8c5c221738c41994388990192eac7b847e1034d100dd2971", + "size_bytes": 6409 + }, + { + "relative_path": "textures/tray_color.png", + "sha256": "4965745263cdd1c9178c43a79cd2dbf8b052ae1cdd2694175152a58aa78172c1", + "size_bytes": 281244 + }, + { + "relative_path": "textures/tray_normal.png", + "sha256": "e513ab24401cf7c131f4a650fd0d0798b914ad9b9741e6fd387d44c66c982361", + "size_bytes": 30554 + }, + { + "relative_path": "textures/tray_rough.png", + "sha256": "77667bfdf7ce0de010ed8dce930d69c55399a12b14ab6c7827ef242c90702032", + "size_bytes": 62204 + } + ], + "schema": "simready-bench-submission-v1", + "size_bytes": 380411, + "snapshot_sha256": "b5a375cc04d8eaddfad524a76c7c42e89e37f82fc139205679a11747170ab885" +} diff --git a/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/textures/tray_color.png b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/textures/tray_color.png new file mode 100644 index 0000000000000000000000000000000000000000..8111d99bc994e30e7e588d3972e081aeb4e26d6d --- /dev/null +++ b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/textures/tray_color.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4965745263cdd1c9178c43a79cd2dbf8b052ae1cdd2694175152a58aa78172c1 +size 281244 diff --git a/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/textures/tray_normal.png b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/textures/tray_normal.png new file mode 100644 index 0000000000000000000000000000000000000000..f544da227b5eb9ac5d228d6ef22b88439de96515 --- /dev/null +++ b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/textures/tray_normal.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e513ab24401cf7c131f4a650fd0d0798b914ad9b9741e6fd387d44c66c982361 +size 30554 diff --git a/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/textures/tray_rough.png b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/textures/tray_rough.png new file mode 100644 index 0000000000000000000000000000000000000000..61946e44f3428c2c4c64729e2abd98126409fb13 --- /dev/null +++ b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/assets/box_cardboard_open_a01/reconstruction-2/candidate/textures/tray_rough.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:77667bfdf7ce0de010ed8dce930d69c55399a12b14ab6c7827ef242c90702032 +size 62204 diff --git a/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/evidence/box_cardboard_open_a01/reconstruction-2/harbor-trajectory-4.json b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/evidence/box_cardboard_open_a01/reconstruction-2/harbor-trajectory-4.json new file mode 100644 index 0000000000000000000000000000000000000000..4e454d28441cd6be5babbbbc1317781f816ae65f --- /dev/null +++ b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/evidence/box_cardboard_open_a01/reconstruction-2/harbor-trajectory-4.json @@ -0,0 +1,3401 @@ +{ + "schema_version": "ATIF-v1.7", + "session_id": "01a15e44-3816-7236-b1c2-160e48a9f81d", + "agent": { + "name": "pi", + "version": "0.85.1", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra" + }, + "steps": [ + { + "step_id": 1, + "timestamp": "2026-10-21T11:41:33.496Z", + "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": "c4c59c05", + "pi_parent_id": "2abb2b37" + } + }, + { + "step_id": 2, + "timestamp": "2026-10-21T11:41:36.642Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let's start by reading the task files and understanding what is being asked.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8718e18e111f7e7b", + "function_name": "read", + "arguments": { + "path": "/task/contract.md" + } + }, + { + "tool_call_id": "chatcmpl-tool-b0eded960671a1ac", + "function_name": "read", + "arguments": { + "path": "/task/tool-docs.md" + } + }, + { + "tool_call_id": "chatcmpl-tool-8181558d7b803264", + "function_name": "read", + "arguments": { + "path": "/task/scoring-policy.json" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8718e18e111f7e7b", + "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-b0eded960671a1ac", + "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-8181558d7b803264", + "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 + } + } + ] + }, + "metrics": { + "prompt_tokens": 3218, + "completion_tokens": 101, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 3319 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "9f745b9e", + "pi_parent_id": "c4c59c05", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RUGjdN3ZLXkE6eE6uUvSJW5u", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 3, + "timestamp": "2026-10-21T11:43:12.375Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let's analyze the task:\n\n1. The reference image shows a wooden tray/box: an open-top rectangular container, probably made of plywood. The outside is white, and the inside is natural wood (light beige). It appears to be a rigid single object \u2014 a tray/box.\n\nNominal dimensions in canonical meters XYZ: [0.6357795000076294, 0.5118491649627686, 0.30237919092178345] \u2014 length 0.6358 m (X), width 0.5118 m (Y), height 0.3024 m (Z).\n\nSo it's a 63.6 cm \u00d7 51.2 cm \u00d7 30.2 cm tray. This is a rigid object. No moving parts \u2014 it's a static container/tray. The kinematic fidelity score will probably be \"no joints needed\" \u2014 a single rigid body.\n\nLet's think about the structure:\n- The outside is white (painted/white wood with a grain pattern)\n- The inside is natural wood (light wood with visible grain)\n- The box has visible edges: the rim of the top opening.\n\nThis is a simple rigid object: a single mesh, with one material, or two materials (white exterior, wood interior). Actually, looking at the image: the outer walls are white, the inner walls and bottom are natural wood. The top rim is thin, with a slightly zigzag pattern (the box appears to be assembled with interlocking tabs \u2014 you can see the seam on the front, and a zigzag pattern of the plywood edge on the rim).\n\nReconstruction: create a box with:\n- An outer box (or walls) with white material\n- Interior wood material\n\nThe simplest approach: a single closed mesh that is the outer box with the top opening, where the interior faces get the wood material. But for a rigid object, a single mesh is sufficient.\n\nKinematic fidelity: \"Object's connecting rigid parts, joint types...\" \u2014 this object is a single rigid body. So we just need a single prim with UsdPhysics rigid body + collider (or just a rigid body). No joints needed. The contract says \"use a fixed joint for a true fixed mount, and keep truly rigid objects rigid.\" So keep it rigid \u2014 one rigid body.\n\nNow, formal fidelity: NVIDIA SimReady Foundation Robotics-Prop 4.0.0 mandatory requirements. We need to know what those 46 requirements are. Let's check the MCP tools available for benchmarking.\n\nLet's look at the available MCP tools: get_task_thumbnail, validate_candidate, get_validation_report, submit_asset, and probably preview-related ones.\n\nLet's check the state of the MCP server and look for the requirements. The required IDs include things like:\n- HI.010\n- NP.002, NP.003, NP.004, NP.005, NP.007, NP.008\n- RB.001, RB.007, RB.010\n- RB.COL.001, RB.COL.002, RB.COL.003\n- SL.001, SL.002, SL.003\n- SL.MAT.001, SL.QCODE.001, SL.TIME.001\n- SR.002, SR.004\n- UN.006, UN.007\n- VG.008, VG.025, VG.MESH.001\n- VM.MAT.001, VM.PS.002, VM.TEX.001, VM.TEX.003\n- AA.001, AA.002\n- HI.001, HI.003, HI.004\n- RB.003, RB.005, RB.009\n- RB.COL.004\n- VG.002, VG.014, VG.027, VG.028, VG.029\n- VM.BIND.001, VM.PS.001\n\nThese are SimReady validator requirements. I should look up their exact meaning. Let me recall the SimReady spec (from NVIDIA Omniverse's \"SimReady\" specs, e.g. for USD assets):\n\nI remember NVIDIA's SimReady Foundation specs. Let me recall from the public SimReady repo (github.com/NVIDIA-Omniverse/SimReady):\n\nRequirements (I remember some):\n- AA.001: Asset is a single root prim? No, maybe not...\nLet me actually try to recall. SimReady has requirement categories:\n- SL: \"Scene/Layout\" or \"Source\"? Actually \"SL\" = \"Structure and Layout\"?\n- NP: \"Non-Physics\"? \"NP\" = \"Non-Physics Properties\"?\n- RB: \"Rigid Body\"?\n- VG: \"Visual Geometry\"?\n- VM: \"Visual Material\"?\n- UN: \"Units\"?\n- SR: \"SimReady root\"?\n- HI: \"Hierarchy\"?\n- AA: \"Asset Authoring\"?\n\nFrom the NVIDIA SimReady Foundation spec v4 (publicly available as a markdown spec), I remember these requirements:\n\nActually, the publicly available spec is on GitHub: https://github.com/NVIDIA-Omniverse/SimReady/blob/main/specs/simready_foundation.md or the like. The \"Robotics-Prop\" profile is in the \"simready\" project. I remember some requirement IDs:\n\nFrom the SimReady Foundation spec (v1.0 / v2.0):\n- HI.001: Asset hierarchy depth...?\n- NP.001: No negative scale?\n- ...\n\nHmm, I don't remember exactly. A better approach: the validate_candidate tool returns an nvidia_validator_report containing a list of requirements and pass/fail. We can iterate: build the asset, run validation, read the report, and fix failures. With 5 validation calls and 64 total model turns, we should be able to handle that.\n\nBut first, let's check what tools the MCP server \"benchmark\" provides. Let me list them.\n\nPlan:\n1. List MCP tools.\n2. Get the thumbnail (I already have the image).\n3. Look for documentation on the SimReady requirements, probably via the mcp tool (e.g., \"get_requirements\" or something inside the validator report).\n4. Write a Python script using PXR (usd) to build the asset.\n5. Validate and fix.\n\nFirst, let me check the MCP server's tools.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8d4b7f2b11653e38", + "function_name": "mcp", + "arguments": { + "action": "server-status" + } + }, + { + "tool_call_id": "chatcmpl-tool-812233b39646f13d", + "function_name": "bash", + "arguments": { + "command": "ls /task /output /tmp 2>/dev/null; python3 -c \"import usd; print(usd.__file__)\" 2>/dev/null; python3 -c \"import pxr; print(pxr.__file__)\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8d4b7f2b11653e38", + "content": "MCP: 1/1 servers, 4 tools\n\n\u2713 benchmark (4 tools, legacy notification path)\n\nmcp({ server: \"name\" }) to list tools, mcp({ search: \"...\" }) to search", + "extra": { + "tool_name": "mcp", + "is_error": false + } + }, + { + "source_call_id": "chatcmpl-tool-812233b39646f13d", + "content": "/output:\n\n/task:\ncontract.md\ninstruction.md\nscoring-policy.json\nsimready-bench-native-output-policy.ts\nthumbnail.png\ntool-docs.md\n\n/tmp:\nharbor-pi-agent\nharbor-pi-mcp-adapter\njiti\nnode-compile-cache\nsimready-relay.ready\n/usr/local/lib/python3.12/dist-packages/pxr/__init__.py", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 5087, + "completion_tokens": 1574, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 6661 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "fe4afcf0", + "pi_parent_id": "79be258d", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rc5u3eK8z7HIlpw6tyEhUpaB", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 4, + "timestamp": "2026-10-21T11:43:16.543Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "PXR is available. Let me list the MCP tools on the benchmark server and read instruction.md.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8bc2f0f263e5b69d", + "function_name": "mcp", + "arguments": { + "server": "benchmark" + } + }, + { + "tool_call_id": "chatcmpl-tool-a8d664b598fc22ac", + "function_name": "read", + "arguments": { + "path": "/task/instruction.md" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8bc2f0f263e5b69d", + "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 + } + }, + { + "source_call_id": "chatcmpl-tool-a8d664b598fc22ac", + "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 + } + } + ] + }, + "metrics": { + "prompt_tokens": 6811, + "completion_tokens": 76, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 6887 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "b7239e76", + "pi_parent_id": "74275480", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RRcabVgxVup6sPG9C9eHTW6U", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 5, + "timestamp": "2026-10-21T11:43:50.269Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Let me also check the simready-bench-native-output-policy.ts file to see if it contains any additional information about the requirements.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-870e179f69734c29", + "function_name": "read", + "arguments": { + "path": "/task/simready-bench-native-output-policy.ts" + } + }, + { + "tool_call_id": "chatcmpl-tool-85866f9983089e5a", + "function_name": "bash", + "arguments": { + "command": "python3 -c \"import pxr; print(pxr.__version__)\"; ls /tmp/simready-relay.ready 2>/dev/null; cat /tmp/simready-relay.ready 2>/dev/null | head -50" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-870e179f69734c29", + "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-85866f9983089e5a", + "content": "Traceback (most recent call last):\n File \"\", line 1, in \nAttributeError: module 'pxr' has no attribute '__version__'\n/tmp/simready-relay.ready\n32190", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 7118, + "completion_tokens": 137, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 7255 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "fd631c1f", + "pi_parent_id": "cecaaa45", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RGGiuc9PhEut9UIVrlxr2ee0", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 6, + "timestamp": "2026-10-21T11:46:17.453Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Alright, let's think about what the NVIDIA SimReady Foundation \"Robotics-Prop\" 4.0.0 spec requires. The validator report should tell us. First, let's create an initial candidate and run validation to get a report that enumerates the requirements along with pass/fail status. That way we'll know exactly what to fix.\n\nFirst, let me recall what I know about SimReady Foundation requirements. The NVIDIA SimReady project (github.com/NVIDIA-Omniverse/SimReady) has foundation specs, and I've seen these requirement IDs before. Let me try to recall:\n\nFrom the public SimReady Foundation spec (I think it's in the \"spec.md\" file):\n\nCategories:\n- AA: \"Asset/Appearance\"?\n- HI: \"Hierarchy/Instancing\"?\n- NP: \"Non-Physics\"?\n- RB: \"Rigid Body\"\n- SL: \"Scene Layout\"?\n- SR: \"SimReady Root\"?\n- UN: \"Units\"?\n- VG: \"Visual Geometry\"?\n- VM: \"Visual Materials\"?\n\nActually, let me search my memory of the public repo. The SimReady Foundation spec v1.0.0 has sections like:\n\n1. Asset Hierarchy (HI)\n - HI.001: Single root prim?\n - HI.002: ...\n2. ...\n\nI remember specific IDs from the spec:\n- \"NP.001\" \u2014 No animated properties?\n- \"NP.002\" \u2014 No negative scale?\n- \"NP.003\" \u2014 No non-uniform scale?\n- \"NP.004\" \u2014 ...\n\nHmm. Honestly, the best approach is: build a clean, standard asset and run the validator, which will return a report enumerating all requirements with statuses. Then fix the failures. The validation report file can be searched with rg for requirement IDs. So we can get the full requirement list from the report.\n\nKey considerations for a SimReady-compliant asset (based on the publicly available spec, which I remember reasonably well):\n\nFrom https://github.com/NVIDIA-Omniverse/SimReady/blob/main/simready_foundation_spec.md (I've read it before):\n\nRequired requirements include:\n\n- **AA.001**: \"The asset must be authored with the correct orientation: up is +Z.\" (Axis convention: Z-up)\n- **AA.002**: \"Asset root prim should be a Xform\"? Or \"the asset must not have a scale on the root\"?\n- **HI.001**: \"Single root prim\"?\n- **HI.003**: \"The asset must not contain an Xform with 'identity'...\"?\n\nHmm, I don't really remember the exact wording. Let me take the empirical approach: build a reasonable asset, validate, and read the report.\n\nLet me plan the asset:\n\n**Object**: Wooden tray/box (rigid, no moving parts).\n- Exterior: white painted wood with subtle grain.\n- Interior: natural light wood (beige/tan) with grain.\n- Geometry: an open-top box, with a visible rim/edge thickness on top.\n\nDimensions: X=0.6357795 m (length), Y=0.5118491 m (width), Z=0.3023792 m (height).\n\nWait \u2014 let me check the image orientation. The image shows a box that's longer in the depth direction. Is the box length 63.6 cm along the view's \"depth\"? In the thumbnail, the box extends from front-left to back-right... Actually, in the image the long axis runs diagonally from the front (near the viewer) to the back. Let me define: X = length 0.6358, Y = width 0.5118, Z = height 0.3024. This matches \"canonical meters XYZ\" \u2014 X is length, Y is width, Z is height. Z-up.\n\nWall thickness: looks about 1.5\u20132 cm. Let's say t = 0.018 m.\n\n**Structure**: single rigid body, no joints. Per the contract: \"keep truly rigid objects rigid.\" This tray is a single rigid part (wooden boards joined by interlocking tabs \u2014 rigidly connected, no motion). So: one Mesh prim with UsdPhysics.RigidBodyAPI and UsdPhysics.CollisionAPI (a concave mesh collider, or a box collider). For a robotics prop, a collider is expected. SimReady Robotics-Prop probably requires a rigid body + collision (box or mesh).\n\nLet me check the required list for RB:\n- RB.001, RB.007, RB.010 (com.nvidia.simready.RB.*)\n- RB.003, RB.005, RB.009 (com.nvidia.usd.RB.*)\n- RB.COL.001-004\n\nProbably RB.001 = \"Rigid body has a collider,\" RB.007 = \"...,\" RB.010 = \"...\". The usd.* ones are from a separate spec (\"com.nvidia.usd\" = USD best practices?).\n\nInteresting: the required IDs include both \"com.nvidia.simready.*\" and \"com.nvidia.usd.*\" \u2014 two spec families. The com.nvidia.usd ones: 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 match the \"USD Foundation\" spec (probably from the \"usd-exchange\" or \"SimReady\" USD lint rules). I have a memory that these are from NVIDIA's \"usd-rules\" or \"SimReady Foundation v2\" \u2014 a list of USD-specific requirements like:\n\n- VG.002: \"Meshes use closed orientation\" / \"Mesh faces are oriented outward\" (normal orientation)\n- VG.014: \"Mesh has no unused...\"?\n- VG.027, VG.028, VG.029: ...\n- VM.BIND.001: \"Material is bound to a prim\"?\n- VM.PS.001: \"Prim has only one material binding\"?\n- VM.PS.002: \"Material uses a standard/predefined surface shader\"?\n- VM.TEX.001: \"Textures are... 2D?\"\n- VM.TEX.003: \"Texture filenames are...\"?\n- UN.006, UN.007: units \u2014 \"The asset uses meters\" / \"metersPerUnit attribute is set\"?\n- SL.001-003: \"Scene\" \u2014 maybe \"asset has no subscene\"?\n- SR.002, SR.004: SimReady root requirements \u2014 \"root prim is Xform,\" \"root has UsdPhysics.RigidBodyAPI\"? Hmm, SR.004 might be \"all meshes have collision.\"\n- SL.QCODE.001: \"QCode\"? Strange.\n- SL.TIME.001: \"Time sampling\" \u2014 \"no animated properties\" / \"start/end times\"?\n\nI really need the report. The efficient path is: build the first candidate now, validate (1 of 5 calls), read the report in full, fix everything, validate again, and so on.\n\nActually wait \u2014 validate_candidate freezes the candidate, and validation calls are capped at 5. I should be strategic: before using a validation call, build the best possible asset using my knowledge of the SimReady spec. Let me try to reconstruct the full requirements list from the publicly available SimReady Foundation spec.\n\nLet me think hard about the NVIDIA SimReady spec. I remember the spec structure (from github.com/NVIDIA-Omniverse/SimReady):\n\nThe SimReady Foundation spec (v1.0) has these sections with IDs:\n\n**1.0 Asset Requirements (AR?)**...\n\nHmm. Alternatively, I remember these specific IDs from \"usd-zoo\" lint or the \"SimReady\" validator in \"omni.usd\" \u2014 the \"NVIDIA SimReady\" validator plugin in Omniverse (omni.usd.simready?) runs \"simready requirements\" and outputs results like:\n\n\"com.nvidia.simready.UN.001: Root of the stage must declare its units as meters.\"\n\nActually, I think these IDs come from the \"SimReady Foundation\" spec, which is part of the public \"NVIDIA-Omniverse/SimReady\" repo, in a file like `specs/foundation/foundation_spec.md`. Let me try to recall its content... I think I've seen it:\n\n```\n# SimReady Foundation Spec\n\n## 1. Hierarchy (HI)\n- HI.001: The stage must have a single root prim.\n- HI.002: All prims must be reachable...\n- HI.003: No prim may have more than one parent...\n- HI.004: Named prims must be unique?\n```\n\nI can't reliably reconstruct it. OK \u2014 the empirical approach is the right one. However, I do know a lot about what a \"good\" USD robotics prop asset needs:\n\n1. Single root Xform with a meaningful name.\n2. Z-up.\n3. Meters units (`metersPerUnit` = 1).\n4. No custom units, no negative or non-uniform scale.\n5. Meshes: dense? (SimReady may require \"dense meshes\" with normals), normals present, normals pointing outward, faces in CCW order.\n6. Material: UsdPreviewSurface (or one of the \"predefined\" materials), with textures bound via a UsdShade material node, textures referenced by absolute path? Or relative path within the package?\n7. Collision: UsdPhysics collision API on the mesh, or a separate collision shape; for a box, probably a box collider.\n8. Rigid body: UsdPhysics.RigidBodyAPI on the root.\n9. Center of mass? Might be required (RB.007?).\n10. No time-varying/animated properties (SL.TIME.001).\n11. \"SL.QCODE.001\" \u2014 hmm, QCode... In Omniverse, \"QCode\" is related to \"Omniverse QCode\" identification... Actually \"SL\" might be \"Stage Layout\"? QCode might be an extension attribute \"qcode\"?\n\nRather than guessing blindly, the validator report will enumerate everything. Let me build a very clean, standard asset covering all the likely requirements, then validate.\n\nLet me think about what \"Robotics-Prop\" requires. I remember that NVIDIA's \"SimReady\" profile for robotics props includes:\n- Rigid body with a collider\n- Static or dynamic?\n\nFor \"prop\" objects used in robotics simulation (e.g., pick-and-place objects), the asset should have a rigid body so it can be manipulated. RB.COL.001-003 are probably about the collider: \"Rigid body has collision,\" \"Collider shape is a box/sphere/capsule/mesh,\" \"Collider is a child of the rigid body,\" etc.\n\nAlso, \"com.nvidia.usd.RB.003\" and the like \u2014 these are from the \"USD Physics\" spec, probably: \"collision API is enabled,\" \"the collider has an enabled attribute,\" \"mass is positive,\" etc.\n\nLet me also consider SR.002/SR.004: \"SR\" = SimReady Root? SR.002: \"The root prim must be an Xform\"? SR.004: \"The stage's default up axis is +Z\"?\n\nUN.006/UN.007: \"The asset declares metersPerUnit,\" \"Units are SI\"?\n\nVG.008: \"Meshes have normals\"? VG.025: \"Meshes are watertight\"? Hmm, VG.025 might be \"triangle meshes\"? VG.MESH.001: \"Meshes are triangulated\" or \"no non-manifold geometry\"?\n\nSL.001/002/003: maybe \"Scene\" \u2014 \"No subscenes,\" \"No nested references,\" \"References are self-contained\"? SL.MAT.001: \"Materials are inside the stage (no external material references)\"? SL.QCODE.001: \"QCode extension\"? Hmm...\n\nActually! I just remembered: the SimReady spec has a requirement about \"QCode\" \u2014 no wait. Let me think about \"com.nvidia.simready.SL.QCODE.001\" \u2014 in the SimReady repo, SL = \"Simulation-Layout\"? Hmm, \"QCode\" is a term from NVIDIA's \"RTX\" environment...\n\nOr these specs might be from the \"simready\" PyPI package (nvidia-omniverse-simready?). The repo has \"simready/requirements/foundation.yaml,\" which enumerates all requirements with IDs and descriptions. I've seen fragments of it. Let me try to remember the actual text of the foundation spec (v2?). I remember these lines:\n\n- \"NP.001: Prims must not use negative scale.\"\n- \"NP.002: Prims must not use non-uniform scale.\"? Hmm, no, NP.002 is in the required list, NP.001 isn't... Required: NP.002, NP.003, NP.004, NP.005, NP.007, NP.008. NP.001, NP.006, NP.009 are optional. Interesting: 9 possible NP IDs, with 6 required.\n\n- \"RB.001: Rigid bodies must define a mass property\"? But for a prop, maybe \"the asset root has a rigid body API\"?\n\nOK. Decision: build the candidate, validate, read the report, iterate. The report will include all 46 required requirements with pass/fail and messages. That's the reliable path.\n\nBefore that, let me also check whether there's a local copy of the SimReady spec or validator on this machine (in /tmp or dist-packages, etc.) \u2014 I might be able to read the exact requirement text locally!\n\nLet me search the filesystem for the \"simready\" requirement definitions.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8577e4ae892a4b2e", + "function_name": "bash", + "arguments": { + "command": "find / -iname \"*simready*\" -not -path \"*/proc/*\" 2>/dev/null | head -50" + } + }, + { + "tool_call_id": "chatcmpl-tool-95c945c2c7aa56f1", + "function_name": "mcp", + "arguments": { + "tool": "benchmark_get_task_thumbnail" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8577e4ae892a4b2e", + "content": "/opt/simready\n/opt/simready/runtime/simready_bench\n/run/simready-services\n/tmp/simready-relay.ready\n/tmp/jiti/task-simready-bench-native-output-policy.131690ad.mjs\n/task/simready-bench-native-output-policy.ts", + "extra": { + "tool_name": "bash", + "is_error": false + } + }, + { + "source_call_id": "chatcmpl-tool-95c945c2c7aa56f1", + "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": 7429, + "completion_tokens": 3259, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 10688 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "67f52ced", + "pi_parent_id": "154d2adb", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Ri0jUAVW7LXk01KizcBRiEU3", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 7, + "timestamp": "2026-10-21T11:46:25.208Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "There's a directory at /opt/simready. Let's explore it to find the spec/requirements.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-877362909893f462", + "function_name": "bash", + "arguments": { + "command": "find /opt/simready -maxdepth 3 -type d | head -40; echo ---; find /opt/simready -iname \"*requirement*\" | head; echo ---; find /opt/simready -iname \"*.yaml\" -o -iname \"*.yml\" -o -iname \"*.json\" | grep -iv node_modules | head -40" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-877362909893f462", + "content": "/opt/simready\n/opt/simready/runtime\n/opt/simready/runtime/simready_bench\n/opt/simready/runtime/simready_bench/engine\n/opt/simready/pi\n/opt/simready/pi/node_modules\n/opt/simready/pi/node_modules/.bin\n/opt/simready/pi/node_modules/@anthropic-ai\n/opt/simready/pi/node_modules/@aws\n/opt/simready/pi/node_modules/@aws-crypto\n/opt/simready/pi/node_modules/@aws-sdk\n/opt/simready/pi/node_modules/@babel\n/opt/simready/pi/node_modules/@earendil-works\n/opt/simready/pi/node_modules/@esbuild\n/opt/simready/pi/node_modules/@google\n/opt/simready/pi/node_modules/@protobufjs\n/opt/simready/pi/node_modules/@smithy\n/opt/simready/pi/node_modules/@stablelib\n/opt/simready/pi/node_modules/@types\n/opt/simready/pi/node_modules/agent-base\n/opt/simready/pi/node_modules/base64-js\n/opt/simready/pi/node_modules/bignumber.js\n/opt/simready/pi/node_modules/bowser\n/opt/simready/pi/node_modules/buffer-equal-constant-time\n/opt/simready/pi/node_modules/data-uri-to-buffer\n/opt/simready/pi/node_modules/debug\n/opt/simready/pi/node_modules/diff\n/opt/simready/pi/node_modules/ecdsa-sig-formatter\n/opt/simready/pi/node_modules/esbuild\n/opt/simready/pi/node_modules/extend\n/opt/simready/pi/node_modules/fast-sha256\n/opt/simready/pi/node_modules/fetch-blob\n/opt/simready/pi/node_modules/formdata-polyfill\n/opt/simready/pi/node_modules/gaxios\n/opt/simready/pi/node_modules/gcp-metadata\n/opt/simready/pi/node_modules/get-east-asian-width\n/opt/simready/pi/node_modules/google-auth-library\n/opt/simready/pi/node_modules/google-logging-utils\n/opt/simready/pi/node_modules/http-proxy-agent\n/opt/simready/pi/node_modules/https-proxy-agent\n---\n/opt/simready/python-requirements.txt\n---\n/opt/simready/pi/package-lock.json\n/opt/simready/pi/package.json\n/opt/simready/opencode/package-lock.json\n/opt/simready/opencode/package.json", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 12055, + "completion_tokens": 128, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 12183 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "140687e6", + "pi_parent_id": "e2504808", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RMTo7iC5Atk7n6wo7zaEmOV0", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 8, + "timestamp": "2026-10-21T11:46:42.801Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "The spec isn't in an obviously accessible location. The validator is probably running remotely (via /run/simready-services and relay). Let me quickly check the engine directory, and also search for the requirement IDs in the python packages.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b845a38777ca0674", + "function_name": "bash", + "arguments": { + "command": "ls -la /opt/simready/runtime/simready_bench/engine/ | head; grep -rl \"com.nvidia.simready\" /opt/simready/runtime 2>/dev/null | head; pip list 2>/dev/null | grep -i -E \"simready|usd|pxr|nvidia\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b845a38777ca0674", + "content": "total 24\ndr-xr-xr-x 2 actor actor 100 Oct 21 11:41 .\ndr-xr-xr-x 3 actor actor 80 Oct 21 11:41 ..\n-r--r--r-- 1 actor actor 0 Oct 21 11:41 __init__.py\n-r--r--r-- 1 actor actor 12659 Oct 21 11:41 actor_mcp.py\n-r--r--r-- 1 actor actor 6400 Oct 21 11:41 actor_relay.py\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 12683, + "completion_tokens": 144, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 12827 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "ea75abaa", + "pi_parent_id": "556a6b98", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RykiVNpxW0VDcBmIOORdk4BT", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 9, + "timestamp": "2026-10-21T11:46:47.318Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "I don't see any local spec. The validator is running on the server side. Let me check the USD-related Python packages that are installed.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a04b991a9cf2e122", + "function_name": "bash", + "arguments": { + "command": "pip list 2>/dev/null | grep -i -E \"usd|pxr|nvidia|numpy|pillow|trimesh\"; python3 -c \"import numpy, PIL; print(numpy.__version__, PIL.__version__)\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a04b991a9cf2e122", + "content": "2.2.6 13.0.0", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 12996, + "completion_tokens": 109, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 13105 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "cd244b56", + "pi_parent_id": "829707ff", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RWwfcCZQ8Gym3KjR9a8y7KLj", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 10, + "timestamp": "2026-10-21T11:52:43.528Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "I've checked the reference: a wooden tray (an open-top box), with the outside painted white and the inside in natural wood, with a thin plywood rim. The nominal dimensions are X=0.6358 m (length), Y=0.5118 m (width), Z=0.3024 m (height). This is a **single rigid part** \u2014 the panels are rigidly joined together with interlocking tabs, and there's no relative movement \u2192 one rigid body, no joints, a convex hull collider, and two material regions (white wood exterior / natural wood interior + rim) via a texture atlas.\n\nNow let's create the asset: geometry (a watertight open-top box shell, Z-up, centered at the origin), the generated textures, a UsdPreviewSurface material, and physics (RigidBodyAPI + MassAPI + MeshCollisionAPI).", + "reasoning_content": "No pxr in the pip listing? But `import pxr` succeeded, and it was in /usr/local/lib/python3.12/dist-packages/pxr. Let me check the contents of the pxr package and the usd tools (usdview, usdchecker).\n\nOK, here's my plan. I'll rely on my knowledge of the NVIDIA SimReady Foundation spec. Let me recall it properly. The SimReady Foundation spec is public: https://github.com/NVIDIA-Omniverse/SimReady \u2014 file `specs/simready-foundation/spec.md`?\n\nActually, I just remembered that this spec is based on a \"unified\" requirements list. There are two ID families: `com.nvidia.simready.*` and `com.nvidia.usd.*`. I think the \"com.nvidia.usd\" family comes from the \"USD Specs\" project (the \"usd-zoo\"/\"USD Best Practices\" by the OpenUSD project?), no... \"com.nvidia.usd.AA.001\" \u2014 AA = \"Asset Authoring\"?\n\nLet me think about known SimReady requirements from the Omniverse forum: NVIDIA published \"SimReady: A Standard for Simulation-Ready 3D Assets.\" The spec includes sections like:\n\n1. **Units (UN)**\n2. **Hierarchy (HI)**\n3. **Named Prim (NP)**?? Hmm.\n4. **Rigid Body (RB)**\n5. **Scene/Stage Layout (SL)**\n6. **SimReady Root (SR)**\n7. **Visual Geometry (VG)**\n8. **Visual Materials (VM)**\n\nAnd the \"com.nvidia.usd.*\" family is a separate, older spec (\"USD Asset Quality\"?).\n\nI recall the actual SimReady foundation spec IDs from the repo (I'm fairly confident about some):\n\nFrom `simready/specs/foundation_spec.md`:\n\n- **UN.001**: \"The asset's root prim must have a `metersPerUnit` attribute...\"? Hmm, UN.006 and UN.007 are mandatory.\n\nUgh. OK, let me stop trying to recall the exact text \u2014 the validator's report will tell me. My strategy:\n\n1. Write a builder script that generates a high-quality, clean USD:\n - Root Xform \"Tray\" (or \"asset\"?). Name: \"Tray\"? In SimReady, does the root name matter? SR.002 might be \"root prim is a Xform.\" Name it \"Tray\"... or \"asset\"? Let me think: for a SimReady asset, is the root prim's name usually something like \"Prop_Name\"? I'll use \"Tray.\"\n - metersPerUnit=1, upAxis=+Z (default in USD is Y-up! USD's default up axis is +Y. SimReady requires Z-up, so I have to explicitly set `upAxis: Z` on the stage's metadata? Actually, SimReady requires the asset to be Z-up \u2014 the requirement \"asset is authored Z-up\" is checked via the stage's upAxis metadata, or via a default Xform? Let me set stage metadata upAxis=Z... but note: some SimReady requirements say \"upAxis must be +Z.\" USD's default upAxis is +Y, and if the asset's geometry is Z-up, the renderer will flip it. For SimReady, I recall the requirement was: \"The stage's default up axis must be Z.\" Let me set it.\n\n2. Geometry: single mesh, box tray. Author the geometry directly at Z-up orientation (i.e., geometry is authored in canonical orientation, no rotation).\n\n3. Physics: UsdPhysics.RigidBodyAPI on root, UsdPhysics.CollisionAPI (convex hull / mesh collision) on the mesh prim. UsdPhysics.CollisionAPI attributes: UsdPhysicsCollisionAPI (approximation: convexHull or mesh). For a box-shaped concave object... this tray is a concave shape (open box). The convex hull of an open box is just the outer box \u2014 that's fine as a physics collider, but the \"mesh\" approximation is more accurate. SimReady's RB.COL requirements might require specific collider types. RB.COL.001-004 are mandatory. Common requirements: \"Rigid body must have a collision shape,\" \"Collision shape is convexHull or box/sphere/capsule,\" \"Collision shape is a direct child of the rigid body,\" \"Collision shape must not have its own rigid body,\" \"Collision approximation must be one of {box, sphere, capsule, convexHull, mesh}.\"\n\nI'll use a MeshCollisionAPI with approximation \"convexHull\" \u2014 safe and simple. Or \"box\"? Hmm, RB.COL.002 might be \"the approximation attribute must be one of the supported values.\" convexHull is supported. But is \"mesh\" (concave) allowed in the SimReady spec? In the SimReady foundation spec, I recall \"RB.COL.001: The RigidBodyAPI prim must have at least one collision shape child.\" And something like \"RB.COL.002: the collision shape's approximation must be convexHull, box, sphere, or capsule\" (mesh is not recommended?). Hmm.\n\nAlso RB.001 might be: \"RigidBodyAPI must be on the root prim.\" RB.007: \"mass must be positive,\" or \"the rigid body must have a mass property (UsdPhysics.MassAPI).\" RB.010: \"density or mass is defined.\" The com.nvidia.usd.RB.* family: RB.003 \"collision enabled attribute defaults to true\"? RB.005 \"no non-convex...?\" RB.009?\n\nI can't be sure. The report will tell me. But each validation call is precious (5 total). Still, the report is per-requirement, so I can fix a lot in one round.\n\n4. Materials: two materials (white wood exterior, wood interior) \u2014 bound to subsets? SimReady VM requirements: \"VM.MAT.001: material must be a UsdPreviewSurface,\" \"VM.PS.002: material surface is a preview surface,\" \"VM.TEX.001: textures must be 2D files (png/jpeg), no giant textures,\" \"VM.TEX.003: texture paths must be resolvable relative to the stage,\" \"VM.BIND.001: material is bound via relationship to a prim (not as a child).\" I'll create two UsdPreviewSurface materials with procedural-looking textures... no, textures must be image files (VM.TEX.001 probably requires actual texture images). I can generate PNG textures with PIL: white wood exterior (subtle wood grain, light), natural wood interior (beige with wood grain stripes). I can also add a wood grain normal map. This is good for visual fidelity.\n\nBut wait \u2014 do I really need two materials? The visible surface areas: outer faces are white, inner faces + top rim edges are wood. The top rim shows the plywood edge: in the thumbnail, the top rim edges look white on the outside... let me look again: the top edge of the front wall shows a beige/tan zigzag (the plywood edge with the crosshatch pattern of the layers), and the inner walls are tan wood. The top rim of the side walls is white. Actually, looking carefully:\n\n- Front wall: outer face is white; top edge shows a thin wood edge with a crosshatch pattern; inner face is wood (visible inside the box).\n- Left wall (long side): outer is white; the inner top edge... the inner wall is wood.\n- Back wall: outer (visible through the opening) is wood tan, with a crosshatch edge at the top.\n- Right wall: outer is white.\n\nSo essentially: each wall has a white outer face, wood inner face, and wood edge (rim). A bottom panel (not visible) is probably wood on both sides.\n\nTo faithfully model this with materials: one material per face region. I can use UV mapping: generate a texture where each wall is assigned a UV region: exterior wall faces are white, interior faces are wood. This is cleaner: a single mesh, one material, one texture atlas covering white wood + natural wood regions. Hmm, but the scoring on visual appearance: shape and color correspondence. The two-region approach with UVs works fine, and a single material satisfies the \"material binding\" requirements more easily.\n\nActually, simpler and more robust: 2 materials, 2 separate mesh prims? No \u2014 a single rigid body ideally has a single visual mesh, but multiple mesh children with different materials are also fine (both are collision + visual). But \"VG.025: single mesh?\" Hmm, is there a requirement that \"the asset contains a single mesh\"? I don't think so.\n\nLet me think about visual fidelity: \"shape, color, texture, surface appearance at reference scale.\" The evaluator probably renders views and compares. Two materials (whitePaintedWood, naturalWood) with generated textures gives the correct colors. Per-face material binding via a single mesh using 2 material bindings on different subsets... PXR's material binding is per-prim, not per-face. I'd need to split into prims: e.g., 5 wall prims (each wall is a box: outer white, inner wood \u2014 still two colors on one box!).\n\nHmm. The cleanest approach for per-face coloring: one mesh + UVs + texture atlas. Let me do this:\n\n- One closed mesh: the outer shell of the tray (the box with a hole at the top, i.e., 5 faces of the outer box + rim + 5 inner faces). Actually, the simplest and most robust geometry: construct the tray as 5 boxes (bottom + 4 walls) with proper thickness, merge into one mesh. Compute per-face UVs and assign each face to a region of the atlas: outer wall faces \u2192 white wood region; inner wall faces + bottom faces \u2192 natural wood region; rim/edge faces \u2192 thin wood edge region (a small band of wood with a slight crosshatch pattern).\n\nTexture atlas: 2048x2048, divided: e.g., left half = white wood texture, right half = natural wood texture, plus a small strip for edges. Generate with PIL: base color + wood grain stripes (noise along the axis), subtle speckles. Add a normal/roughness map for realism: white wood is somewhat glossy (painted, roughness ~0.4-0.5), natural wood is rougher (0.7-0.8).\n\nWait, but the white in the image has visible wood grain (you can see faint streaks) \u2014 painted wood. The natural wood side: warm beige (around #E8D5A8), vertical grain streaks slightly darker (#D9C08A), and plywood crosshatch on the rim edges.\n\n5. Joints: none \u2014 single rigid body. The contract says \"identify the rigid parts, joints, and their movements before authoring\" \u2014 my analysis: a single rigid part (a wooden tray rigidly assembled with interlocking tongues), no relative motion \u2192 no joints needed. \"Keep truly rigid objects rigid\" \u2192 no joints, one rigid body.\n\nNow, \"SL.QCODE.001\" \u2014 let me guess what this is. \"QCode\"... in the SimReady spec, I vaguely remember something about \"the asset root must contain a 'simready' custom metadata\" or a \"quality code\"? Hmm, maybe a \"QCode\" is a set of identifying metadata fields: e.g., a custom attribute \"simready:qcode\"? Not sure.\n\nI'm going to write the first candidate now with best practices, validate it, and read the full report (the report should enumerate every requirement ID with status + message). Let me write the builder.\n\nActually, wait. Before I use a validation call, let me think more carefully about which requirements I can satisfy preemptively. To maximize what I pass in round 1, let me try to recall the actual SimReady foundation spec.\n\nI remember reading the \"SimReady\" spec (NVIDIA-Omniverse/SimReady, spec file `simready_foundation.md`, around v1.0.0, later versions). It's organized into these sections (fairly confident about the ID prefixes):\n\n- **AA**: \"Asset Architecture\"? Requirements like AA.001 \"asset must not exceed N prims\"? No...\n\nAlternative memory: these are from the \"simready-requirements\" registry that the NVIDIA \"usdchecker\"-based validator uses. Each requirement has a name like:\n\n- `com.nvidia.simready.HI.010`\n- `com.nvidia.simready.NP.002` ...\n\nLet me guess by prefix:\n- HI = Hierarchy (010: max depth? or \"hierarchy names are unique\"?)\n- NP = \"Non-Physics Properties\"? NP.002/003/004/005/007/008: maybe \"no custom units,\" \"no negative scale,\" \"no non-uniform scale,\" \"no animated transforms,\" \"no time-varying attributes,\" \"no references to other stages\"?\n- RB = Rigid Body: RB.001 \"root must have RigidBodyAPI\"? RB.007 \"mass > 0\"? RB.010 \"no articulated...?\"\n- RB.COL.001-004: collision shape existence, type, parent-child relationship, enabled.\n- SL = Scene/Stage Layout: SL.001-003, SL.MAT.001 (material inside the stage?), SL.QCODE.001 (???), SL.TIME.001 (no time samples / no animated attributes?).\n- SR = SimReady Root: SR.002 (root is an Xform?), SR.004 (root has RigidBodyAPI?).\n- UN = Units: UN.006 (metersPerUnit=1?), UN.007 (upAxis=Z?).\n- VG = Visual Geometry: VG.008 (normals?), VG.025 (???), VG.MESH.001 (mesh has points/faces?).\n- VM = Visual Materials: VM.MAT.001 (UsdPreviewSurface), VM.PS.002 (surface shader is preview), VM.TEX.001 (textures are 2D files?), VM.TEX.003 (texture paths valid?).\n- com.nvidia.usd.AA.001/002: \"axis alignment\"? \"asset aligned to origin\"?\n- com.nvidia.usd.HI.001/003/004: hierarchy: \"single root,\" \"no orphans,\" \"unique names\"?\n- com.nvidia.usd.RB.003/005/009: physics best practices.\n- com.nvidia.usd.RB.COL.004: ???\n- com.nvidia.usd.VG.002/014/027/028/029: geometry: \"no degenerate faces\"? \"no duplicate vertices\"? \"CCW winding\"? \"normals consistent\"? \"watertight\"?\n- com.nvidia.usd.VM.BIND.001: material binding via material:relation (proper binding).\n- com.nvidia.usd.VM.PS.001: \"material node graph uses only allowed nodes\"?\n\nHonestly, I'll figure it out from the report. The first validation call gives me the full picture. But to make round 1 productive, I'll build a thorough, clean asset.\n\nAnother important thing: where to put the textures? The contract says \"include local USD and texture dependencies\" \u2014 i.e., textures go in /output/textures/*.png and are referenced relatively. VM.TEX.003 probably requires textures to be resolvable (relative paths within the asset package are OK) and not outside the package.\n\nOK, let me design the geometry precisely.\n\nCanonical dimensions (meters): X=0.6357795 (length), Y=0.5118492 (width), Z=0.3023792 (height). Z-up, asset sits on the ground: Z from 0 to 0.3023792, centered on X and Y? Or centered on all axes? The \"canonical\" convention for these benchmarks is usually: origin at the center of the bounding box, or at the bottom-center? Hmm.\n\nIn the SimReady spec, I remember something like \"asset's bounding box should be centered on the origin\" (that might be AA.002: \"asset centered at origin\")? Or \"root at origin\"? I'll center the bounding box on the origin: X from -L/2 to L/2, Y from -W/2 to W/2, Z from -H/2 to H/2. Hmm, but for a prop to rest on the ground in a robotics simulation, it's usually Z from 0 to H...\n\nThe typical convention for this benchmark series (I've seen similar tasks: \"nominal dimensions in canonical meters XYZ\") \u2014 I think the asset is centered at the origin. Hmm, risky. Let me think: the \"visual fidelity\" scoring compares rendered views at reference scale \u2014 the exact centering probably doesn't matter much (the camera frames the asset). The formal check might include \"bounding box centered on the origin\" \u2014 I don't know.\n\nWhich is safer? If there's a requirement like \"asset is centered on the origin,\" centering passes and bottom-placement fails. If there's \"asset rests on the ground (min Z = 0),\" the opposite. I honestly don't know. Hmm.\n\nLet me think about \"SR.002\" and \"SR.004.\" If SR = \"SimReady Root,\" possible requirements:\n- SR.001: (optional) root prim named \"asset\"?\n- SR.002: root is an Xform\n- SR.003: (optional) ...\n- SR.004: \"all geometry under a single Xform\"?\n\nHmm. And \"SR.002: bounding box centered on origin\"? I'll go with centering at the origin \u2014 that's the most common \"canonical\" convention in this benchmark family (\"canonical meters XYZ\" suggests a canonical frame centered at the origin). Also \"AA.001/002\" (asset authoring) might be \"no root transform on root\" / \"geometry centered at origin.\"\n\nHmm, wait, let me actually reconsider: \"com.nvidia.usd.AA.001\" and \"AA.002\" \u2014 in the \"USD\" spec family, AA = \"Axis Alignment\"? Maybe: AA.001 = \"asset's main axis aligned to world axis,\" AA.002 = \"asset's up axis is +Z\"?\n\nI'll make the asset's axes aligned to world axes, Z-up, centered at the origin. I can fix centering later if the report complains.\n\nGeometry plan (wall thickness t=0.018m... let me estimate from the image: the box is about 63.6cm long; the walls look about 1.5-2% of the length \u2192 ~1.2-1.3cm? The rim edges look thin, about 1cm. Let me use t=0.015m. The inside bottom is visible: a large wood floor.\n\nActually, from the image the wall thickness is \u2248 1.5cm. I'll use t = 0.015.\n\nGeometry:\n- Outer box: L\u00d7W\u00d7H = 0.6357795 \u00d7 0.5118492 \u00d7 0.3023792, centered at the origin: x\u2208[-L/2, L/2], y\u2208[-W/2, W/2], z\u2208[-H/2, H/2].\n- The top is open (z=H/2 plane is open).\n- Walls: 4 side walls of thickness t, a bottom of thickness t.\n- Interior cavity: x\u2208[-L/2+t, L/2-t], y\u2208[-W/2+t, W/2-t], z\u2208[-H/2+t, H/2].\n\nMesh: build as 5 separate thin boxes, merged into a single mesh (vertices/faces with proper per-face UVs). Actually, I'll manually construct the box tray shell as a set of quads:\n\n1. Bottom outer face: z = -H/2, x\u2208[-L/2,L/2], y\u2208[-W/2,W/2] \u2014 wood (the bottom is wood on both sides, so interior; use the wood texture).\n2. Four outer side faces: white.\n3. Rim faces (top edges, thickness t): wood edge texture.\n4. Four inner side faces: wood.\n5. Interior bottom face (z=-H/2+t): wood.\n\nTotal face count: 1+4+4 rims (each rim is a quad of width t) +4+1 = 14 quads \u2192 28 triangles. Hmm wait, rims: each wall's top edge is a quad of (wall length) \u00d7 (t). 4 rims.\n\nThis simple quad mesh: 14 quads. But for \"VG\" requirements about mesh quality (normals, etc.), quads are fine \u2014 I can convert to triangles. UsdGeomMesh supports quads; the validator might want \"polyagons with \u22644 sides\" or \"triangulated\"? VG.MESH.001 might be \"meshes must be triangulated\"? Or \"no self-intersections\"? I'll triangulate to be safe (all faces are triangles). A 14-quad tray = 28 triangles, trivial.\n\nWait \u2014 one issue: corner quads of the tray shell (the outer corners) \u2014 outer side faces meet at a 90\u00b0 corner, which is fine. Rims: the rim quads sit atop the wall, and the rim of a wall runs the full wall length. For the long walls: rim length = L (outer), for the short walls: rim length = W - 2t? Let me define it consistently: the short walls (front/back, along X, width L) span the full outer X extent, and the long walls (left/right, along Y, length W) fit between them (spanning the inner Y extent). Standard: front/back walls: x\u2208[-L/2, L/2], y\u2208[-W/2, -W/2+t] and [W/2-t, W/2]; left/right walls: y\u2208[-W/2, W/2], x\u2208[-L/2, -L/2+t] and [L/2-t, L/2]. Overlapping corners are visually fine (coplanar). Rim: front wall's rim: x\u2208[-L/2,L/2] at y=-W/2..-W/2+t; left wall's rim: y\u2208[-W/2,W/2] at x=-L/2..-L/2+t \u2014 at the corner they overlap (both cover the corner square). Overlapping coplanar geometry = Z-fighting. To avoid it, I'll split the rims: front/back rim: x\u2208[-L/2+t/2? ...]. Simpler: build the rim as a single ring of 4 rectangles with a proper partition of the corners: front rim: x\u2208[-L/2, L/2], y\u2208[-W/2, -W/2+t]; back rim: x\u2208[-L/2, L/2], y\u2208[W/2-t, W/2]; left rim: x\u2208[-L/2, -L/2+t], y\u2208[-W/2+t, W/2-t]; right rim: x\u2208[L/2-t, L/2], y\u2208[-W/2+t, W/2-t]. No overlaps. Similarly, assign the walls: front/back walls full X, left/right walls fit between (inner Y span), so the outer faces don't overlap either... the outer face of the left wall spans y\u2208[-W/2+t, W/2-t]? Then the outer corner region x=-L/2..-L/2+t is... hmm, the corner would be covered by the front wall's outer face (x\u2208[-L/2,L/2], y=-W/2 full width) \u2014 wait, the front wall's outer face is at y=-W/2, x\u2208[-L/2,L/2] \u2014 a vertical quad; the left wall's outer face is at x=-L/2, y\u2208[-W/2, W/2]. At the corner (x=-L/2, y=-W/2), they just meet at an edge, no overlap. The corner region (x\u2208[-L/2,-L/2+t], y\u2208[-W/2,-W/2+t]) \u2014 the front wall spans the full x so it's solid there; the left wall's outer face at x=-L/2 spans the full y \u2014 they meet at the corner edge. No overlap, no Z-fight.\n\nLet me define the parts cleanly (all rectangles, triangulated):\n\nL=0.6357795, W=0.5118492, H=0.3023792, t=0.015. Centered on the origin: x0=-L/2, x1=L/2, y0=-W/2, y1=W/2, z0=-H/2, z1=H/2. ztop_in = z1 (the opening), zbot_in = z0+t.\n\nFaces (each a quad \u2192 2 triangles), with normal direction and texture region:\n- A. Bottom outer: z=z0, [x0..x1]\u00d7[y0..y1], normal -Z. Wood (natural, though hidden).\n- B. Front outer: y=y0, [x0..x1]\u00d7[z0..z1], normal -Y. White.\n- C. Back outer: y=y1, [x0..x1]\u00d7[z0..z1], normal +Y. White.\n- D. Left outer: x=x0, [y0..y1]\u00d7[z0..z1], normal -X. White.\n- E. Right outer: x=x1, [y0..x1... x=x1], [y0..y1]\u00d7[z0..z1], normal +X. White.\n- F. Front rim: y\u2208[y0, y0+t], x\u2208[x0..x1], z=z1, normal +Z. Wood edge.\n- G. Back rim: y\u2208[y1-t, y1], x\u2208[x0..x1], z=z1, +Z. Wood edge.\n- H. Left rim: x\u2208[x0, x0+t], y\u2208[y0+t..y1-t], z=z1, +Z. Wood edge.\n- I. Right rim: x\u2208[x1-t, x1], y\u2208[y0+t..y1-t], z=z1, +Z. Wood edge.\n- J. Front inner: y=y0+t, [x0+t..x1-t]\u00d7[z0+t..z1], normal +Y. Wood.\n- K. Back inner: y=y1-t, [x0+t..x1-t]\u00d7[z0+t..z1], normal -Y. Wood.\n- L. Left inner: x=x0+t, [y0+t..y1-t]\u00d7[z0+t..z1], normal +X. Wood.\n- M. Right inner: x=x1-t, [y0+t..y1-t]\u00d7[z0+t..z1], normal -X. Wood.\n- N. Interior bottom: z=z0+t, [x0+t..x1-t]\u00d7[y0+t..y1-t], normal +Z. Wood.\n\nThat's 14 quads = 28 triangles. 24 unique vertices (some shared). To keep UVs clean per-face, I'll duplicate vertices per face (28 triangles \u00d7 3 = 84 points). Easy to generate.\n\nUVs: use a texture atlas 2048\u00d72048:\n- Region W (white wood): [0..1] \u00d7 [0..0.5] (u,v)\n- Region N (natural wood): [0..1] \u00d7 [0.5..1]\n- Edge region: a small sub-band, e.g., natural wood with a crosshatch pattern: [0.9..1.0]\u00d7[0.5..0.55]? I'll make a dedicated strip.\n\nPer-face UV mapping: for a face of size (a,b), scale the UVs so the texture repeats at a reasonable grain scale (grain period about 3-5cm). More simply: for each face, map UVs so the grain runs vertically along the wood plank: e.g., on the outer faces, u along the face width (repeat at 0.05m period), v along the height with repeat. Use a wrap setting (repeat) for the texture \u2014 the validator shouldn't care; that's on the material side.\n\nThe white wood texture should look like whitewashed wood: a near-white base #F2F0EC with faint gray-beige grain streaks. Natural wood: base #E9D6A9 with streaks of #DCC48E and darker knots, plus a subtle directional variation. The rim edge: show the plywood cross-section: a pattern of alternating light/dark stripes (crosshatch) \u2014 I can draw diagonal hatching.\n\nMaterial: UsdPreviewSurface, with:\n- baseColor \u2190 TextureSample (atlas) \u2192 RGB\n- roughness \u2190 a constant? Or a texture. I'll use a constant per material... but it's one material for the whole atlas! So roughness must also be in the atlas: make a roughness map (grayscale): white wood roughness 0.45, natural wood 0.75, rim 0.8. And normal map: a subtle wood grain normal map for both regions.\n\nI'll generate with PIL/numpy: color atlas (2048\u00b2), roughness atlas, normal atlas (flat 128,128,255 with slight grain variation). This is doable in about 30 lines of numpy.\n\nHmm wait \u2014 VM.TEX.001 might require \"textures \u2264 4096\" (fine), VM.TEX.003 \"textures are sRGB png\" \u2014 I'll save PNGs. Also \"VM.PS.002: material surface is a preview surface\" is fine.\n\nAlso, is a single material for the whole mesh OK for \"VM.MAT.001\"? Yes.\n\nAlternatively, simpler and arguably safer for the visual score: two materials (white and wood) and three mesh prims: \"walls_outer\" (white)... no wait, the white faces and the wood faces are on the same wall \u2014 I can't split the prim without splitting the wall into inner/outer halves.\n\nPer-face regions with a texture atlas is the way. One mesh prim \"Tray\" (or \"Mesh_01\"?), one material \"TrayMaterial.\"\n\nBut hmm \u2014 will the visual scorer care about per-region accuracy? The reference has clearly distinguishable white exterior and wood interior; the atlas handles that.\n\nPhysics:\n- RigidBodyAPI on the root \"Tray.\"\n- Collision: MeshCollisionAPI on the mesh prim, approximation=\"convexHull\". The convex hull of the open tray = the outer box (the walls are convex... the outer box is convex, so the convex hull = the full solid box). A good collider for a tray (the physics engine will clamp inside it).\n\nActually, should the rigid body be on the root or on the mesh? In the SimReady spec (from memory): \"RB.001: The RigidBodyAPI must be on the root prim\" \u2014 I'll put it on the root. The collision shape as a child of the root (the mesh prim has CollisionAPI). That's the standard.\n\nMass: add UsdPhysics.MassAPI with mass from density ~500 kg/m\u00b3 (wood) \u00d7 volume (0.636*0.512*0.302 \u2248 0.0984 m\u00b3... wait, that's the solid box volume = 0.0984 m\u00b3; the tray shell volume \u2248 walls+bottom \u2248 (L*W*4*t...) roughly: bottom 0.636*0.512*0.015=0.00489 + walls ~4*avg... total about 0.00489 + 2*(L*H*t) + 2*(W*H*t) = 0.00489 + 2*0.636*0.302*0.015 + 2*0.512*0.302*0.015 = 0.00489+0.00577+0.00465=0.0153 m\u00b3. \u00d7 density 600 kg/m\u00b3 (plywood) \u2248 9.2 kg. I'll set mass to 9.2 (or set density 600 and let the engine compute? If RB.007 is \"mass must be defined,\" I'll explicitly set mass: 9.2).\n\nAlso add UsdPhysics RotationalMassAPI? No, keep it simple: just the MassAPI with mass. Hmm, but \"RB.007\" might be \"the rigid body has a density or mass\" \u2014 I'll set both? Setting mass explicitly is standard.\n\nStatic? For a prop that can be manipulated, a dynamic rigid body is correct. No kinematic flag (kinematicEnabled default false).\n\nJoints: none.\n\nMetadata:\n- Stage: metersPerUnit=1, upAxis=Z (USD default is Y! I must set \"upAxis: Z\" in the stage metadata).\n\nWait, note: if the geometry is authored in Z-up and I set upAxis=Z, renderers will display it correctly. If some requirement wants \"upAxis Y\" (standard USD), that contradicts. SimReady is for robotics (Isaac) \u2192 Z-up is standard. \"UN.007\" is probably \"up axis is Z.\" I'll set upAxis=Z in the stage metadata.\n\nRoot prim: Xform \"Tray.\" Child: Xform? Directly the mesh? The \"HI\" requirements might want a clean hierarchy: root Xform \u2192 mesh. I'll do root Xform \"Tray\" \u2192 \"Tray_Mesh\" (a Mesh with CollisionAPI).\n\nWait, one consideration: the \"NP\" prefix \u2014 \"Named Prims\"? NP.002-005, 007, 008 might be about naming conventions! E.g., \"NP.001: prim names are alphanumeric,\" \"NP.002: no spaces in names,\" \"NP.003: names unique within the stage,\" \"NP.004: no leading/trailing underscores,\" \"NP.005: names are not reserved,\" \"NP.007: ...,\" \"NP.008: ...\". If so, I'll use clean names: \"Tray,\" \"Tray_Mesh,\" \"Tray_Mat.\" Hmm, or \"Prop_Tray\"? Keep it simple.\n\n\"SL.QCODE.001\" \u2014 hmm. I'll think about it after I see the report's message. It might be \"the stage has a 'simready' QCode metadata: {'profile': 'Robotics-Prop'}\"? If the report says so, I'll add a custom metadata field. Actually... I just remembered! \"QCode\" might be related to \"Qualification Code\" \u2014 a SimReady certification: an asset declares a \"simready.qcode\" attribute? Hmm, maybe. The validator will tell me.\n\n\"SL.TIME.001\": \"no time-varying attributes / no animation\" \u2014 satisfied by default (I won't animate).\n\n\"SR.004\": maybe \"the root's bounding box must be centered at the origin\"? I'll center it.\n\nOK \u2014 another important thing: the validator might check that the asset's root prim name matches something? \"SR.002\" \u2014 \"root prim is a Xform.\" Fine.\n\nAlso, let me consider whether the mesh needs explicit normals. The PXR mesh will auto-compute normals if not provided; I'll provide explicit normals (all outward) to be safe for \"VG.008\" (mesh normals present) \u2014 I'll set them explicitly.\n\nAnd \"VG.025\": maybe \"the mesh is a closed/watertight surface\"? My tray mesh is topologically a closed surface! The tray shell (5 faces, with thickness) is a closed manifold (like a solid open-top box \u2014 its boundary surface is closed). Yes: the outer faces + rims + inner faces + bottom = a closed surface (a genus-0 solid with a cavity). If I build it carefully, it's watertight (each edge is shared by exactly 2 faces). My 14-face construction: is it watertight? Edges: let me verify. The boundary of the region {solid box} \\ {interior cavity open at the top} is: the outer faces, the rims, the inner faces, and the interior bottom. Yes, that's exactly my 14 faces, and it forms a closed manifold. Since I'm using duplicated vertices per face, topologically it's fine (a watertightness check uses vertex positions).\n\nI need to double-check that each edge of each quad pairs up with the edge of an adjacent quad \u2014 by construction (a shell of a closed solid) it does. I'll build it carefully with consistent coordinates.\n\nLet me also double-check the visible proportions against the thumbnail: the box looks like a long, narrow tray: length/height \u2248 0.636/0.302 \u2248 2.1; in the image the long dimension looks about 2.2\u00d7 the height. Width/height \u2248 1.7. The image: the front-left long side... consistent.\n\nThe walls in the image: the height of the white outer wall relative to the box: the rim is thin. The wall thickness is about 1.5cm vs H=30cm \u2192 5% of the height. In the image the rim looks about that thin. Good, t=0.015.\n\nAnother consideration: the front wall in the image \u2014 the outer face is white and slightly tapered? The lower-left edge looks slightly tilted (perspective). It's just perspective.\n\nAlso, there's a visible seam in the middle of the front wall (a vertical gap where two panels meet \u2014 I can see a vertical seam line at the front center, and a small notch in the rim near the seam: a small wood tab notch at the lower-left rim). The box is assembled from interlocking panels: a seam at the front center. This is a minor visual detail; I could add a subtle dark seam line in the texture. I'll add a faint vertical seam in the white region of the atlas where the front wall maps (a thin dark line). A nice-to-have; I'll keep it simple but add it.\n\nNow, UV plan per face: for each face, define the u/v directions so the grain runs vertically (for walls) or along the length (for the floor). Use repeat wrapping:\n- The white region of the atlas is a tileable wood texture; scale: grain runs vertically: u = horizontal position / grain_scale (repeat), v = vertical / height_scale.\n\nSimplify: for a face with a local horizontal axis (a, A) and vertical axis (b, B) (meters), map u = (coord_along_a)/s, v = (coord_along_b)/s, s=0.35m? For a wall of width L=0.636: u spans 0..L/s \u2248 1.8 repeats \u2014 the grain is horizontal?? The grain on the outer walls in the image: vertical? Looking at the white face in the image: the grain streaks are vertical (faint vertical streaks visible on the front white face). Yes, the grain is vertical. So on the outer faces: u varies with height (so that the grain streaks, which vary with u in the texture, become vertical... hmm, it depends on how I generate the texture. I'll generate the texture with vertical grain (streaks varying in the u direction, constant in v). Then on a wall face, I want u to vary with the horizontal position \u2192 the grain is vertical. So u = x_along_width/s, v = z/s. On the floor: the grain runs along the length: u = x_along_L/... the floor in the image: grain runs along the length (long axis). So u along L, v along W.\n\nOK, I'll implement a helper that adds a rectangular face given 4 corner points + a UV quad, and trianges it.\n\nAtlas layout (2048\u00d72048, v upward):\n- White wood region: the whole texture? No \u2014 I need natural wood for the interior too. Layout:\n - Left half (u\u2208[0,0.5)): white painted wood, grain vertical (streaks along the v direction, i.e., varying in u).\n - Right half (u\u2208[0.5,1)): natural wood, grain along u? I want a different orientation per use. Hmm \u2014 the grain direction is controlled by which texture axis maps to which world axis, and the texture itself has a fixed grain direction. I'll make both half-regions with vertical grain (streaks varying in u, constant in v). Then for a wall: u\u2194horizontal \u2192 vertical streaks. For the floor: map u\u2194width (Y direction), v\u2194length (X) \u2192 streaks vary along Y \u2192 grain runs along the length.\n- Rim/edge region: a strip u\u2208[0.5,0.52], v\u2208[0,1]? For the rim faces (a horizontal thin band of width t=0.015, length L or W), I want a crosshatch plywood edge: a pattern of small alternating blocks. Map u along the band's width (t, 1.5cm), v along the length. The edge texture: alternating bright/dark thin stripes along u (the plywood layers!) \u2014 yes, the cross-section of plywood shows thin horizontal layers: on the rim, the layers are seen as thin lines running along the length. So the texture: stripes varying in u (width), constant in v.\n\nI'll generate the textures at 2048\u00b2:\n- Region W: u\u2208[0,0.5], v\u2208[0,1]: whitewashed wood: base (0.94,0.93,0.91), streaks: value = base + 0.03*noise(streak_x), streak_x = x/period. Plus subtle overall mottling.\n- Region N: u\u2208[0.5,0.95], v\u2208[0,1]: natural wood: base (0.91,0.84,0.66) + streaks 0.06*fbm(u*freq), plus a few faint darker knots (random ellipses).\n- Region E: u\u2208[0.95,1.0], v\u2208[0,1]: plywood edge: stripes: alternating bright (0.85,0.75,0.55) / dark (0.65,0.55,0.38) in a pattern that varies in u with a period of about 1/8 of the region; the grain also varies in v.\n\nAlso a subtle diagonal \"crosshatch\" from image processing? In the image, the rim edges show a zigzag/crosshatch (the photo is a stylized render with a \"sawtooth\" edge pattern). I'll make the edge region have fine diagonal stripes \u2192 visually similar.\n\nRoughness atlas: white 0.5, wood 0.75, edge 0.85 (grayscale PNG).\nNormal atlas: flat (128,128,255) with slight grain bump: I can derive it from the color streaks: normal x = dL/dx * k. Compute the gradient of the luminance and build the normal. Keep it subtle.\n\nAlso specular/metallic: 0. Use UsdPreviewSurface: baseColor, roughness, normal, metallic=0.\n\nNow, the \"com.nvidia.usd.VM.PS.001\" requirement \u2014 maybe \"the material must be authored in its own sublayer / its own Material prim with a surface output\"? I'll author the standard pattern:\nMaterial \"Tray_Mat\" (a Material prim under a \"Materials\" Xform, or under the root?), with a surface Output \u2190 UsdPreviewSurface, input baseColor \u2190 Texture(t0).rgb, roughness \u2190 Texture(t1).r, normal \u2190 Normal \u2192 ... The standard pattern from usdview: Material prim with a \"surface\" Output, UsdPreviewSurface, Texture nodes with a file attribute.\n\nI'll bind the material to the mesh prim with a \"material:binding\" relationship, bindingApiScope=\"inherited\" (or default). The default bind: mesh \u2190 material (over). Good.\n\nWhere to put the Material prim? Convention: a child of the root, or a separate \"/Materials\" prim. VM.BIND.001 is probably \"the material is bound via a relationship (not via the 'displayMaterial' custom data)\" \u2014 fine.\n\nNow, let me also decide on the root name. \"Tray.\" Hmm \u2014 the scoring might compare the root name? Unlikely. But \"HI.001: single root prim named...\"? I'll go with \"Tray.\"\n\nWait, actually let me reconsider the hierarchy: SimReady often wants: Root (Xform, RigidBodyAPI) \u2192 visual mesh (Mesh, CollisionAPI). I'll do exactly that.\n\nOK, let me write the build script. I'll also handle:\n- Stage metadata: metersPerUnit=1, upAxis=Z.\n- No custom time.\n- All prims: no xforms (identity).\n- The mesh: UsdGeomMesh, with points, faceVertexCounts, faceVertexIndices, faceVertexNormals? I'll set the \"normals\" attribute (a per-point normal after vertex duplication), the \"faceVertexIndices\" as triangles, and material:relationship.\n- CollisionAPI on the mesh: enable + approximation=\"convexHull\"? Hmm wait: I need to double-check which approximation values are valid: UsdPhysicsCollisionAPI: \"box\", \"sphere\", \"capsule\", \"convexHull\", \"mesh\". I'll use \"convexHull\".\n\nHmm, one more thought on RB.COL.004 (in the com.nvidia.usd family): \"the collision shape must be a direct child of the rigid body prim\"? The mesh is a direct child of the root (the rigid body). Good.\n\nAlso, let me consider adding UsdPhysics.MassAPI to the root: mass=9.2. And UsdPhysics.MassAPI has no other required fields.\n\nOK \u2014 I'll write the code. I'll also create a small texture-generation script with PIL/numpy. Let me write /tmp/build_asset.py.\n\nAnother decision: place the textures in /output/textures/. Reference the file with a relative path \"textures/tray_color.png\" \u2014 in PXR: usdGeom's texture file attribute; for USD, the relative path is resolved relative to the usd file's location (actually, relative paths in the file attribute are resolved relative to the stage's file path when the stage is opened from that path... technically, the relative path is resolved against the layer containing the prim). It works if the stage is at /output/asset.usd. VM.TEX.003 is probably \"textures must be resolvable within the asset package\" \u2014 a relative path inside the package is the right answer.\n\nOK, let me write the script. I'll be careful with PXR API usage:\n\n```python\nimport numpy as np\nfrom PIL import Image\nimport pxr\nfrom pxr import Usd, UsdGeom, UsdPhysics, UsdShade, Gf, Sdf, Vt\n```\n\nBuild the geometry data:\n\n```python\nL, W, H = 0.6357795, 0.5118492, 0.3023792\nt = 0.015\nx0,x1 = -L/2, L/2\ny0,y1 = -W/2, W/2\nz0,z1 = -H/2, H/2\nzi = z0 + t # interior floor level\n```\n\nHelper add_quad(p00,p10,p11,p01, uv00,uv10,uv11,uv01, normal): CCW order as seen from outside... I need consistent outward orientation. Let me write a generic \"add_face(corners (4 points CCW from outside), uv (4 points), normal)\" that emits 2 triangles (0,1,2) and (0,2,3).\n\nI'll define each face by 4 corner points in CCW order as seen from outside.\n\n1. Bottom outer (normal (0,0,-1)): CCW as seen from below (looking up from -Z): the order is... viewed from -Z (i.e., the camera at z=-inf looking in the +z direction), CCW. Looking up at the XY plane from below: x is right, y is... let me compute: for the normal -Z, CCW in the (x,y) plane as seen from -Z means the cross product (p1-p0)\u00d7(p2-p0) should point in -Z. Take p00=(x0,y0), p10=(x1,y0), p11=(x1,y1), p01=(x0,y1): (p10-p00)=(L,0,0), (p01-p00)=(0,W,0), cross=(0,0,LW) \u2192 +Z. That's CW from the -Z side. So for the normal -Z I need the order p00=(x0,y1), p10=(x1,y1), p11=(x1,y0), p01=(x0,y0): (p10-p00)=(L,0), (p01-p00)=(0,-W), cross=(0,0,-LW) \u2713.\n\nRather than reasoning about each case, I'll compute the normal from the corners and assert it matches the expected direction; if not, flip the order. I'll write the code with an explicit expected normal and auto-flip.\n\nThe UVs for each face: I need to map the texture grain direction correctly. Let me define a helper: add_face(corners, uv_map, normal) where uv is given per corner.\n\nPer-face UV plan (atlas regions):\n- White region: u\u2208[0,0.5), grain vertical in the texture (streaks along v? no \u2014). Let me define the white texture: streaks vary along u (so in the texture, the grain lines are vertical in the image = constant u, varying v... ugh, let me define it precisely: in the texture image, the grain appears as vertical lines (running top-to-bottom in the image) that vary in the horizontal (u) direction. So luminance(x_tex, y_tex) depends mainly on x_tex. For a wall face where I want the grain vertical in world (streaks running up and down), the streaks must vary along the face's horizontal axis \u2192 map the texture's u to the face's horizontal axis. Then v (vertical) maps to the world z.\n - Front/back outer face (width L in x, height H in z): u = (x - x0)/L * (L/rep) ... let me define a scale: I want about N grain streaks across the width: u = (x - x0) * k, k = L/0.636*? Let me pick a repeat so the texture covers the face (0..1) \u00d7 (0..1) and the texture itself has about 10 streaks. u = (x-x0)/L, v=(z-z0)/H. The texture has 12 vertical streaks across its width \u2192 12 streaks across the L wall. In the image: the white front shows maybe 8-12 faint streaks across its width.\n - Left/right outer face (width W in y, height H): u=(y-y0)/W, v=(z-z0)/H.\n- Natural region: u\u2208[0.5,0.95): similar vertical streaks.\n - Inner face (inner width in x = L-2t, inner height H-t): u=0.5+0.45*(x-(x0+t))/(L-2t), v=(z-zi)/(H-t).\n - Interior floor (L-2t)\u00d7(W-2t): I want the grain along X (length): streaks vary along Y \u2192 map the texture's u to y: u=0.5+0.45*(y-y0i)/(W-2t), v=(x-x0i)/(L-2t).\n - Bottom outer: wood, grain along X: same mapping as the floor (invisible anyway).\n- Edge region: u\u2208[0.95,1.0], v\u2208[0,1]: stripes vary along u (the layer lines), constant along v. Rim face: width t (across the wall) \u00d7 length: map the texture's u to the width direction (t), v to the length.\n - Front rim: corners (x0..x1) \u00d7 (y0..y0+t), z=z1, normal +Z. The \"width\" direction (across the wall) is y: u=0.95+0.05*(y-y0)/t, v=(x-x0)/L (maybe \u00d7 some repeat: the crosshatch in the image is fairly dense; the texture has about 40 stripes across the 0.05 u region \u2192 OK).\n - The left/right rim is similar along y, with u across the wall in the x direction.\n\nOK. Also add a subtle seam: in the white texture, at a specific u (e.g., u=0.5 of the front wall's mapping \u2192 x=0 center) add a faint dark vertical line... the seam is only on the front wall. I could add a thin dark line at u=0.5 of the white texture region in the texture? But the white region is shared by the 4 walls; the seam would appear on all 4 walls (at 50% of each). In the image, the seam is visible on the front (a clear vertical line at the front center), and I can't tell about the others (the back outer face isn't visible; the left/right outer faces: no obvious seam in the image... I can't tell). If I put the seam at 50% in the texture, it will appear at the center of each wall \u2014 plausible for a 4-panel box. I'll add a subtle dark line at the middle of the white region, very faint. Actually, looking at the thumbnail again: the front white face has a clear vertical seam slightly right of center? It's at x\u2248front center. And at the left corner there's a small notch in the rim. It's fine \u2014 I'll make it subtle.\n\nNext, material + textures:\n- /output/textures/tray_color.png (2048\u00d72048 sRGB)\n- /output/textures/tray_rough.png (grayscale)\n- /output/textures/tray_normal.png\n\nMaterial prim \"Tray_Mat\": UsdPreviewSurface, inputs: baseColor = texture(srgb).rgb, roughness = texture(lin).r, normal = NormalFromPrim? Standard:\n```\nMaterial\n outputs:surface = UsdPreviewSurface(\n baseColor=Texture(file=color).rgb,\n roughness=Texture(file=rough).r,\n normal=Normal( Texture(file=normal).rgb - 0.5 ?...\n```\nThe normal attribute of UsdPreviewSurface expects tangent-space RGB (0..1). UsdUVTexture outputs the sampled value directly; I'll just connect Texture(node).rgb \u2192 previewSurface.normal. In the shading API, a texture file node with a \"normal\" color space? UsdUVTexture has no color space attribute; the preview surface's normal input expects a 0-1 normal map. UsdShade's texture node has \"source_texture\" and a \"color_space\" parameter (srgb/linear/raw). I'll set color_space=\"raw\" for the normal/roughness, and \"srgb\" for the color.\n\nI'll create the material with UsdShade APIs:\n```python\nmat = UsdShade.Material.Define(stage, \"Tray/Tray_Mat\")\nmat.CreateSurfaceOutput()\nps = mat.CreateUsdPreviewSurface()\ntex_c = mat.CreateTexture(\"textures/tray_color.png\", \"srgb\")\ntex_r = mat.CreateTexture(\"textures/tray_rough.png\", \"raw\") # hmm \"linear\"?\ntex_n = mat.CreateTexture(\"textures/tray_normal.png\", \"raw\")\nps.ConnectInput(\"baseColor\", tex_c.ConnectOutput(\"rgb\"))\nps.ConnectInput(\"roughness\", tex_r.ConnectOutput(\"r\"))\nps.ConnectInput(\"normal\", tex_n.ConnectOutput(\"rgb\"))\nps.CreateInput(\"metallic\", 0.0)\nmat.Bind(mesh_prim)\n```\nUsdShadeMaterial.CreateTexture(file, color_space): the signature is CreateTexture(file, color_space=\"auto\")? I'll double-check: `UsdShadeMaterial.CreateTexture(file, color_space='auto', offset=(0,0), size=(1,1), wrap_s='repeat', wrap_t='repeat')`. Yes.\n\nThen `mesh.GetPrim().GetAttribute(\"material:binding\")`? Actually, the binding: `UsdGeom.Mesh` \u2192 `mat.Bind(prim)` sets the relationship on the prim.\n\nPhysics:\n```python\nrb = UsdPhysics.RigidBodyAPI.Apply(root_prim)\ncol = UsdPhysics.MeshCollisionAPI.Apply(mesh_prim)\ncol.CreateApproximationAttr(\"convexHull\")\ncol.CreateCollisionEnabledAttr(True)\nmass = UsdPhysics.MassAPI.Apply(root_prim)\nmass.CreateMassAttr(9.2)\n```\nHmm, RigidBodyAPI: `.Apply(root)`. Also there's a \"kinematicEnabled\" property (default false) \u2014 I'll leave it false. Also the \"disabled\" attribute default false.\n\nStage metadata:\n```python\nstage = Usd.Stage.CreateNew(\"/output/asset.usd\")\nstage.SetDefaultPrim(root)\nstage.GetMetadata()...\n```\nSet upAxis: `stage.GetRootLayer().specifier`... the stage metadata is on the root layer:\n```python\nfrom pxr import Sdf\nlayer = stage.GetRootLayer()\nlayer.usdUpAxis = \"Z\" # ? SdfLayer.usdUpAxis?\n```\nSdf.Layer has `usdUpAxis` and `usdUnits` properties (in the newer API: `layer.usdUpAxis = \"Z\"`). Or via the API schema: Sdf.Layer has \"usdUpAxis\" (Z or Y) and \"usdUnits\" (centimeters, meters, ...). Yes: `layer.usdUnits = \"meters\"`, `layer.usdUpAxis = \"Z\"`.\n\nAlso, metersPerUnit = 1 comes from usdUnits=\"meters.\"\n\nI'll set `stage.SetDefaultPrim(root)`.\n\nNaming: root \"Tray\"; mesh \"Tray_Mesh\"? Hmm, \"NP\" naming rules \u2014 avoid underscores? Maybe \"NP.008: prim names must not contain underscores\"?? I really don't know. Common simready guidance: alphanumeric names. Let me use simple names: \"Tray,\" \"TrayMesh,\" \"TrayMat.\" Hmm. If NP requires a specific format, the report will tell me. I'll use \"Tray,\" \"TrayMesh,\" \"TrayMaterial.\"\n\nOK, another thought on \"SR.002\"/\"SR.004\": let me try to recall the SimReady spec better. I think the SimReady Foundation spec (from the public repo, \"specs/foundation/spec.md\") had a \"SimReady Root (SR)\" section:\n- SR.001: \"The asset root must be a single prim\" (maybe)\n- SR.002: \"The asset root must be an Xform\"\n- SR.003: \"The asset root must not have any transform\" (identity)\n- SR.004: \"The asset root must be centered at the origin\"??\n\nAnd UN (Units):\n- UN.006: \"metersPerUnit = 1\"\n- UN.007: \"up axis = +Z\"\n\nAnd SL (Stage/Scene Layout?):\n- SL.001: \"the stage has a single root layer / no sublayers\"\n- SL.002: \"no references/payloads/compositions from outside the asset\"\n- SL.003: \"the stage opens with the default prim set\"\n- SL.MAT.001: \"all materials are in the same stage / material prims are descendants of the root\"\n- SL.QCODE.001: ??? Maybe \"the stage declares a 'simready' metadata\"\n- SL.TIME.001: \"no animated attributes (time samples)\"\n\nVG (Visual Geometry):\n- VG.002 (usd family): \"the mesh has no non-finite values\"?\n- VG.008: \"the mesh has normals\"\n- VG.014: \"the mesh is manifold / no open boundaries\"?\n- VG.025: \"the mesh has no duplicate points\"?\n- VG.027/028/029: \"no degenerate triangles,\" \"faces are CCW,\" \"normals consistent with faces\"?\n- VG.MESH.001: \"the mesh is triangulated\"? Or \"point count < N\"?\n\nVM (Visual Material):\n- VM.MAT.001: \"the material is a UsdPreviewSurface (or one of the predefined)\"\n- VM.BIND.001: \"the material is properly bound (scope is inherited/direct, no unbound prims with the material attribute)\"\n- VM.PS.001: \"the material uses a single surface output\"\n- VM.PS.002: \"the surface shader is UsdPreviewSurface\"\n- VM.TEX.001: \"textures are 2D, \u22644096, a supported format (png/jpeg)\"\n- VM.TEX.003: \"texture paths resolve within the asset\"\n\nNP (Named Prim):\n- NP.002: \"no prim names with spaces\"?\n- NP.003: \"unique names\"?\n- NP.004: \"names start with a letter\"?\n- NP.005: \"no reserved names ('material', 'primvars'...)\"?\n- NP.007/008: ...\n\nRB (Rigid Body):\n- RB.001: \"the rigid body (root) has a collider\"?\n- RB.003/005/009: \"the rigid body has a mass,\" \"no articulation,\" \"the rigid body is enabled\"?\n- RB.007: \"mass > 0\"?\n- RB.010: \"density or mass is consistent\"?\n- RB.COL.001: \"the collision shape exists\"\n- RB.COL.002: \"the approximation is valid\"\n- RB.COL.003: \"the collision shape is a child of the rigid body\"\n- RB.COL.004 (usd): \"the collision is enabled\"\n\nHI:\n- HI.001: \"single root\"\n- HI.003/004: \"no nested roots / hierarchy depth \u2264 N\"?\n- HI.010 (simready): \"hierarchy names are unique\"?\n\nAA (usd family):\n- AA.001: \"the asset is Z-up\"?\n- AA.002: \"the asset is centered at the origin\"?\n\nI'm fairly confident in the overall picture. Let me build it. I'll also add a mass and a center-of-mass override? I'll keep just mass.\n\nOK, let me write the script. Also, per the contract, the build script goes in /tmp (\"put the authoring script in /tmp\").\n\nAfter building, I'll run a sanity check: load the stage, print the bounds, count prims, check the bounding box centering. Then validate (call 1/5), read the report, fix, validate (call 2), etc.\n\nLet me also prepare for the possibility that \"upAxis\" must remain \"Y\" and the asset is authored Y-up... I'll trust Z-up for SimReady robotics. The contract says \"canonical meters XYZ\" with Z=0.302 as the height \u2192 Z is up. And the dimensions are given in XYZ with Z as the height \u2192 a Z-up canonical frame. Good, Z-up confirmed by the dimension order (the smallest of the three dimensions is Z, and it's the height).\n\nLet me write the code.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8555f57018a7ec04", + "function_name": "write", + "arguments": { + "path": "/tmp/build_asset.py", + "content": "#!/usr/bin/env python3\n\"\"\"Reconstruct the wooden tray as /output/asset.usd (SimReady Robotics-Prop).\"\"\"\nimport numpy as np\nfrom PIL import Image, ImageDraw\nfrom pxr import Usd, UsdGeom, UsdPhysics, UsdShade, Gf, Sdf, Vt, Tf\n\nOUT_USD = \"/output/asset.usd\"\nTEX_DIR = \"/output/textures\"\n\nL, W, H = 0.6357795000076294, 0.5118491649627686, 0.30237919092178345\nT = 0.015 # wall / panel thickness\n\nimport os\nos.makedirs(TEX_DIR, exist_ok=True)\n\n# ---------------------------------------------------------------- textures\nS = 2048\nrng = np.random.default_rng(7)\n\ndef value_noise(x, y, period, octaves=4, rng=None):\n \"\"\"1-D-ish horizontal value noise along x, mild variation along y.\"\"\"\n n = np.zeros_like(x, dtype=float)\n amp = 1.0\n tot = 0.0\n for o in range(octaves):\n p = period * (2 ** o)\n xi = x / p\n yi = y / (p * 3.0)\n xi0 = np.floor(xi).astype(int)\n yi0 = np.floor(yi).astype(int)\n fx = xi - xi0\n fy = yi - yi0\n g = rng.random((xi0.max() - xi0.min() + 3, yi0.max() - yi0.min() + 3)) if o == 0 else None\n # cheap hash\n def h(i, j):\n v = np.sin(i * 127.1 + j * 311.7 + o * 74.7) * 43758.5453\n return v - np.floor(v)\n c00 = h(xi0, yi0); c10 = h(xi0 + 1, yi0)\n c01 = h(xi0, yi0 + 1); c11 = h(xi0 + 1, yi0 + 1)\n s = c00 * (1 - fx) * (1 - fy) + c10 * fx * (1 - fy) + c01 * (1 - fx) * fy + c11 * fx * fy\n n += amp * (2 * s - 1)\n tot += amp\n amp *= 0.5\n return n / tot\n\nxs = np.arange(S)\nys = np.arange(S)\n\n# --- white painted wood (u in [0, 0.5))\nbase_w = np.array([0.945, 0.937, 0.918])\n# --- natural wood (u in [0.5, 0.95))\nbase_n = np.array([0.913, 0.839, 0.651])\n\ncol = np.zeros((S, S, 3))\nrough = np.full((S, S, 3), 0.6, dtype=float)\n# white region\nu_w = (xs[None, :] / S) < 0.5\nstreak_w = value_noise(np.outer(1.0, xs[None, :]), np.outer(ys[:, None], 1.0), 260.0)\nw = base_w + 0.028 * streak_w[:, :, None] + 0.010 * value_noise(np.outer(1.0, xs[None, :]), np.outer(ys[:, None], 1.0), 5200.0)[:, :, None]\nw = np.clip(w, 0, 1)\n# subtle center seam line on white panels\nseam = np.abs(xs[None, :] - 0.25 * S) < 3.0\nw[:, seam, :] = w[:, seam, :] * 0.93\ncol[u_w] = w[u_w]\nrough[u_w] = 0.48\n\n# natural region\nu_n = (xs[None, :] >= 0.5) & (xs[None, :] < 0.95)\nnn = (xs[None, :] - 0.5 * S) / (0.45 * S) # local 0..1\nstreak_n = value_noise(nn[:, :] * 40.0, np.outer(ys[:, None] / S * 4.0, 1.0), 180.0, 5)\nbright = 0.07 * streak_n\nnk = np.zeros_like(nn)\n# a few faint knots\nfor _ in range(26):\n cx = rng.random() * S * 0.45 + 0.5 * S\n cy = rng.random() * S\n rx = rng.integers(6, 16); ry = rng.integers(20, 60)\n yy, xx = np.ogrid[:S, :S]\n d = ((xx - cx) / rx) ** 2 + ((yy - cy) / ry) ** 2\n nk += (d < 1).astype(float) * rng.uniform(0.03, 0.07)\nn = base_n + bright[:, :, None] - 0.10 * nk[:, :, None]\n# vertical light/dark broad variation along the planks\nbroad = 0.02 * value_noise(nn[:, :] * 8.0, np.outer(ys[:, None] / S * 2.0, 1.0), 400.0)\nn = n + broad[:, :, None]\ncol[u_n] = np.clip(n, 0, 1)[u_n]\nrough[u_n] = 0.75\n\n# edge (plywood cross-section) region u in [0.95, 1.0)\nu_e = xs[None, :] >= 0.95 * S\nee = (xs[None, :] - 0.95 * S) / (0.05 * S)\nstripe = (ee * 46.0) % 1.0 < 0.5\nlayer_c1 = np.array([0.83, 0.72, 0.50]); layer_c2 = np.array([0.62, 0.51, 0.34])\ne = np.where(stripe[:, :, None], layer_c2[None, None, :], layer_c1[None, None, :])\ne = e * (1.0 + 0.05 * value_noise(np.outer(1.0, ee[:, :] * 46.0), np.outer(ys[:, None] / S * 8.0, 1.0), 300.0)[:, :, None])\ncol[u_e] = np.clip(e, 0, 1)[u_e]\nrough[u_e] = 0.85\n\n# normal map: derive subtle grain bumps from luminance\nlum = 0.299 * col[:, :, 0] + 0.587 * col[:, :, 1] + 0.114 * col[:, :, 2]\ngx = np.gradient(lum, axis=1)\ngy = np.gradient(lum, axis=0)\nk = 3.0\nnrm = np.stack([(-gx * k + 0.5), (-gy * k + 0.5), 1.0 - np.abs(gx * k) - np.abs(gy * k)], axis=-1)\nnrm = nrm / np.linalg.norm(nrm, axis=-1, keepdims=True)\nnrm_u8 = (nrm * 0.5 + 0.5) * 255\n\nImage.fromarray(np.clip(col * 255, 0, 255).astype(np.uint8)).save(f\"{TEX_DIR}/tray_color.png\")\nImage.fromarray(np.clip(rough[:, :, 0] * 255, 0, 255).astype(np.uint8)).save(f\"{TEX_DIR}/tray_rough.png\")\nImage.fromarray(nrm_u8.astype(np.uint8)).save(f\"{TEX_DIR}/tray_normal.png\")\nprint(\"textures written\")\n\n# ---------------------------------------------------------------- geometry\nx0, x1 = -L / 2, L / 2\ny0, y1 = -W / 2, W / 2\nz0, z1 = -H / 2, H / 2\nzi = z0 + T # interior floor level\n\npoints = []\ntris = []\nuvs = []\nnormals = []\n\ndef add_face(c0, c1, c2, c3, uv0, uv1, uv2, uv3, nrm_expect):\n \"\"\"c0,c1,c2,c3 in CCW order as seen from OUTSIDE (asserted), uv in same order.\"\"\"\n p0 = np.array(c0, float); p1 = np.array(c1, float)\n p2 = np.array(c2, float); p3 = np.array(c3, float)\n n = np.cross(p1 - p0, p3 - p0)\n n = n / (np.linalg.norm(n) + 1e-12)\n if np.dot(n, nrm_expect) < 0:\n p1, p3 = p3, p1 # flip ordering\n uv1, uv3 = uv3, uv1\n n = -n\n base = len(points)\n for p, uv in ((p0, uv0), (p1, uv1), (p2, uv2), (p3, uv3)):\n points.append(p); uvs.append(uv); normals.append(n)\n tris.append([base, base + 1, base + 2])\n tris.append([base, base + 2, base + 3])\n\nZW = (0.0, 0.5) # white wood region (u 0..0.5)\nZN = (0.5, 0.95) # natural wood region\nZE = (0.95, 1.0) # plywood edge region\n\ndef uvz(reg, a, b, sa, sb):\n lo, hi = reg\n return (lo + (hi - lo) * a, sb * b)\n\n# bottom outer (hidden): wood, grain along X\nadd_face((x0, y1, z0), (x1, y1, z0), (x1, y0, z0), (x0, y0, z0),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (0, 0, -1))\n# front outer (y0), white, grain vertical: u along x, v along z\nadd_face((x0, y0, z0), (x1, y0, z0), (x1, y0, z1), (x0, y0, z1),\n (ZW[0], 0.0), (ZW[1], 0.0), (ZW[1], 1.0), (ZW[0], 1.0), (0, -1, 0))\n# back outer (y1), white\nadd_face((x1, y1, z0), (x0, y1, z0), (x0, y1, z1), (x1, y1, z1),\n (ZW[0], 0.0), (ZW[1], 0.0), (ZW[1], 1.0), (ZW[0], 1.0), (0, 1, 0))\n# left outer (x0), white, u along y\nadd_face((x0, y0, z0), (x0, y1, z0), (x0, y1, z1), (x0, y0, z1),\n (ZW[0], 0.0), (ZW[1], 0.0), (ZW[1], 1.0), (ZW[0], 1.0), (-1, 0, 0))\n# right outer (x1), white\nadd_face((x1, y1, z0), (x1, y0, z0), (x1, y0, z1), (x1, y1, z1),\n (ZW[0], 0.0), (ZW[1], 0.0), (ZW[1], 1.0), (ZW[0], 1.0), (1, 0, 0))\n# rims (top edges, normal +Z): u across wall thickness, v along length\nadd_face((x0, y0, z1), (x1, y0, z1), (x1, y0 + T, z1), (x0, y0 + T, z1),\n (ZE[0], 0.0), (ZE[0], 1.0), (ZE[1], 1.0), (ZE[1], 0.0), (0, 0, 1))\nadd_face((x0, y1, z1), (x1, y1, z1), (x1, y1 - T, z1), (x0, y1 - T, z1),\n (ZE[0], 0.0), (ZE[0], 1.0), (ZE[1], 1.0), (ZE[1], 0.0), (0, 0, 1))\nadd_face((x0, y0 + T, z1), (x0 + T, y0 + T, z1), (x0 + T, y1 - T, z1), (x0, y1 - T, z1),\n (ZE[0], 0.0), (ZE[0], 1.0), (ZE[1], 1.0), (ZE[1], 0.0), (0, 0, 1))\nadd_face((x1 - T, y1 - T, z1), (x1, y1 - T, z1), (x1, y0 + T, z1), (x1 - T, y0 + T, z1),\n (ZE[0], 0.0), (ZE[0], 1.0), (ZE[1], 1.0), (ZE[1], 0.0), (0, 0, 1))\n# inner walls, wood, grain vertical: u along width, v along z\nxi0, xi1 = x0 + T, x1 - T\nyi0, yi1 = y0 + T, y1 - T\nadd_face((xi0, yi0, zi), (xi1, yi0, zi), (xi1, yi0, z1), (xi0, yi0, z1),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (0, -1, 0))\nadd_face((xi1, yi1, zi), (xi0, yi1, zi), (xi0, yi1, z1), (xi1, yi1, z1),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (0, 1, 0))\nadd_face((xi0, yi1, zi), (xi0, yi0, zi), (xi0, yi0, z1), (xi0, yi1, z1),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (-1, 0, 0))\nadd_face((xi1, yi0, zi), (xi1, yi1, zi), (xi1, yi1, z1), (xi1, yi0, z1),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (1, 0, 0))\n# interior floor, wood, grain along X: u along y, v along x\nadd_face((xi0, yi1, zi), (xi1, yi1, zi), (xi1, yi0, zi), (xi0, yi0, zi),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (0, 0, 1))\n\npts = [Gf.Vec3f(*p) for p in points]\nidx = [i for t in tris for i in t]\nuvlist = [Gf.Vec2f(*u) for u in uvs]\nnrmv = [Gf.Vec3f(*n) for n in normals]\n\n# ---------------------------------------------------------------- stage\nUsd.Stage.DeleteStage(Usd.Stage.GetStageAtPath(OUT_USD)) if Usd.Stage.GetStageAtPath(OUT_USD) else None\nstage = Usd.Stage.CreateNew(OUT_USD)\nroot = UsdGeom.Xform.Define(stage, \"/Tray\")\nstage.SetDefaultPrim(root)\nlayer = stage.GetRootLayer()\nlayer.usdUnits = \"meters\"\nlayer.usdUpAxis = \"Z\"\n\nmesh = UsdGeom.Mesh.Define(stage, \"/Tray/TrayMesh\")\nmesh.CreatePointsAttr(pts)\nmesh.CreateFaceVertexIndicesAttr(idx)\nmesh.CreateFaceVertexNormalsAttr(nrmv)\nmesh.CreatePrimvar(\"uv\", Sdf.Tokens.float2, Vt.Vec2fArrayHandle(uvlist), UsdGeom.Tokens.faceVertex)\nmesh.AddNormalsRel? = None # placeholder, removed below\n" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8555f57018a7ec04", + "content": "Successfully wrote to /tmp/build_asset.py", + "extra": { + "tool_name": "write", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 13132, + "completion_tokens": 18791, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 31923 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "736b5b41", + "pi_parent_id": "f704fc10", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R6WvXJ12btiFPjKD487RyJKv", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 11, + "timestamp": "2026-10-21T11:53:32.472Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Oops, I left a broken line in the file (`mesh.AddNormalsRel? = None`). Let me fix it and properly complete the file including the materials and physics.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8146b9ff177b4a6b", + "function_name": "edit", + "arguments": { + "path": "/tmp/build_asset.py", + "edits": [ + { + "oldText": "mesh.CreatePrimvar(\"uv\", Sdf.Tokens.float2, Vt.Vec2fArrayHandle(uvlist), UsdGeom.Tokens.faceVertex)\nmesh.AddNormalsRel? = None # placeholder, removed below\n", + "newText": "mesh.CreatePrimvar(\"uv\", Sdf.Tokens.float2, Vt.Vec2fArrayHandle(uvlist), UsdGeom.Tokens.faceVertex)\n\n# ---------------------------------------------------------------- material\nmat = UsdShade.Material.Define(stage, \"/Tray/TrayMaterial\")\nmat.CreateSurfaceOutput()\nps = mat.CreateUsdPreviewSurface()\ntex_c = mat.CreateTexture(\"textures/tray_color.png\", \"srgb\")\ntex_r = mat.CreateTexture(\"textures/tray_rough.png\", \"raw\")\ntex_n = mat.CreateTexture(\"textures/tray_normal.png\", \"raw\")\nps.ConnectInput(\"baseColor\", tex_c.ConnectOutput(\"rgb\"))\nps.ConnectInput(\"roughness\", tex_r.ConnectOutput(\"r\"))\nps.ConnectInput(\"normal\", tex_n.ConnectOutput(\"rgb\"))\nps.CreateInput(\"metallic\", 0.0)\nps.CreateInput(\"clearcoat\", 0.0)\nmat.Bind(mesh)\n\n# ---------------------------------------------------------------- physics\nUsdPhysics.RigidBodyAPI.Apply(root)\ncol = UsdPhysics.MeshCollisionAPI.Apply(mesh)\ncol.CreateApproximationAttr(\"convexHull\")\ncol.CreateCollisionEnabledAttr(True)\nmass = UsdPhysics.MassAPI.Apply(root)\nmass.CreateMassAttr(9.2)\n\nstage.Export(OUT_USD)\nprint(\"exported\", OUT_USD)\n\n# sanity\nst = Usd.Stage.Open(OUT_USD)\nbb = UsdGeom.BBoxCache(Usd.TimeCode.Default()).ComputeWorldBound(root)\nprint(\"bbox range:\", bb.GetRange())\nprint(\"prims:\", [p for p in st.Traverse()])\n" + } + ] + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8146b9ff177b4a6b", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 31946, + "completion_tokens": 511, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 32457 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "fb41fd87", + "pi_parent_id": "da215915", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RtumEe5wG3TRLywKKPSYXycm", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 12, + "timestamp": "2026-10-21T11:57:20.213Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "The texture-generation part had broadcasting bugs. Let me rewrite the script cleanly from scratch:", + "reasoning_content": "Wait a moment, there's an issue with the texture generation code. In `value_noise`, I'm passing arguments in a weird way:\n\n```python\nstreak_w = value_noise(np.outer(1.0, xs[None, :]), np.outer(ys[:, None], 1.0), 260.0)\n```\n\n`np.outer(1.0, xs[None, :])` \u2014 xs[None,:] is shape (1,S); np.outer flattens both \u2192 (S,). So x is a 1D array of shape (S,). `np.outer(ys[:, None], 1.0)` \u2192 (S,). So value_noise(x: (S,), y: (S,), ...) \u2014 x and y are the same length S, so element-wise operations work. Result is (S,). But then I do `streak_w[:, :, None]` \u2014 no wait: `0.028 * streak_w[:, :, None]` \u2014 streak_w is (S,), so `[:, :, None]` on a 1D array will fail! Indexing a 1D array with 2 slices + None: `streak_w[:, :, None]` \u2192 too many indices error.\n\nLet me rewrite the texture generation part more carefully and cleanly. I'll rewrite the whole script from scratch to avoid bugs. Simpler approach: build a 2D texture directly with a function f(x_tex, y_tex) using vectorized 2D grids.\n\nLet me define a 2D grid: X = xs[None, :] * 1.0 \u2192 shape (1, S) broadcast; Y = ys[:, None] \u2192 (S, 1). All operations broadcast to (S, S).\n\nValue noise 2D (hash-based, bilinear):\n\n```python\ndef vnoise(X, Y, period, seed=0):\n fx = X / period; fy = Y / (period * 2.0)\n fx0 = np.floor(fx); fy0 = np.floor(fy)\n fx = fx - fx0; fy = fy - fy0\n def h(i, j):\n v = np.sin(i * 127.1 + j * 311.7 + seed * 74.7) * 43758.5453\n return v - np.floor(v)\n c00 = h(fx0, fy0); c10 = h(fx0 + 1, fy0)\n c01 = h(fx0, fy0 + 1); c11 = h(fx0 + 1, fy0 + 1)\n s = (c00 * (1 - fx) * (1 - fy) + c10 * fx * (1 - fy)\n + c01 * (1 - fx) * fy + c11 * fx * fy)\n return 2 * s - 1\n```\n\nThis is fbm-like via summing octaves. No problem.\n\nWhite region (u\u2208[0,0.5)): the grain is vertical lines in the texture (streaks varying with X, period ~ 2048*0.5/12 \u2248 85 px): streak = vnoise(X, Y, period=2048*0.5/12). Plus fine noise.\n\nRegion split: build the full texture as the natural wood base, then overwrite the white part and the edge part.\n\nLet me cleanly rewrite the entire script file.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a8e4b15a11aa8eaa", + "function_name": "write", + "arguments": { + "content": "#!/usr/bin/env python3\n\"\"\"Reconstruct the wooden tray as /output/asset.usd (SimReady Robotics-Prop).\"\"\"\nimport os\nimport numpy as np\nfrom PIL import Image\nfrom pxr import Usd, UsdGeom, UsdPhysics, UsdShade, Gf, Sdf, Vt\n\nOUT_USD = \"/output/asset.usd\"\nTEX_DIR = \"/output/textures\"\nos.makedirs(TEX_DIR, exist_ok=True)\n\n# =================================================================== textures\nS = 2048\nX = np.arange(S, dtype=float)[None, :] # (1,S)\nY = np.arange(S, dtype=float)[:, None] # (S,1)\n\ndef vnoise(Xv, Yv, period, seed=0):\n \"\"\"hash-value noise, period in px along X, stretched along Y; range ~[-1,1]\"\"\"\n fx = Xv / period\n fy = Yv / (period * 2.5)\n fx0 = np.floor(fx)\n fy0 = np.floor(fy)\n fx = fx - fx0\n fy = fy - fy0\n\n def h(i, j):\n v = np.sin(i * 127.1 + j * 311.7 + seed * 74.7) * 43758.5453\n return v - np.floor(v)\n\n s = (h(fx0, fy0) * (1 - fx) * (1 - fy) + h(fx0 + 1, fy0) * fx * (1 - fy)\n + h(fx0, fy0 + 1) * (1 - fx) * fy + h(fx0 + 1, fy0 + 1) * fx * fy)\n return 2.0 * s - 1.0\n\ndef fbm(Xv, Yv, period, seed=0, octaves=4):\n n = np.zeros_like(Xv)\n amp, tot = 1.0, 0.0\n for o in range(octaves):\n n += amp * vnoise(Xv, Yv, period * (2 ** o), seed + o * 13)\n tot += amp\n amp *= 0.5\n return n / tot\n\n# white painted wood occupies u in [0, 0.5); grain lines vertical in image\nhw = 0.5 * S\nstreak_w = fbm(X[:, :hw], Y, hw / 11.0, seed=3)\nfine_w = fbm(X[:, :hw], Y, 34.0, seed=9, octaves=2)\ncol_w = (np.array([0.945, 0.937, 0.918])\n + 0.030 * streak_w[..., None]\n + 0.010 * fine_w[..., None])\n# faint vertical seam at middle of the white panels\nseam = np.abs(X[:, :hw] - 0.25 * S) < 4.0\ncol_w[:, seam, :] *= 0.94\ncol_w = np.clip(col_w, 0, 1)\n\n# natural wood occupies u in [0.5, 0.95)\nln = 0.45 * S\nnn = X[:, hw:int(0.95 * S)] / ln\nstreak_n = fbm(nn[:, :] * (ln / 14.0), Y, ln / 14.0, seed=5)\nfine_n = fbm(nn[:, :] * (ln / 40.0), Y, ln / 40.0, seed=11, octaves=2)\ncol_n = (np.array([0.913, 0.839, 0.651])\n + 0.060 * streak_n[..., None]\n + 0.025 * fine_n[..., None])\n# faint knots\nrng = np.random.default_rng(7)\nyy, xx = np.mgrid[0:S, 0:S]\nfor _ in range(30):\n cx = rng.random() * 0.45 * S + hw\n cy = rng.random() * S\n rx = rng.integers(5, 14)\n ry = rng.integers(18, 60)\n d = ((xx - cx) / rx) ** 2 + ((yy - cy) / ry) ** 2\n col_n -= (np.clip(1 - d, 0, 1) ** 2 * rng.uniform(0.05, 0.10))[..., None]\n# broad light/dark plank variation (varying along the local horizontal)\nbroad = 0.020 * vnoise(nn[:, :] * (ln / 6.0), Y, ln / 6.0, seed=21)\ncol_n = np.clip(col_n + broad[..., None], 0, 1)\n\n# plywood edge strip u in [0.95, 1.0): layers run across the strip (vary along X)\nle = 0.05 * S\nee = X[:, int(0.95 * S):] / le\nstripe = ((ee * 44.0) % 1.0) < 0.5\nc1 = np.array([0.85, 0.74, 0.52])\nc2 = np.array([0.60, 0.49, 0.32])\ncol_e = np.where(stripe[..., None], c2[None, None, :], c1[None, None, :])\ncol_e *= 1.0 + 0.05 * fbm(ee[:, :] * le / 6.0, Y, le / 6.0, seed=17)[..., None]\ncol_e = np.clip(col_e, 0, 1)\n\ncol = np.zeros((S, S, 3))\ncol[:, :hw] = col_w\ncol[:, hw:int(0.95 * S)] = col_n\ncol[:, int(0.95 * S):] = col_e\n\n# roughness: white 0.48, wood 0.75, edge 0.85 (+ grain modulation)\nrough = np.empty((S, S))\nrough[:, :hw] = 0.48 + 0.03 * fbm(X[:, :hw], Y, hw / 11.0, seed=3)\nrough[:, hw:int(0.95 * S)] = 0.75 + 0.04 * fbm(nn[:, :] * (ln / 14.0), Y, ln / 14.0, seed=5)\nrough[:, int(0.95 * S):] = 0.85\nrough = np.clip(rough, 0, 1)\n\n# subtle normal map from grain\nlum = 0.299 * col[..., 0] + 0.587 * col[..., 1] + 0.114 * col[..., 2]\ngx = np.gradient(lum, axis=1)\ngy = np.gradient(lum, axis=0)\nk = 2.5\nnrm = np.stack([0.5 - gx * k, 0.5 - gy * k, 1.0], axis=-1)\nnrm /= np.linalg.norm(nrm, axis=-1, keepdims=True)\nnrm_u8 = np.clip((nrm * 0.5 + 0.5) * 255, 0, 255).astype(np.uint8)\n\nImage.fromarray(np.clip(col * 255, 0, 255).astype(np.uint8)).save(f\"{TEX_DIR}/tray_color.png\")\nImage.fromarray(np.clip(rough * 255, 0, 255).astype(np.uint8)).save(f\"{TEX_DIR}/tray_rough.png\")\nImage.fromarray(nrm_u8).save(f\"{TEX_DIR}/tray_normal.png\")\nprint(\"textures written\")\n\n# =================================================================== geometry\nL, W, H = 0.6357795000076294, 0.5118491649627686, 0.30237919092178345\nT = 0.015\nx0, x1 = -L / 2, L / 2\ny0, y1 = -W / 2, W / 2\nz0, z1 = -H / 2, H / 2\nzi = z0 + T\n\npoints, tris, uvs, normals = [], [], [], []\n\ndef add_face(c0, c1, c2, c3, uv0, uv1, uv2, uv3, nrm_expect):\n p0 = np.array(c0, float)\n p1 = np.array(c1, float)\n p2 = np.array(c2, float)\n p3 = np.array(c3, float)\n n = np.cross(p1 - p0, p3 - p0)\n n /= (np.linalg.norm(n) + 1e-12)\n if np.dot(n, np.array(nrm_expect)) < 0:\n p1, p3 = p3, p1\n uv1, uv3 = uv3, uv1\n n = -n\n b = len(points)\n for p, uv in ((p0, uv0), (p1, uv1), (p2, uv2), (p3, uv3)):\n points.append(p)\n uvs.append(uv)\n normals.append(n)\n tris.append([b, b + 1, b + 2])\n tris.append([b, b + 2, b + 3])\n\nZW = (0.0, 0.5) # white wood\nZN = (0.5, 0.95) # natural wood\nZE = (0.95, 1.0) # plywood edge\nxi0, xi1 = x0 + T, x1 - T\nyi0, yi1 = y0 + T, y1 - T\n\n# bottom outer (hidden) - wood, grain along X\nadd_face((x0, y1, z0), (x1, y1, z0), (x1, y0, z0), (x0, y0, z0),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (0, 0, -1))\n# outer walls - white, grain vertical\nadd_face((x0, y0, z0), (x1, y0, z0), (x1, y0, z1), (x0, y0, z1),\n (ZW[0], 0.0), (ZW[1], 0.0), (ZW[1], 1.0), (ZW[0], 1.0), (0, -1, 0))\nadd_face((x1, y1, z0), (x0, y1, z0), (x0, y1, z1), (x1, y1, z1),\n (ZW[0], 0.0), (ZW[1], 0.0), (ZW[1], 1.0), (ZW[0], 1.0), (0, 1, 0))\nadd_face((x0, y0, z0), (x0, y1, z0), (x0, y1, z1), (x0, y0, z1),\n (ZW[0], 0.0), (ZW[1], 0.0), (ZW[1], 1.0), (ZW[0], 1.0), (-1, 0, 0))\nadd_face((x1, y1, z0), (x1, y0, z0), (x1, y0, z1), (x1, y1, z1),\n (ZW[0], 0.0), (ZW[1], 0.0), (ZW[1], 1.0), (ZW[0], 1.0), (1, 0, 0))\n# rims - plywood edge: u across thickness, v along length\nadd_face((x0, y0, z1), (x1, y0, z1), (x1, y0 + T, z1), (x0, y0 + T, z1),\n (ZE[0], 0.0), (ZE[0], 1.0), (ZE[1], 1.0), (ZE[1], 0.0), (0, 0, 1))\nadd_face((x0, y1, z1), (x1, y1, z1), (x1, y1 - T, z1), (x0, y1 - T, z1),\n (ZE[0], 0.0), (ZE[0], 1.0), (ZE[1], 1.0), (ZE[1], 0.0), (0, 0, 1))\nadd_face((x0, y0 + T, z1), (x0 + T, y0 + T, z1), (x0 + T, y1 - T, z1), (x0, y1 - T, z1),\n (ZE[0], 0.0), (ZE[0], 1.0), (ZE[1], 1.0), (ZE[1], 0.0), (0, 0, 1))\nadd_face((x1 - T, y1 - T, z1), (x1, y1 - T, z1), (x1, y0 + T, z1), (x1 - T, y0 + T, z1),\n (ZE[0], 0.0), (ZE[0], 1.0), (ZE[1], 1.0), (ZE[1], 0.0), (0, 0, 1))\n# inner walls - wood, grain vertical\nadd_face((xi0, yi0, zi), (xi1, yi0, zi), (xi1, yi0, z1), (xi0, yi0, z1),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (0, -1, 0))\nadd_face((xi1, yi1, zi), (xi0, yi1, zi), (xi0, yi1, z1), (xi1, yi1, z1),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (0, 1, 0))\nadd_face((xi0, yi1, zi), (xi0, yi0, zi), (xi0, yi0, z1), (xi0, yi1, z1),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (-1, 0, 0))\nadd_face((xi1, yi0, zi), (xi1, yi1, zi), (xi1, yi1, z1), (xi1, yi0, z1),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (1, 0, 0))\n# interior floor - wood, grain along X (u along y, v along x)\nadd_face((xi0, yi1, zi), (xi1, yi1, zi), (xi1, yi0, zi), (xi0, yi0, zi),\n (ZN[0], 0.0), (ZN[1], 0.0), (ZN[1], 1.0), (ZN[0], 1.0), (0, 0, 1))\n\npts = [Gf.Vec3f(*p) for p in points]\nidx = [i for t in tris for i in t]\nuvlist = [Gf.Vec2f(*u) for u in uvs]\nnrmv = [Gf.Vec3f(*n) for n in normals]\n\n# =================================================================== stage\nold = Usd.Stage.GetStageAtPath(OUT_USD)\nif old:\n Usd.Stage.DeleteStage(old)\nstage = Usd.Stage.CreateNew(OUT_USD)\nroot = UsdGeom.Xform.Define(stage, \"/Tray\")\nstage.SetDefaultPrim(root)\nlayer = stage.GetRootLayer()\nlayer.usdUnits = \"meters\"\nlayer.usdUpAxis = \"Z\"\n\nmesh = UsdGeom.Mesh.Define(stage, \"/Tray/TrayMesh\")\nmesh.CreatePointsAttr(pts)\nmesh.CreateFaceVertexIndicesAttr(idx)\nmesh.CreateFaceVertexNormalsAttr(nrmv)\nmesh.CreatePrimvar(\"uv\", Sdf.Tokens.float2, Vt.Vec2fArrayHandle(uvlist),\n UsdGeom.Tokens.faceVertex)\n\n# =================================================================== material\nmat = UsdShade.Material.Define(stage, \"/Tray/TrayMaterial\")\nmat.CreateSurfaceOutput()\nps = mat.CreateUsdPreviewSurface()\ntex_c = mat.CreateTexture(\"textures/tray_color.png\", \"srgb\")\ntex_r = mat.CreateTexture(\"textures/tray_rough.png\", \"raw\")\ntex_n = mat.CreateTexture(\"textures/tray_normal.png\", \"raw\")\nps.ConnectInput(\"baseColor\", tex_c.ConnectOutput(\"rgb\"))\nps.ConnectInput(\"roughness\", tex_r.ConnectOutput(\"r\"))\nps.ConnectInput(\"normal\", tex_n.ConnectOutput(\"rgb\"))\nps.CreateInput(\"metallic\", 0.0)\nmat.Bind(mesh)\n\n# =================================================================== physics\n# Single rigid part: rigidly joined panels -> one rigid body, no joints.\nUsdPhysics.RigidBodyAPI.Apply(root)\ncol = UsdPhysics.MeshCollisionAPI.Apply(mesh)\ncol.CreateApproximationAttr(\"convexHull\")\ncol.CreateCollisionEnabledAttr(True)\nmass = UsdPhysics.MassAPI.Apply(root)\nmass.CreateMassAttr(9.2)\n\nstage.Export(OUT_USD)\nprint(\"exported\", OUT_USD)\n\n# sanity\nst = Usd.Stage.Open(OUT_USD)\nbb = UsdGeom.BBoxCache(Usd.TimeCode.Default()).ComputeWorldBound(root)\nprint(\"bbox min:\", bb.GetRange().GetMin())\nprint(\"bbox max:\", bb.GetRange().GetMax())\nprint(\"bbox extents:\", bb.GetRange().GetMax() - bb.GetRange().GetMin())\nprint(\"prims:\", [str(p.GetPath()) for p in st.Traverse()])\n", + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a8e4b15a11aa8eaa", + "content": "Successfully wrote to /tmp/build_asset.py", + "extra": { + "tool_name": "write", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 32486, + "completion_tokens": 5190, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 37676 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "9a2b0798", + "pi_parent_id": "d3524f48", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RG8X1xSk6b20W34uIktXxBkg", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 13, + "timestamp": "2026-10-21T11:58:12.020Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Wait, there's a bug: the rim (RIM) face \u2014 I mapped `u` to \"across the wall thickness\" and `v` to \"along the length.\" But looking at the UV I passed: for the front rim, the corners are (x0,y0,z1),(x1,y0,z1),(x1,y0+T,z1),(x0,y0+T,z1), and the UVs are (ZE[0],0),(ZE[0],1),(ZE[1],1),(ZE[1],0). That is, at corner (x0,y0) the uv=(ZE[0],0); at (x1,y0) it's (ZE[0],1); at (x1,y0+T) it's (ZE[1],1); at (x0,y0+T) it's (ZE[1],0). So u varies with y (the thickness direction, 0\u21921 across T) and v varies with x (the length). OK, this matches the intent: the stripes in the texture vary in the X direction (u) \u2192 the layer lines run along v = along the length. Correct.\n\nBut wait: the stripe pattern of the edge texture: `stripe = ((ee * 44.0) % 1.0) < 0.5`, where ee = X/(0.05*S) \u2192 44 stripes across the entire 0.05 region \u2192 stripe period in u = 0.05/44 \u2248 0.0011 \u2192 mapped to a physical width of 0.015m \u2192 period \u2248 0.015*0.0011... no wait: the UV u spans the entire 0.05 region across the physical width T=0.015m. 44 stripes over a physical 15mm \u2192 each stripe \u2248 0.34mm. Actual plywood layers are ~1\u20131.5mm thick. In the image, the zigzag pattern on the rim looks like small dense hatching. 44 stripes over 15mm looks fine visually.\n\nAlso `ee = X[:, int(0.95*S):] / le` \u2014 X is (1,S), slicing \u2192 (1, le). `ee[:, :] * le/6.0` inside fbm \u2192 no problem.\n\nOne concern: `fbm(X[:, :hw], Y, hw / 11.0, seed=3)` \u2014 X[:, :hw] is (1,hw), Y is (S,1) \u2192 broadcasts to (S,hw). Good.\n\n`seam = np.abs(X[:, :hw] - 0.25*S) < 4.0` \u2192 (1,hw); `col_w[:, seam, :] *= 0.94` \u2014 boolean indexing with (1,hw) on axis 1 of col_w (S,hw,3): `col_w[:, seam, :]` \u2192 shape (S, k, 3) where k = number of True. In-place multiplication is OK.\n\nNext, the vnoise function: fx = Xv/period, with Xv (1,hw), Yv (S,1) \u2192 broadcasts to (S,hw). All good.\n\nGeometry check: add_face's flip logic \u2014 for the bottom outer face: c0=(x0,y1,z0), c1=(x1,y1,z0), c2=(x1,y0,z0), c3=(x0,y0,z0), expected normal (0,0,-1). p1-p0 = (L,0,0), p3-p0 = (0,-W,0). cross = (0*0-0*(-W), 0*0-L*0, L*(-W)-0) = (0,0,-LW) \u2192 -Z. Dot with (0,0,-1) > 0 \u2192 no flip. Good, the order is CCW as seen from -Z (outside).\n\nWinding of a CCW triangle as seen from outside: the face is (b,b+1,b+2),(b,b+2,b+3) with p0..p3 in CCW order \u2192 the triangle is also CCW. Good.\n\nThe normal is stored per-vertex as the face normal; for a flat face, no problem.\n\nNext, the material: `mat.CreateTexture(\"textures/tray_color.png\", \"srgb\")` \u2014 UsdShadeMaterial.CreateTexture's signature: CreateTexture(file, color_space='auto', offset=(0,0), size=(1,1), wrap_s='repeat', wrap_t='repeat', ...). OK.\n\n`ps.CreateInput(\"metallic\", 0.0)` \u2014 creates an input with default value 0. Good.\n\nPhysics: MeshCollisionAPI \u2014 correct name: `UsdPhysics.MeshCollisionAPI` (schema for the mesh collision API). Yes.\n\nAnother potential issue: `layer.usdUpAxis = \"Z\"` \u2014 Sdf.Layer supports the usdUpAxis attribute in OpenUSD 22.05+? The Python attribute exists as `layer.usdUpAxis`. And `layer.usdUnits = \"meters\"`. Both are valid (usdUnits was added in 23.05? Actually the \"units\" metadata existed in the SDF layer as `units`... hmm). Let me check: Sdf.Layer has properties: `usdUpAxis` (added around 22.05) and `usdUnits` (added in 23.05 as part of the unification of the units specification). If the installed OpenUSD is older, the assignment will fail or be silently ignored... In Python, setting an attribute that doesn't exist on a C++ class raises AttributeError. Let me first check the OpenUSD version.\n\nLet me run the script and see what happens.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8f911ec2dda7e433", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 -c \"import pxr; from pxr import Usd; s=Usd.Stage.CreateInMemory(); print('ok'); import inspect; from pxr import Sdf; l=Sdf.Layer.CreateNew('/tmp/_t.usd'); print([a for a in dir(l) if 'nit' in a.lower() or 'pAxis' in a])\" && python3 /tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8f911ec2dda7e433", + "content": "ok\n['__init__', '__init_subclass__']\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 45, in \n streak_w = fbm(X[:, :hw], Y, hw / 11.0, seed=3)\n ~^^^^^^^^\nTypeError: slice indices must be integers or None or have an __index__ method\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 37699, + "completion_tokens": 1380, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 39079 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "0a708881", + "pi_parent_id": "19f8528a", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RPMAgJ0qhRC81ltjJ83iqQLH", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 14, + "timestamp": "2026-10-21T11:58:33.304Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Two issues:\n1. `hw = 0.5 * S` is a float \u2014 need to make it an int.\n2. The dir() check on Sdf.Layer couldn't find anything with 'unit'/'pAxis' in the name \u2014 this suggests that this build of OpenUSD may not have the `usdUnits`/`usdUpAxis` attributes. Let me properly check which version of OpenUSD this is and which attributes exist on the layer.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9f4e198d91b5e02f", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "hw = int(0.5 * S)", + "oldText": "hw = 0.5 * S" + }, + { + "newText": "ln = int(0.45 * S)\nle = int(0.05 * S)", + "oldText": "ln = 0.45 * S\nle = 0.05 * S" + }, + { + "newText": "nn = X[:, hw:int(0.95 * S)] / ln\n", + "oldText": "nn = X[:, hw:int(0.95 * S)] / ln # local 0..1\n" + } + ], + "path": "/tmp/build_asset.py" + } + }, + { + "tool_call_id": "chatcmpl-tool-9f050103dd4473eb", + "function_name": "bash", + "arguments": { + "command": "python3 - <<'EOF'\nimport pxr, importlib.metadata as im\ntry:\n print(im.version(\"openusd\"))\nexcept Exception as e:\n print(\"no dist\", e)\nfrom pxr import Sdf\nl = Sdf.Layer.CreateNew('/tmp/_t2.usd')\nprint([a for a in dir(l) if 's' in a.lower()][:80])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9f4e198d91b5e02f", + "content": "Could not find edits[1] in /tmp/build_asset.py. The oldText must match exactly including all whitespace and newlines.", + "extra": { + "tool_name": "edit", + "is_error": true + } + }, + { + "source_call_id": "chatcmpl-tool-9f050103dd4473eb", + "content": "no dist No package metadata was found for openusd\n['AddToMutedLayers', 'ClearColorManagementSystem', 'ClearCustomLayerData', 'ClearExpressionVariables', 'ClearFramePrecision', 'ClearFramesPerSecond', 'ClearRelocates', 'ClearSessionOwner', 'ClearStartTimeCode', 'ClearTimeCodesPerSecond', 'ColorManagementSystemKey', 'ComputeAbsolutePath', 'CreateAnonymous', 'DetachedLayerRules', 'EraseTimeSample', 'ExportToString', 'FramePrecisionKey', 'FramesPerSecondKey', 'GetAssetInfo', 'GetAssetName', 'GetBracketingTimeSamples', 'GetBracketingTimeSamplesForPath', 'GetCompositionAssetDependencies', 'GetDefaultPrimAsPath', 'GetDetachedLayerRules', 'GetDisplayName', 'GetDisplayNameFromIdentifier', 'GetExternalAssetDependencies', 'GetExternalReferences', 'GetFileFormatArguments', 'GetLoadedLayers', 'GetMutedLayers', 'GetNumTimeSamplesForPath', 'GetPreviousTimeSampleForPath', 'GetRelationshipAtPath', 'HasColorConfiguration', 'HasColorManagementSystem', 'HasCustomLayerData', 'HasDefaultPrim', 'HasEndTimeCode', 'HasExpressionVariables', 'HasFramePrecision', 'HasFramesPerSecond', 'HasOwnedSubLayers', 'HasOwner', 'HasRelocates', 'HasSessionOwner', 'HasStartTimeCode', 'HasTimeCodesPerSecond', 'ImportFromString', 'IsAnonymousLayerIdentifier', 'IsDetached', 'IsIncludedByDetachedLayerRules', 'IsMuted', 'LayerRelocatesKey', 'ListAllTimeSamples', 'ListTimeSamplesForPath', 'OpenAsAnonymous', 'QueryTimeSample', 'ReloadLayers', 'RemoveFromMutedLayers', 'RemoveInertSceneDescription', 'RemovePropertyIfHasOnlyRequiredFields', 'Save', 'ScheduleRemoveIfInert', 'SessionOwnerKey', 'SetDetachedLayerRules', 'SetMuted', 'SetPermissionToEdit', 'SetPermissionToSave', 'SetTimeSample', 'SplitIdentifier', 'StartFrameKey', 'StartTimeCodeKey', 'StreamsData', 'TimeCodesPerSecondKey', 'TransferContent', 'Traverse', 'UpdateAssetInfo', 'UpdateCompositionAssetDependency']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 39187, + "completion_tokens": 396, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 39583 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "b0239f08", + "pi_parent_id": "b2d2efe2", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RDs3WEq2Xbv6GMfwtfUjHJsc", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 15, + "timestamp": "2026-10-21T11:59:36.268Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "This OpenUSD build lacks `usdUpAxis`/`usdUnits` layer properties, so I'll set upAxis/metersPerUnit via the `#usda` header text post-export. Fixing the script:", + "reasoning_content": "This build's Sdf.Layer doesn't have the usdUpAxis/usdUnits attributes (these are newer OpenUSD features from around 23.08 onward, exposed as `layer.usdUpAxis`). Hmm \u2014 wait, actually `usdUpAxis` and `usdUnits` were added as Python properties of Sdf.Layer in OpenUSD 22.05? I don't see them in the dir listing... Let me check the version of pxr that's installed.\n\nActually, the dir() list is cut off midway (I was only showing entries containing 's'). Let me specifically search for 'pAxis' and 'nits' \u2014 in the previous check I filtered by 'nit' in a.lower() or 'pAxis', but only the __init__ entry came back. So it doesn't exist in this build.\n\nHow do I declare upAxis/units in this version? I can either write raw SDF text with `#usda 1.0 (upAxis = Z) (units = \"meters\")`, or set the asset info. In older OpenUSD, is upAxis specified as a comment in the #usda header: `#usda 1.0 (upAxis = Z)`? No wait \u2014 upAxis is stage metadata, declared in the layer header: `#usda 1.0 ( upAxis = Z )`. Hmm, actually the layer header supports `(defaultPrim = \"x\") (subLayersPath = ...) (metersPerUnit = 1.0) (upAxis = Z) (frameRate = ...)`. Right! The SDF layer header supports the metadata tokens: metersPerUnit, upAxis, defaultPrim, frameRate, customLayerData, timeCodesPerSecond, colorManagementSystem, startFrame, streams.\n\nSo I can export and post-process the .usd file, or better: use Sdf to write the header. The easiest way: after stage.Export, read the file, replace the `#usda 1.0` header with `#usda 1.0 (upAxis = Z) (metersPerUnit = 1.0)`. This is clean and works with any OpenUSD version.\n\nLet me check what the current header looks like, and how metersPerUnit is set. `stage.GetMetadata()` also exists, but... in the old API: `Usd.Stage` has `GetDefaultPrim`, and the units are in the layer's `metersPerUnit` property? Sdf.Layer historically didn't expose metersPerUnit directly; it's in the header comment. Actually, Sdf.Layer does have a `metersPerUnit` property! Let me check: Sdf.Layer has `GetMetersPerUnit`/`SetMetersPerUnit`? Hmm, not in the list above... I don't see it in the dir list. Let me grep for 'eters'.\n\nActually, the simplest and most robust approach: write the #usda header manually. Create the stage with CreateNew, export, then fix the header text with a small sed-like Python step, then re-validate that it parses.\n\nAlso let me check the pxr version: python3 -c \"import pxr; print(pxr.BuildVersionString())\"? Let me try it.\n\nNow, let me fix the script: replace the layer.usdUnits/upAxis lines with a post-export header edit.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9424008fa31da44e", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nimport re\nsrc = open('build_asset.py').read()\nsrc = src.replace(\"hw = 0.5 * S\", \"hw = int(0.5 * S)\")\nsrc = src.replace(\"ln = 0.45 * S\", \"ln = int(0.45 * S)\")\nsrc = src.replace(\"le = 0.05 * S\", \"le = int(0.05 * S)\")\nsrc = src.replace('''layer = stage.GetRootLayer()\nlayer.usdUnits = \"meters\"\nlayer.usdUpAxis = \"Z\"\n\n''', \"\")\nsrc = src.replace('''stage.Export(OUT_USD)\nprint(\"exported\", OUT_USD)\n''', '''stage.Export(OUT_USD)\n\n# this OpenUSD build: set upAxis / units via the #usda layer header\nhdr = open(OUT_USD).read().splitlines()[0]\nassert hdr.startswith(\"#usda\"), hdr\nopen(OUT_USD, \"w\").write(hdr + \" (upAxis = Z) (metersPerUnit = 1.0)\" + open(OUT_USD).read().split(\"\\\\n\", 1)[1])\nprint(\"exported\", OUT_USD)\n''')\nopen('build_asset.py','w').write(src)\nEOF\npython3 /tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9424008fa31da44e", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 45, in \n streak_w = fbm(X[:, :hw], Y, hw / 11.0, seed=3)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/build_asset.py\", line 38, in fbm\n n += amp * vnoise(Xv, Yv, period * (2 ** o), seed + o * 13)\nValueError: non-broadcastable output operand with shape (1,1024) doesn't match the broadcast shape (2048,1024)\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 40095, + "completion_tokens": 1094, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 41189 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "59e6be95", + "pi_parent_id": "1a667d17", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-REgp27j49TtNwY7AjqCWotqQ", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 16, + "timestamp": "2026-10-21T11:59:45.674Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Bug in fbm: `n = np.zeros_like(Xv)` creates shape (1, hw), but the broadcasted result is (S, hw). Fix: initialize n with the broadcast shape: `n = np.zeros(np.broadcast(Xv, Yv).shape)`.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-95067c1e67d4d909", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": " n = np.zeros(np.broadcast(Xv, Yv).shape)\n amp, tot = 1.0, 0.0", + "oldText": " n = np.zeros_like(Xv)\n amp, tot = 1.0, 0.0" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-95067c1e67d4d909", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 41359, + "completion_tokens": 168, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 41527 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "d02030d3", + "pi_parent_id": "b253fc5f", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RtHYsXdcjkKRWCeZY34TTQd0", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 17, + "timestamp": "2026-10-21T11:59:53.283Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Also, the `nn = X[:, hw:int(0.95 * S)] / ln` in vnoise is fine (shape (1, ln)). Then in `fbm(nn[:, :] * (ln / 14.0), Y, ...)`, Xv (1,ln), Yv (S,1) \u2192 no problem.\n\nBut wait, in the natural region: I'm passing Xv = nn*ln/14 as vnoise's X argument, and Y as Y \u2014 but that's fine.\n\nRunning it again.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b6ee7142ecb6316b", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b6ee7142ecb6316b", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 52, in \n col_w[:, seam, :] *= 0.94\n ~~~~~^^^^^^^^^^^^\nIndexError: too many indices for array: array is 3-dimensional, but 4 were indexed\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 41556, + "completion_tokens": 153, + "cached_tokens": 32928, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 41709 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "43bd3950", + "pi_parent_id": "1d3c5464", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R8GPHEwJhCbQCPk3ipbweGcT", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 18, + "timestamp": "2026-10-21T12:00:07.283Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`seam` has shape (1, hw) (from X[:, :hw]). `col_w[:, seam, :]` \u2014 the boolean mask (1,hw) is applied to the second axis, but the mask's shape (1,hw) doesn't match the axis's length hw... Actually, when applying a 2D boolean mask (1,hw) to axis 1 of a 3D array (S,hw,3): numpy treats the boolean mask as an index along a single axis, and the mask's shape needs to either match that axis or be broadcastable to it... A mask of (1,hw) for an axis of length hw: the leading 1 isn't broadcastable to hw \u2192 error \"too many indices\"? The error says 4 indices were applied to a 3D array \u2014 it's because the (1,hw) boolean array was treated as two separate masks (one per dimension). Fix: use `seam[0]` (shape (hw,)).", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-98a17bfd752e296e", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && sed -i 's/col_w\\[:, seam, :\\] \\*= 0.94/col_w[:, seam[0], :] *= 0.94/' build_asset.py && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-98a17bfd752e296e", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 72, in \n col_n -= (np.clip(1 - d, 0, 1) ** 2 * rng.uniform(0.05, 0.10))[..., None]\nValueError: operands could not be broadcast together with shapes (2048,921,3) (2048,2048,1) (2048,921,3) \n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 41799, + "completion_tokens": 287, + "cached_tokens": 41552, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 42086 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "99a5b141", + "pi_parent_id": "2081cbd4", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rw7QWP16WIA4swayI52yLVfH", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 19, + "timestamp": "2026-10-21T12:00:44.602Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Knot loop: `yy, xx = np.mgrid[0:S, 0:S]` are each (S,S), and `d = ((xx-cx)/rx)**2 + ((yy-cy)/ry)**2` is (S,S). `col_n` is (S, ln) (height S \u00d7 width ln=921). Mismatch: I computed d over the full (S,S), but col_n is (S, 921). Need to slice d to the natural region: d_n = d[:, hw:int(0.95*S)]. Fix it.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b03d7da939838e0b", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": " d = ((xx - cx) / rx) ** 2 + ((yy - cy) / ry) ** 2\n col_n -= (np.clip(1 - d[:, hw:int(0.95 * S)], 0, 1) ** 2 * rng.uniform(0.05, 0.10))[..., None]", + "oldText": " d = ((xx - cx) / rx) ** 2 + ((yy - cy) / ry) ** 2\n col_n -= (np.clip(1 - d, 0, 1) ** 2 * rng.uniform(0.05, 0.10))[..., None]" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b03d7da939838e0b", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 42223, + "completion_tokens": 324, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 42547 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "9123340c", + "pi_parent_id": "37380a58", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RcpwfkKLQAIJsoAwm0F9H7nt", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 20, + "timestamp": "2026-10-21T12:00:54.257Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Wait, cx is in the range [hw, hw+0.45S], and xx-cx is (S,S); d is computed on the full grid, then sliced in the X direction (axis 1) by [hw:0.95S] \u2014 this is correct. Since the region in question is X columns hw..0.95S, Y is all of them. col_n's shape is (S, ln), where the columns correspond to X=hw..0.95S. d[:, hw:0.95S] has shape (S, ln). Good.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a02d36e2e7d23178", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a02d36e2e7d23178", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 84, in \n col_e *= 1.0 + 0.05 * fbm(ee[:, :] * le / 6.0, Y, le / 6.0, seed=17)[..., None]\nValueError: non-broadcastable output operand with shape (1,103,3) doesn't match the broadcast shape (2048,103,3)\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 42576, + "completion_tokens": 164, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 42740 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "8ac5ac75", + "pi_parent_id": "191faa1f", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RthqYvU174ExFa7NrINx8uRS", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 21, + "timestamp": "2026-10-21T12:01:27.068Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Same kind of problem: `col_e`'s shape is (1, le, 3) (X slice (1,le) \u00d7 where (3)). In-place operation of `col_e *= ...` with the right-hand side (S, le, 3) \u2192 broadcasting error. Fix: either build col_e as (S, le, 3) from the start, or do `col_e = np.broadcast_to(...)`. More simply: create the stripes using Y: `stripe = (((ee * 44.0) % 1.0) < 0.5) * np.ones((S,1))`... Let me restructure: make `ee2 = np.broadcast_to(ee, (S, le))`. Then stripe is (S,le). c1/c2 are constant arrays.\n\nRewriting the edge region block.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-919a71486095cb53", + "function_name": "edit", + "arguments": { + "edits": [ + { + "newText": "le = int(0.05 * S)\nee = np.broadcast_to(X[:, int(0.95 * S):] / le, (S, le))\nstripe = ((ee * 44.0) % 1.0) < 0.5\nc1 = np.array([0.85, 0.74, 0.52])\nc2 = np.array([0.60, 0.49, 0.32])\ncol_e = np.where(stripe[..., None], c2[None, None, :], c1[None, None, :])\ncol_e = col_e * (1.0 + 0.05 * fbm(ee * le / 6.0, Y, le / 6.0, seed=17)[..., None])\ncol_e = np.clip(col_e, 0, 1)", + "oldText": "le = int(0.05 * S)\nee = X[:, int(0.95 * S):] / le\nstripe = ((ee * 44.0) % 1.0) < 0.5\nc1 = np.array([0.85, 0.74, 0.52])\nc2 = np.array([0.60, 0.49, 0.32])\ncol_e = np.where(stripe[..., None], c2[None, None, :], c1[None, None, :])\ncol_e *= 1.0 + 0.05 * fbm(ee[:, :] * le / 6.0, Y, le / 6.0, seed=17)[..., None]\ncol_e = np.clip(col_e, 0, 1)" + } + ], + "path": "/tmp/build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-919a71486095cb53", + "content": "Successfully replaced 1 block(s) in /tmp/build_asset.py.", + "extra": { + "tool_name": "edit", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 42874, + "completion_tokens": 626, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 43500 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "644e8daa", + "pi_parent_id": "df2e0c65", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RNCgpfSKcb7v1X1m4ow9N8j4", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 22, + "timestamp": "2026-10-21T12:01:44.648Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Also, the white region: col_w is (S, hw, 3) \u2014 the streak_w from fbm(X[:, :hw], Y, ...) is (S, hw). Good. And col[:, :hw] = col_w is fine too.\n\nFor the natural region: streak_n = fbm(nn[:, :] * (ln/14.0), Y, ln/14.0) \u2014 Xv (1,ln), Yv (S,1) \u2192 (S,ln). Good.\n\nRunning it once more.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8c0665779de88798", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8c0665779de88798", + "content": "Traceback (most recent call last):\n File \"/tmp/build_asset.py\", line 79, in \n ee = np.broadcast_to(X[:, int(0.95 * S):] / le, (S, le))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/numpy/lib/_stride_tricks_impl.py\", line 410, in broadcast_to\n return _broadcast_to(array, shape, subok=subok, readonly=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/numpy/lib/_stride_tricks_impl.py\", line 349, in _broadcast_to\n it = np.nditer(\n ^^^^^^^^^^\nValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (1,103) and requested shape (2048,102)\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 43529, + "completion_tokens": 151, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 43680 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "c1cb5a31", + "pi_parent_id": "73390da2", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R448eDvdBcSh48Cdnc9ykekx", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 23, + "timestamp": "2026-10-21T12:02:18.913Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "le = int(0.05*2048) = int(102.4) = 102. However, the slice X[:, int(0.95*S):] \u2192 int(0.95*2048) = int(1945.6) = 1945 \u2192 the slice is 1945..2048 = 103 elements. Mismatch: hw + ln + le should equal S. hw=1024, ln=int(921.6)=921, le=102 \u2192 1024+921+102 = 2047 \u2260 2048. The natural region's slice int(0.95*S)=1945 \u2192 width = 1945-1024 = 921 \u2713 (ln=921 matches). The edge region should be 2048-1945 = 103. Therefore, rather than computing it as int(0.05*S), let's define the edges with le = S - 1945 = 103. Let me define the boundaries: B1 = 1024, B2 = 1945, with hw = B1, ln = B2-B1, le = S-B2.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-81c566f089a4b0d9", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nsrc = src.replace(\"hw = int(0.5 * S)\", \"B1 = int(0.5 * S)\\nhw = B1\")\nsrc = src.replace(\"ln = int(0.45 * S)\", \"B2 = int(0.95 * S)\\nln = B2 - B1\")\nsrc = src.replace(\"le = int(0.05 * S)\", \"le = S - B2\")\nsrc = src.replace(\"X[:, hw:int(0.95 * S)]\", \"X[:, B2]\")\nsrc = src.replace(\"col[:, hw:int(0.95 * S)]\", \"col[:, B2]\")\nsrc = src.replace(\"d[:, hw:int(0.95 * S)]\", \"d[:, B2]\")\nopen('build_asset.py','w').write(src)\nEOF\ngrep -n \"B1\\|B2\\|int(0.95\" build_asset.py | head; python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-81c566f089a4b0d9", + "content": "44:B1 = int(0.5 * S)\n45:hw = B1\n57:B2 = int(0.95 * S)\n58:ln = B2 - B1\n59:nn = X[:, B2] / ln\n74: col_n -= (np.clip(1 - d[:, B2], 0, 1) ** 2 * rng.uniform(0.05, 0.10))[..., None]\n80:le = S - B2\n81:ee = np.broadcast_to(X[:, int(0.95 * S):] / le, (S, le))\n91:col[:, B2] = col_n\n92:col[:, int(0.95 * S):] = col_e\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 60, in \n streak_n = fbm(nn[:, :] * (ln / 14.0), Y, ln / 14.0, seed=5)\n ~~^^^^^^\nIndexError: too many indices for array: array is 1-dimensional, but 2 were indexed\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 43928, + "completion_tokens": 570, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 44498 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "83df4524", + "pi_parent_id": "9d3af6b7", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R6Vv3nuEdgOfjKBH4dAquHro", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 24, + "timestamp": "2026-10-21T12:02:49.879Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`nn = X[:, B2] / ln` \u2014 X has shape (1,S), and X[:, B2] is selecting a column \u2192 becomes (1,)!! X is (1, S): X[:, B2] \u2192 indexes the second axis \u2192 shape (1,). No wait: X = np.arange(S)[None,:] \u2192 shape (1, S). X[:, B2] \u2192 indexes the axis-1 position \u2192 shape (1,). I want the slice X[:, B1:B2]. Ugh, the sed-style replacement broke the slicing. Let me fix all the remaining spots:\n\n- `nn = X[:, B2] / ln` \u2192 `nn = X[:, B1:B2] / ln`\n- `col[:, B2] = col_n` \u2192 `col[:, B1:B2] = col_n`\n- `d[:, B2]` \u2192 `d[:, B1:B2]`\n- `X[:, B2]` (roughness) \u2192 `X[:, B1:B2]`\n- `nn[:, :] * ...` is fine.\n\nWait, but B2 is a scalar \u2014 `X[:, B2]` becomes a single column. Right, all of them need to be slices B1:B2.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b193bf5484a785a9", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && sed -i 's/X\\[:, B2\\]/X[:, B1:B2]/g; s/d\\[:, B2\\]/d[:, B1:B2]/g; s/col\\[:, B2\\]/col[:, B1:B2]/g' build_asset.py && grep -n \"B1:B2\" build_asset.py && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b193bf5484a785a9", + "content": "59:nn = X[:, B1:B2] / ln\n74: col_n -= (np.clip(1 - d[:, B1:B2], 0, 1) ** 2 * rng.uniform(0.05, 0.10))[..., None]\n91:col[:, B1:B2] = col_n\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 106, in \n nrm = np.stack([0.5 - gx * k, 0.5 - gy * k, 1.0], axis=-1)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/numpy/_core/shape_base.py\", line 460, in stack\n raise ValueError('all input arrays must have the same shape')\nValueError: all input arrays must have the same shape\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 44783, + "completion_tokens": 376, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 45159 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "5973b19a", + "pi_parent_id": "956ffa2a", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RPOUF8tWoZTTe2cejZpQIcve", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 25, + "timestamp": "2026-10-21T12:03:01.029Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`np.stack([0.5 - gx*k, 0.5 - gy*k, 1.0])` \u2014 the scalar 1.0 can't be stacked. Fix: `np.full((S,S), 1.0)` or `np.ones_like(gx)`.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9ffa69517428a402", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && sed -i 's/np.stack(\\[0.5 - gx \\* k, 0.5 - gy \\* k, 1.0\\], axis=-1)/np.stack([0.5 - gx * k, 0.5 - gy * k, np.ones_like(gx)], axis=-1)/' build_asset.py && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9ffa69517428a402", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 190, in \n old = Usd.Stage.GetStageAtPath(OUT_USD)\n ^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: type object 'Stage' has no attribute 'GetStageAtPath'. Did you mean: 'GetPrimAtPath'?\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 45393, + "completion_tokens": 171, + "cached_tokens": 43904, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 45564 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "ad2ccdf1", + "pi_parent_id": "23fd51f9", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RsOpH6DRUqZu7k4NDgo84bqb", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 26, + "timestamp": "2026-10-21T12:03:39.355Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "The texture is done. Next, fix the stage part: `Usd.Stage.GetStageAtPath` doesn't exist in this build \u2014 use `Usd.Stage.Open` + a file-existence check, or simply delete the file and CreateNew. Let's replace that block with the following:\n\n```python\nimport os as _os\nif _os.path.exists(OUT_USD):\n _os.remove(OUT_USD)\nstage = Usd.Stage.CreateNew(OUT_USD)\n```", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-abb7ebce89b3ed2a", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nsrc = src.replace('''old = Usd.Stage.GetStageAtPath(OUT_USD)\nif old:\n Usd.Stage.DeleteStage(old)\nstage = Usd.Stage.CreateNew(OUT_USD)''',\n'''if os.path.exists(OUT_USD):\n os.remove(OUT_USD)\nstage = Usd.Stage.CreateNew(OUT_USD)''')\nopen('build_asset.py','w').write(src)\nEOF\npython3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-abb7ebce89b3ed2a", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 194, in \n stage.SetDefaultPrim(root)\nBoost.Python.ArgumentError: Python argument types in\n Stage.SetDefaultPrim(Stage, Xform)\ndid not match C++ signature:\n SetDefaultPrim(pxrInternal_v0_26_8__pxrReserved__::UsdStage {lvalue}, pxrInternal_v0_26_8__pxrReserved__::UsdPrim prim)\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 45667, + "completion_tokens": 256, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 45923 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "fa523e13", + "pi_parent_id": "6f1ece9d", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RFSiVYrm7kVztf2KrqUtaiOm", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 27, + "timestamp": "2026-10-21T12:03:53.119Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`SetDefaultPrim` requires a UsdPrim, not a UsdGeom.Xform. Use `stage.SetDefaultPrim(root.GetPrim())` or `root.GetPrim()`.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8d4fe6a66f4f1ccc", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && sed -i 's/stage.SetDefaultPrim(root)/stage.SetDefaultPrim(root.GetPrim())/' build_asset.py && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8d4fe6a66f4f1ccc", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 198, in \n mesh.CreateFaceVertexNormalsAttr(nrmv)\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": 46063, + "completion_tokens": 99, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 46162 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "725f97ed", + "pi_parent_id": "f5ae0251", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RmTIf9Hw0LRF8Oj8wHZrzlot", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 28, + "timestamp": "2026-10-21T12:04:15.604Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "In this build, UsdGeomMesh doesn't have CreateFaceVertexNormalsAttr (it was added in a later version). Instead, use `mesh.CreateNormalsAttr(...)` to create the \"normals\" primvar (point/vertex level). In the case of vertex duplication, the per-point \"normals\" primvar (interpolation point) = face normals \u2014 this is equivalent to face-vertex normals.\n\nUse the following:\n```python\nmesh.CreateNormalsAttr([Gf.Vec3f(*n) for n in normals])\n```\nCreateNormalsAttr creates a primvar named \"normals\" with point interpolation. The shape is the same as points.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-bed361a9d6ce2d70", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && sed -i 's/mesh.CreateFaceVertexNormalsAttr(nrmv)/mesh.CreateNormalsAttr(nrmv)/' build_asset.py && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-bed361a9d6ce2d70", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 199, in \n mesh.CreatePrimvar(\"uv\", Sdf.Tokens.float2, Vt.Vec2fArrayHandle(uvlist),\n ^^^^^^^^^^^^^^^^^^\nAttributeError: 'Mesh' object has no attribute 'CreatePrimvar'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 46267, + "completion_tokens": 205, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 46472 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "0f81ff4b", + "pi_parent_id": "afdd5625", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RD4qxBbGURJvsE3qizSmklpO", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 29, + "timestamp": "2026-10-21T12:04:44.634Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "This build's UsdGeomMesh doesn't have CreatePrimvar? Actually, UsdGeomMesh should have CreatePrimvar(name, SdfValueTypeNames, value, interpolation) via UsdGeomMeshAPI... Hmm, in older versions UsdGeomMesh inherits UsdSkelMesh, which inherits UsdGeomPrim... CreatePrimvar is a method of UsdGeomMesh in recent versions. This build seems to be old (the v0_26_8 API hints suggest OpenUSD 26.08?? No wait, \"pxrInternal_v0_26_8\" \u2014 that's a pxr API version 0.26.8, which corresponds to OpenUSD... hmm, OpenUSD version 26.08? That's 2026. But that build doesn't have usdUnits on Sdf.Layer?? Strange. Oh well.)\n\nAlternative: create the primvar via `UsdGeom.PrimvarsAPI(mesh.GetPrim()).CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, ...)`? Hmm. Or use the generic `mesh.GetPrim().CreateAttribute(\"primvars:uv\", ...)`? Actually, primvars are stored as attributes named \"primvars:uv\". A cleaner approach:\n\n```python\nprimvars = UsdGeom.PrimvarsAPI(mesh.GetPrim())\nprimvars.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, UsdGeom.Tokens.faceVertex)\nprimvars.GetPrimvar(\"uv\").Set(uvlist)\n```\n\nLet's check what exists: `dir(mesh)` \u2014 let's introspect quickly.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8e6f671ce6efdbce", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf, Vt\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\nprint([a for a in dir(m) if 'rimvar' in a or 'Normal' in a or 'Primvar' in a])\nimport pxr\ntry:\n print(pxr.BuildVersionString())\nexcept Exception as e:\n print(e)\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8e6f671ce6efdbce", + "content": "['CreateDisplayColorPrimvar', 'CreateDisplayOpacityPrimvar', 'CreateNormalsAttr', 'GetDisplayColorPrimvar', 'GetDisplayOpacityPrimvar', 'GetNormalsAttr', 'GetNormalsInterpolation', 'SetNormalsInterpolation']\nmodule 'pxr' has no attribute 'BuildVersionString'", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 46576, + "completion_tokens": 479, + "cached_tokens": 32928, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 47055 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "a3f087e2", + "pi_parent_id": "63decbcd", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R1L0OiCVZcUF3prvginNVJk2", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 30, + "timestamp": "2026-10-21T12:05:12.383Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "There's no CreatePrimvar on Mesh. Use UsdGeom.PrimvarsAPI:\n\n```python\nfrom pxr import UsdGeom\npv = UsdGeom.PrimvarsAPI(mesh.GetPrim())\npv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, UsdGeom.Tokens.faceVertex)\npv.GetPrimvar(\"uv\").Set(uvlist)\n```\n\nDoes PrimvarsAPI have CreatePrimvar? UsdGeomPrimvarsAPI: methods are AddPrimvar? Let me check right away. Actually, in PXR it's `UsdGeomPrimvarsAPI.CreatePrimvar(name, valueTypeName, interpolation)`. Let me check via dir.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8c1970811769e29e", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf, Vt\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\nprint([a for a in dir(pv) if not a.startswith('_')][:40])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8c1970811769e29e", + "content": "['BlockPrimvar', 'CanContainPropertyName', 'CreateIndexedPrimvar', 'CreateNonIndexedPrimvar', 'CreatePrimvar', 'FindIncrementallyInheritablePrimvars', 'FindInheritablePrimvars', 'FindPrimvarWithInheritance', 'FindPrimvarsWithInheritance', 'Get', 'GetAuthoredPrimvars', 'GetPath', 'GetPrim', 'GetPrimvar', 'GetPrimvars', 'GetPrimvarsWithAuthoredValues', 'GetPrimvarsWithValues', 'GetSchemaAttributeNames', 'GetSchemaClassPrimDefinition', 'GetSchemaKind', 'HasPossiblyInheritedPrimvar', 'HasPrimvar', 'IsAPISchema', 'IsAppliedAPISchema', 'IsConcrete', 'IsMultipleApplyAPISchema', 'IsTyped', 'RemovePrimvar']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 47134, + "completion_tokens": 259, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 47393 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "a89cc3d5", + "pi_parent_id": "af9ad86c", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Ros7dhNyFLmTRKXlkoypWQlq", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 31, + "timestamp": "2026-10-21T12:05:22.845Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`PrimvarsAPI.CreatePrimvar` exists. Signature: CreatePrimvar(name, valueTypeName (Sdf.ValueTypeNames or str), interpolation (UsdGeom.Tokens or str), defaultValue?). Let's fix the script:\n\n```python\npv = UsdGeom.PrimvarsAPI(mesh.GetPrim())\npv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, UsdGeom.Tokens.faceVertex)\npv.GetPrimvar(\"uv\").Set(uvlist)\n```", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a3750bcbbac4227a", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nsrc = src.replace('''mesh.CreatePrimvar(\"uv\", Sdf.Tokens.float2, Vt.Vec2fArrayHandle(uvlist),\n UsdGeom.Tokens.faceVertex)''',\n'''pv = UsdGeom.PrimvarsAPI(mesh.GetPrim())\npv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, UsdGeom.Tokens.faceVertex)\npv.GetPrimvar(\"uv\").Set(uvlist)''')\nopen('build_asset.py','w').write(src)\nEOF\npython3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a3750bcbbac4227a", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 200, in \n pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, UsdGeom.Tokens.faceVertex)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: type object 'Tokens' has no attribute 'faceVertex'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 47580, + "completion_tokens": 270, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 47850 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "50ad7ea7", + "pi_parent_id": "04e08ffc", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RBsgNcUI8mId3PbmyXzOFTGo", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 32, + "timestamp": "2026-10-21T12:05:36.636Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "UsdGeom.Tokens.faceVertex \u2014 hmm, the tokens for interpolation are: vertex, face, faceVtx, point, none. Right! In PXR it's `UsdGeom.Tokens.faceVtx` (not faceVertex). Let me fix it.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-906d46d65d9dc54d", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && sed -i 's/UsdGeom.Tokens.faceVertex/UsdGeom.Tokens.faceVtx/' build_asset.py && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-906d46d65d9dc54d", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 200, in \n pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, UsdGeom.Tokens.faceVtx)\n ^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: type object 'Tokens' has no attribute 'faceVtx'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 47952, + "completion_tokens": 119, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 48071 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "6251ff6c", + "pi_parent_id": "0fa6ad29", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RVRJva6GBlcUYRxSEhbJt30d", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 33, + "timestamp": "2026-10-21T12:06:06.738Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Hmm. Let me check which tokens exist in UsdGeom.Tokens.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-82e50283e1a54482", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import UsdGeom\nt = UsdGeom.Tokens\nprint([a for a in dir(t) if not a.startswith('_')])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-82e50283e1a54482", + "content": "['BackPlateAPI', 'BasisCurves', 'Boundable', 'Camera', 'Capsule', 'Capsule_1', 'Cone', 'Cube', 'Curves', 'Cylinder', 'Cylinder_1', 'GeomModelAPI', 'GeomSubset', 'Gprim', 'HermiteCurves', 'Imageable', 'Mesh', 'MotionAPI', 'NurbsCurves', 'NurbsPatch', 'Plane', 'PointBased', 'PointInstancer', 'Points', 'PrimvarsAPI', 'Scope', 'Sphere', 'TetMesh', 'VisibilityAPI', 'Xform', 'XformCommonAPI', 'Xformable', 'accelerations', 'all', 'angularVelocities', 'axis', 'backPlate', 'backPlate_MultipleApplyTemplate_AlphaImage', 'backPlate_MultipleApplyTemplate_DepthCameraSpaceOffset', 'backPlate_MultipleApplyTemplate_DepthImage', 'backPlate_MultipleApplyTemplate_DepthMinOffset', 'backPlate_MultipleApplyTemplate_DepthNormalizingFactor', 'backPlate_MultipleApplyTemplate_Image', 'backPlate_MultipleApplyTemplate_LumaGain', 'backPlate_MultipleApplyTemplate_LumaGamma', 'backPlate_MultipleApplyTemplate_LumaLift', 'backPlate_MultipleApplyTemplate_PlateVisibility', 'backPlate_MultipleApplyTemplate_RotateXYZTweak', 'backPlate_MultipleApplyTemplate_ScaleTweak', 'backPlate_MultipleApplyTemplate_TranslateTweak', 'basis', 'bezier', 'bilinear', 'boundaries', 'bounds', 'box', 'bspline', 'cards', 'catmullClark', 'catmullRom', 'clippingPlanes', 'clippingRange', 'closed', 'constant', 'cornerIndices', 'cornerSharpnesses', 'cornersOnly', 'cornersPlus1', 'cornersPlus2', 'creaseIndices', 'creaseLengths', 'creaseSharpnesses', 'cross', 'cubic', 'curveVertexCounts', 'default_', 'doubleSided', 'edge', 'edgeAndCorner', 'edgeOnly', 'elementSize', 'elementType', 'exposure', 'exposureFStop', 'exposureIso', 'exposureResponsivity', 'exposureTime', 'extent', 'extentsHint', 'fStop', 'face', 'faceVarying', 'faceVaryingLinearInterpolation', 'faceVertexCounts', 'faceVertexIndices', 'familyName', 'focalLength', 'focusDistance', 'fromTexture', 'full', 'guide', 'guideVisibility', 'height', 'hermite', 'holeIndices', 'horizontalAperture', 'horizontalApertureOffset', 'ids', 'inactiveIds', 'indices', 'inherited', 'interpolateBoundary', 'interpolation', 'invisible', 'invisibleIds', 'knots', 'left', 'leftHanded', 'length', 'linear', 'loop', 'metersPerUnit', 'modelApplyDrawMode', 'modelCardGeometry', 'modelCardTextureXNeg', 'modelCardTextureXPos', 'modelCardTextureYNeg', 'modelCardTextureYPos', 'modelCardTextureZNeg', 'modelCardTextureZPos', 'modelCardVisibility', 'modelDrawMode', 'modelDrawModeColor', 'mono', 'motionBlurScale', 'motionNonlinearSampleCount', 'motionVelocityScale', 'mute', 'nonOverlapping', 'none', 'nonperiodic', 'normals', 'open', 'order', 'orientation', 'orientations', 'orientationsf', 'origin', 'orthographic', 'partition', 'periodic', 'perspective', 'pinned', 'pivot', 'point', 'pointWeights', 'points', 'positions', 'power', 'primvarsDisplayColor', 'primvarsDisplayOpacity', 'projection', 'protoIndices', 'prototypes', 'proxy', 'proxyPrim', 'proxyVisibility', 'purpose', 'radius', 'radiusBottom', 'radiusTop', 'ranges', 'render', 'renderVisibility', 'right', 'rightHanded', 'scales', 'segment', 'shutterClose', 'shutterOpen', 'simple', 'size', 'smooth', 'solo', 'stereoRole', 'subdivisionScheme', 'surfaceFaceVertexIndices', 'tangents', 'tetVertexIndices', 'tetrahedron', 'triangleSubdivisionRule', 'trimCurveCounts', 'trimCurveKnots', 'trimCurveOrders', 'trimCurvePoints', 'trimCurveRanges', 'trimCurveVertexCounts', 'type', 'uForm', 'uKnots', 'uOrder', 'uRange', 'uVertexCount', 'unauthoredValuesIndex', 'uniform', 'unrestricted', 'upAxis', 'vForm', 'vKnots', 'vOrder', 'vRange', 'vVertexCount', 'varying', 'velocities', 'vertex', 'verticalAperture', 'verticalApertureOffset', 'visibility', 'visible', 'width', 'widths', 'wrap', 'x', 'xformOpOrder', 'y', 'z']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 48175, + "completion_tokens": 89, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 48264 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "617493c7", + "pi_parent_id": "9a1bf747", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RbAZcyz0u440UR6EVFnVF2R2", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 34, + "timestamp": "2026-10-21T12:06:30.642Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Interesting \u2014 the token list shows `faceVertexIndices`, `face`, `vertex`, `point`, `varying`, `none`... but there's no `faceVtx`?! That's strange. Wait, is this the token list for UsdGeom? UsdGeom.Tokens should include \"faceVtx\". It doesn't exist here. In OpenUSD, the interpolation tokens are in `Usd.Tokens` (usdGeom interpolation tokens: vertex, face, faceVtx, point, none). Actually, is `UsdGeom.Tokens.faceVtx` a thing? Hmm, the interpolation tokens are defined in `UsdGeomTokens` (pxrInternal) and exposed as `UsdGeom.Tokens`: 'none','uniform','vertex','face','faceVtx','variance'... but in this list there's no faceVtx, no uniform either... 'vertex', 'face', 'point', 'none', 'varying' are there. Hmm, 'faceVtx' is completely missing \u2014 strange. Wait, maybe in the newer version the name was changed to 'faceVertex'? But that wasn't in the list either.\n\nHmm wait \u2014 is this really the latest pxr? \"pxrInternal_v0_26_8\" hmm. In OpenUSD 26.08, did they rename the faceVtx token? Actually! I remember: OpenUSD introduced `usdGeom` interpolation tokens... in very recent OpenUSD (2025 and later), there was a change: \"faceVtx\" \u2192 hmm, no.\n\nLet's check Usd.Tokens instead.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9dfbf9e3531c2c59", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom\nt = Usd.Tokens\ncands = [a for a in dir(t) if not a.startswith('_') and any(k in a for k in ('tx','tx','ertex','oint','ace'))]\nprint(cands)\n# try PrimvarsAPI.CreatePrimvar with 'faceVtx' string and check interpolation attr\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\ntry:\n pv.CreatePrimvar(\"uv\", \"float2\", \"faceVtx\")\n print(\"faceVtx ok\")\nexcept Exception as e:\n print(\"faceVtx fail:\", e)\n pv.CreatePrimvar(\"uv\", \"float2\", \"faceVertex\")\n print(\"faceVertex ok\")\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9dfbf9e3531c2c59", + "content": "['ColorSpaceAPI', 'ColorSpaceDefinitionAPI', 'colorSpaceDefinition', 'colorSpaceDefinition_MultipleApplyTemplate_BlueChroma', 'colorSpaceDefinition_MultipleApplyTemplate_Gamma', 'colorSpaceDefinition_MultipleApplyTemplate_GreenChroma', 'colorSpaceDefinition_MultipleApplyTemplate_LinearBias', 'colorSpaceDefinition_MultipleApplyTemplate_Name', 'colorSpaceDefinition_MultipleApplyTemplate_RedChroma', 'colorSpaceDefinition_MultipleApplyTemplate_WhitePoint', 'colorSpaceName']\nfaceVtx fail: Python argument types in\n PrimvarsAPI.CreatePrimvar(PrimvarsAPI, str, str, str)\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)\nTraceback (most recent call last):\n File \"\", line 10, in \nBoost.Python.ArgumentError: Python argument types in\n PrimvarsAPI.CreatePrimvar(PrimvarsAPI, str, str, str)\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\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"\", line 14, in \nBoost.Python.ArgumentError: Python argument types in\n PrimvarsAPI.CreatePrimvar(PrimvarsAPI, str, str, str)\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": 49357, + "completion_tokens": 572, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 49929 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "e3c83cb2", + "pi_parent_id": "1f904e5b", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RwqZvdCfGTzbWF73J2Kqiim1", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 35, + "timestamp": "2026-10-21T12:06:49.791Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "The signature requires a TfToken for name, typeName, and interpolation. In other words, you need to pass Tf.TfToken objects. And regarding the valid token for faceVtx interpolation \u2014 let's try creating it with Tf.TfToken(\"faceVtx\") and see if the file gets written. Actually, the interpolation token for \"face-vertex\" in USD is \"faceVtx\". Let's test it:\n\n```python\nfrom pxr import Tf\npv.CreatePrimvar(Tf.TfToken(\"uv\"), Tf.TfToken(\"float2\"), Tf.TfToken(\"faceVtx\"))\n```\n\nWait, but the third argument's type is SdfValueTypeName \u2014 Tf.TfToken(\"float2\") should be fine. Let me check which interpolation tokens are valid: Tf.TfToken('faceVtx') is the standard one. Let's test it.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a47fc8ae2591a798", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Tf\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\npv.CreatePrimvar(Tf.TfToken(\"uv\"), Tf.TfToken(\"float2\"), Tf.TfToken(\"faceVtx\"))\npr = pv.GetPrimvar(\"uv\")\nprint(\"interp:\", pr.GetInterpolation())\ns.Export(\"/tmp/_uvtest.usd\")\nprint(open(\"/tmp/_uvtest.usd\").read()[:800])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a47fc8ae2591a798", + "content": "Traceback (most recent call last):\n File \"\", line 5, in \nAttributeError: module 'pxr.Tf' has no attribute 'TfToken'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 50530, + "completion_tokens": 353, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 50883 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "f090ea0a", + "pi_parent_id": "b4e9f1fd", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R5w4YRACbN73zad4YaO0HS6s", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 36, + "timestamp": "2026-10-21T12:06:54.546Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "TfToken isn't exposed as Tf.TfToken? In this pxr build, TfToken is a C++ class that's only used as a parameter, and in the Python build it might be exposed as `pxr.Tf.Token`? Let me check dir(Tf).", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a79f12f9672706ff", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 -c \"\nfrom pxr import Tf\nprint([a for a in dir(Tf) if 'oken' in a])\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a79f12f9672706ff", + "content": "['DumpTokenStats', '_TestStaticTokens', '_testStaticTokens']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 50946, + "completion_tokens": 120, + "cached_tokens": 50176, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 51066 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "92b27a02", + "pi_parent_id": "6b65121c", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R4Zrx7WkmAj9gFgdGMltIOvJ", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 37, + "timestamp": "2026-10-21T12:07:21.850Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "This is a non-standard / stripped-down pxr build (Boost.Python-based, probably built from source without the full Python token API). `Tf.TfToken` is missing. Hmm \u2014 but the error message said \"Python argument types in ... did not match C++ signature ... TfToken name\" \u2014 so it's expecting a TfToken object, and that... I can't construct it? Maybe the string is automatically converted to TfToken, and the problem was that I was passing a string to a TfToken parameter? In Boost.Python's pxr bindings, str \u2192 TfToken conversion is usually registered. The error occurred with (str, str, str) \u2014 the signature is (TfToken, SdfValueTypeName, TfToken, int) \u2014 if the first parameter is name (TfToken), the second is typeName (SdfValueTypeName), the third is interpolation (TfToken, default ''), and the fourth is elementSize (int, default -1).\n\nMaybe I should pass the string only in the right place: `pv.CreatePrimvar(\"uv\", \"float2\")` with the default interpolation... but the default interpolation is empty \u2192 maybe 'none' gets created? For UVs, I want faceVtx.\n\nOptions: pass 4 arguments (name, typename, interpolation, elementSize): pv.CreatePrimvar(\"uv\", \"float2\", \"faceVtx\", 1)? If the str\u2192TfToken conversion works. The previous failure was with 3 string arguments \u2014 that should have matched (TfToken, SdfValueTypeName, TfToken)... unless SdfValueTypeName requires a real Sdf.ValueTypeNames object. Maybe the second parameter wasn't matching. Let me try pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, \"faceVtx\", 1) \u2014 hmm, wait, previously when I passed (str, Sdf.Tokens.float2, ...) \u2014 Sdf.Tokens.float2 is a token, but the parameter's type is SdfValueTypeName; perhaps the token \u2192 ValueTypeNames conversion isn't registered, and str \u2192 ValueTypeNames isn't registered either. Let me quickly test a few combinations.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-ac74136d6753b64e", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\nfor args in [\n (\"uv\", Sdf.ValueTypeNames.Float2, \"faceVtx\", 1),\n (\"uv\", \"float2\", \"faceVtx\", 1),\n]:\n try:\n pv.CreatePrimvar(*args)\n print(\"OK\", args[:3], \"interp:\", pv.GetPrimvar(\"uv\").GetInterpolation())\n break\n except Exception as e:\n print(\"FAIL\", args[:3], str(e).splitlines()[0][:120])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-ac74136d6753b64e", + "content": "FAIL ('uv', , 'faceVtx') \nFAIL ('uv', 'float2', 'faceVtx') Python argument types in", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 51096, + "completion_tokens": 656, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 51752 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "10f4580e", + "pi_parent_id": "7e1d4121", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RoIj6CivF6iFHnvG0j6oXgNT", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 38, + "timestamp": "2026-10-21T12:07:38.143Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Both failed. Let's look at the complete error message for the first one (it was cut off partway through). And let's check how `CreateIndexedPrimvar` / `CreateNonIndexedPrimvar` behave. Those might take a more Python-friendly signature. Let's try printing out the full error.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b10b6cef78bbe27d", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\ntry:\n pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, \"faceVtx\", 1)\nexcept Exception as e:\n print(str(e)[:600])\nprint(\"---nonindexed---\")\ntry:\n pv.CreateNonIndexedPrimvar(\"uv\", Sdf.ValueTypeNames.Float2, [1.0, 2.0])\n print(\"nonindexed ok\")\nexcept Exception as e:\n print(str(e)[:300])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b10b6cef78bbe27d", + "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 >\n\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 /m.primvars:uv'\n---nonindexed---\n\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\tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdGeomPrimvar::BlockIndices'", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 51818, + "completion_tokens": 263, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 52081 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "7e684d91", + "pi_parent_id": "16cda60f", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RhBtNO3WJ3APykASwhpaRyk8", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 39, + "timestamp": "2026-10-21T12:08:08.405Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Progress! CreatePrimvar was created (the attribute was created), but then `SetInterpolation(\"faceVtx\")` was called and rejected as invalid?? \"Attempt to set invalid primvar interpolation faceVtx\" \u2014 weird. The valid interpolations in USD are: none, vertex, face, faceVtx, point, constant, varying, default. Wait \u2014 maybe in this build the valid tokens have been changed? The error says faceVtx is invalid. Hmm, is it a case-sensitivity issue? The valid interpolation tokens are defined in `usdGeomTokens`: none, vertex, face, faceVtx, point, constant, varying, default. faceVtx is valid, so it's strange...\n\nOh, wait. The problem might be this: I created it with elementSize=1 and typeName float2 \u2192 elementSize 1 float2 = Vec2f. That's fine. Next, SetInterpolation is called with the TfToken \"faceVtx\" \u2014 but maybe this pxr build's primvar interpolation validation is using `SdInterpolation` and expecting different tokens? Let me check what values are accepted: try \"vertex\", \"point\", \"face\", \"none\".\n\nAlternative: skip the interpolation step \u2014 first create with default interpolation (empty), then set the value, and after that... the same problem will occur. Or directly create the attribute via the Sdf layer: write the attribute \"primvars:uv\" with interpolation=faceVtx into the layer file. The SDF syntax is:\n\n```\npoint float2[] primvars:uv = [...] ( interpolation = \"faceVtx\" )\n```\n\nI could create the entire primvar block directly via Sdf! In fact, since I have the complete data, the simplest and most robust approach is: build the entire USD via raw Sdf text? That's heavy for 84 points, but fine (text). Hmm, but the material graph in raw SDF is verbose.\n\nAnother approach: create the attribute via prim.CreateAttribute(\"primvars:uv\", Sdf.ValueTypeNames.Point2f, ...) and set the value; the interpolation default becomes... does a primvar without an interpolation attribute mean \"point\"? Actually, in USD, if a primvar has no interpolation attribute, it defaults to the schema default \u2014 for \"uv\" the default is 'none'?? If the primvar has no interpolation metadata, the default is 'none' \u2192 for point-count data... 'none' means a single value for the entire prim. That's wrong.\n\nLet me check which interpolation strings this build accepts.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b749d752a65b7fc1", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\nfor name in (\"none\",\"vertex\",\"face\",\"faceVtx\",\"point\",\"constant\",\"varying\",\"default\"):\n try:\n pr = pv.CreatePrimvar(\"uv_\"+name, Sdf.ValueTypeNames.Float2, name, 1)\n print(name, \"->\", pr.GetInterpolation())\n except Exception as e:\n print(name, \"FAIL\", str(e).splitlines()[-1][:100])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b749d752a65b7fc1", + "content": "none FAIL \tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdGeomPrimvar::SetInterpolation' at line 155 in file\nvertex -> vertex\nface FAIL \tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdGeomPrimvar::SetInterpolation' at line 155 in file\nfaceVtx FAIL \tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdGeomPrimvar::SetInterpolation' at line 155 in file\npoint FAIL \tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdGeomPrimvar::SetInterpolation' at line 155 in file\nconstant -> constant\nvarying -> varying\ndefault FAIL \tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdGeomPrimvar::SetInterpolation' at line 155 in file", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 52340, + "completion_tokens": 725, + "cached_tokens": 47040, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 53065 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "4548cf59", + "pi_parent_id": "b9dc029f", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RX9ejrWurKLe5zz5bB88Nbsy", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 40, + "timestamp": "2026-10-21T12:08:56.737Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "This stripped USD build only accepts `vertex`/`constant` interpolation via the API \u2014 since I duplicate vertices per face, `vertex` is exactly equivalent to faceVtx here. Updating:", + "reasoning_content": "Only \"vertex\" and \"constant\" are accepted (plus \"varying\" too?). This is a broken/stripped-down USD build \u2014 the SdInterpolation tokens face/faceVtx/point/none can't be set via the API?! Strange. \"vertex\" works.\n\nInteresting point: this pxr build at /opt/USD seems to be a partial build. Wait \u2014 is it possible that two pxr installations exist? The earlier `python3 -c \"import pxr\"` found /usr/local/lib/python3.12/dist-packages/pxr. And the error mentions the /opt/USD/pxr/... source path \u2014 that's just where it was built.\n\nHmm, wait, actually \u2014 the interpolation check in usdGeom/primvar.cpp: `SdInterpolation::GetTokenFromString`? In the actual source, SetInterpolation checks `if (!SdInterpolation::FromToken(interpolation).IsValid())`. The tokens are \"none\",\"uniform\",\"vertex\",\"face\",\"faceVtx\",\"point\",\"varying\",\"constant\",\"default\". If face/faceVtx/point/none are rejected, it's possible that this build's SdInterpolation enum is different... or, the token string being passed isn't the actual TfToken and string comparison is failing. Oh well \u2014 workaround: use \"vertex\" interpolation.\n\nActually, in my geometry I'm duplicating vertices per face, so the \"vertex\" interpolation primvar (one value per point) is completely equivalent to faceVtx! Each face's vertices are dedicated. So I can just use \"vertex\" interpolation for both uv and normals.\n\nSo: pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, \"vertex\", 1); then .Set(list of Gf.Vec2f).\n\nAnd normals: CreateNormalsAttr also takes a TfToken for interpolation... CreateNormalsAttr(values) probably sets interpolation='vertex' by default. Already worked? It didn't error \u2014 the earlier failure was with CreatePrimvar after CreateNormalsAttr. Let me check whether normals were created with correct interpolation. No problem.\n\nUpdate the script accordingly.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-846138822d42a75d", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nsrc = src.replace('pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, UsdGeom.Tokens.faceVtx)',\n 'pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, \"vertex\", 1)')\nopen('build_asset.py','w').write(src)\nEOF\npython3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-846138822d42a75d", + "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 201, in \n pv.GetPrimvar(\"uv\").Set(uvlist)\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 >''\ntextures written\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 53299, + "completion_tokens": 611, + "cached_tokens": 52528, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 53910 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "68a7968d", + "pi_parent_id": "eb0239c5", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R03dASVelh5OxM3do2KTAoRK", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 41, + "timestamp": "2026-10-21T12:09:52.939Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "The warning \"Returning unknown type for VtValue with unregistered C++ type vector\" is a signal that this pxr build has incomplete Vt type registration \u2014 `Vt.Vec2fArrayHandle(uvlist)` gets registered as an opaque vector, and the stage rejects it. Interesting point: it says \"expected 'GfVec2f'\" \u2014 meaning the attribute's type name is \"point\" (a single GfVec2f), not \"point[]\". Because CreatePrimvar with elementSize=1... no wait, I passed 1 as elementSize. Hmm, with elementSize=1 and float2 \u2192 the element type should be GfVec2f, and the attribute should be GfVec2f[]? The fact that \"expected 'GfVec2f'\" is shown means the attribute is a single value (no array) \u2014 so elementSize=1 means \"element size 1\" and the type became a single float2?? Confusing. The signature is: CreatePrimvar(name, typeName, interpolation, elementSize). If elementSize>1, the element type becomes an N-element vector (e.g., 3 floats \u2192 GfVec3f). So elementSize=1 \u2192 element = a single float?? No wait \u2014 typeName=float2 with elementSize=1... hmm, it says expected GfVec2f \u2192 element type GfVec2f, and the attribute is a single GfVec2f (not an array). So the created attribute is a single-value attribute! I need an array attribute.\n\nUse CreateNonIndexedPrimvar instead? It failed earlier due to a type mismatch (I was passing a Python list of floats, but it was expected as GfVec2f... it wanted an array of GfVec2f but I passed a list of floats). Try pv.CreateNonIndexedPrimvar(\"uv\", Sdf.ValueTypeNames.Point2f, uvlist_gfvec2f)? The earlier error: expected 'GfVec2f', got 'vector' \u2014 because a plain Python list of floats \u2192 VtValue vector. Passing [Gf.Vec2f, ...] might work... but the \"unregistered C++ type vector\" warning suggests the VtArray type isn't registered \u2014 though a GfVec2fArray should be a different registered type.\n\nLet me test: pr = pv.GetPrimvar(\"uv\"); pr.Set([Gf.Vec2f(u,v) for ...]) \u2014 if the attribute's type is a single GfVec2f, then setting an array will fail. First, I need to fix the attribute creation so it becomes an array.\n\nPlan B (more robust): skip the primvar API. After creating the stage with all prims, edit the root Sdf layer's spec text to directly add the attribute to the mesh's definition. For example:\n\n```python\nspec = Sdf.Spec(layer.GetPrimInMaster?...)\n```\nMore simply: after exporting to .usd (ASCII), textually append the primvar lines to the mesh prim's block. Even simpler: build the entire mesh via the Sdf layer:\n\n```python\nmaster = stage.GetRootLayer().GetMaster()\nspec = Sdf.Spec.Create(spec_path, Sdf.AssetType.usd) # from a master of a new layer\n```\n\nHmm, but I want to keep using UsdGeom.Mesh for the standard attributes (points, faceVertexIndices) \u2014 those worked (CreatePointsAttr and CreateFaceVertexIndicesAttr succeeded). Only the custom primvar (uv) is the problem.\n\nThe cleanest approach: post-process the exported .usd text: find the `/Tray/TrayMesh` def Xform... no wait, it's a def Mesh, and inside it add the following:\n\n```\n point float2[] primvars:uv = [ (u0, v0), ... ] ( interpolation = \"faceVtx\" )\n```\n\nInside the prim block. Text insertion into the prim's block is easy: find the line \" def Mesh \\\"TrayMesh\\\"\", find the end of its block (the next line at the same indent level), and insert before it.\n\nActually, the simplest approach is: write out the entire asset.usd as handcrafted ASCII USD? No \u2014 keep the PXR-based authoring (the contract stipulates Python/PXR), and do a small text patch for uv. This is still PXR authoring + post-processing.\n\nOr test whether pr.Set works when the attribute is single-valued... no, I need an array of 84 values.\n\nLet me check how CreateNonIndexedPrimvar handles a list of Gf.Vec2f:", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-bac81d132f9bfa3d", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf, Gf\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\npr = pv.CreateNonIndexedPrimvar(\"uv\", Sdf.ValueTypeNames.Point2f, [Gf.Vec2f(0,0), Gf.Vec2f(1,1)])\nprint(\"created:\", pr.GetAttr().TypeName(), pr.GetAttr().IsArray())\nprint(pr.GetAttr().Get())\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-bac81d132f9bfa3d", + "content": "Traceback (most recent call last):\n File \"\", line 5, in \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": 54122, + "completion_tokens": 1158, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 55280 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "1b0d027d", + "pi_parent_id": "a3c84d6e", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R4jDoa3IjwcCmnmOKCA6VaR9", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 42, + "timestamp": "2026-10-21T12:09:59.561Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Point2f doesn't exist. Use Sdf.ValueTypeNames.Float2 together with a list of Gf.Vec2f.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a740149d814969e9", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf, Gf\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\ntry:\n pr = pv.CreateNonIndexedPrimvar(\"uv\", Sdf.ValueTypeNames.Float2, [Gf.Vec2f(0,0), Gf.Vec2f(1,1)])\n print(\"ok:\", pr.GetAttr().TypeName(), pr.GetAttr().IsArray(), pr.GetAttr().Get())\nexcept Exception as e:\n print(\"FAIL\", str(e)[:300])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a740149d814969e9", + "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 >\nFAIL \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\tError in 'pxrInternal_v0_26_8__pxrReserved__::UsdGeomPrimvar::BlockIndices'", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 55351, + "completion_tokens": 214, + "cached_tokens": 54880, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 55565 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "b2610113", + "pi_parent_id": "e2359db6", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R9urvNuVkMvW1mQ6eiODmW8R", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 43, + "timestamp": "2026-10-21T12:10:30.586Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "This pxr build has Vt array registration broken: any Python list \u2192 VtValue vector \u2192 \"unregistered C++ type vector\". So in this build, setting array values via the Python API is broken?! But wait \u2014 CreatePointsAttr(pts) worked with a list of Gf.Vec3f... really? The script passed through mesh creation without issue (the error occurred at the uv line). CreatePointsAttr took [Gf.Vec3f...] \u2014 that's also a list \u2192 VtValue vector... yet it worked? Maybe CreatePointsAttr uses a different internal path (direct attr creation via typed VtArray? no...).\n\nHmm, \"unregistered C++ type vector>\" \u2014 this warning was also appearing in the uv creation path. CreatePointsAttr(pts) apparently succeeded (no error was shown). Let's verify that the points attribute was actually set: re-run a test where I print GetPoints().\n\nActually, a simpler explanation: CreatePointsAttr's signature directly takes a VtVec3fArrayHandle (C++ side), so Boost.Python converts a Python list \u2192 VtVec3fArray via a registered converter. Meanwhile CreatePrimvar/primvar.Set takes a generic VtValue (via attr.Set(value) where value is a VtValue) \u2014 in this build the list\u2192VtValue conversion produces a vector instead of a typed VtArray. The generic VtValue from a list is broken, but the typed handles (VtVec3fArrayHandle etc.) work.\n\nSo for uv, the options are:\n1. Use attr.Set with a VtValue constructed from a typed handle: `UsdGeom.Primvar` has Set(...), but maybe I can construct a VtValue from a handle: `Vt.Value(uvlist_handle)`? Vt.Value's constructor from VtVec2fArrayHandle \u2192 might work.\n2. Post-process the .usd text to insert the uv primvar.\n\nLet me try option 1 first:\n```python\nattr = pv.GetPrimvar(\"uv\").GetAttr()\nattr.Set(Vt.Value(uvlist)) # uvlist is a list of Gf.Vec2f; or wrap it\n```\nIf the VtValue(VtVec2fArrayHandle) conversion works... does the Python Vt.Value accept a list? Probably the same broken path.\n\nLet me test both quickly.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-bc063afcca814a6c", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf, Gf, Vt\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\nm.CreatePointsAttr([Gf.Vec3f(0,0,0), Gf.Vec3f(1,0,0), Gf.Vec3f(0,1,0)])\nprint(\"points:\", m.GetPoints())\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\npr = pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, \"vertex\", 1)\nattr = pr.GetAttr()\nprint(\"attr type:\", attr.TypeName(), \"array:\", attr.IsArray())\ntry:\n attr.Set(Vt.Value([Gf.Vec2f(0,0), Gf.Vec2f(1,1), Gf.Vec2f(0,1)]))\n print(\"Vt.Value set ok:\", attr.Get())\nexcept Exception as e:\n print(\"Vt.Value FAIL:\", str(e)[:200])\ntry:\n attr.Set(Vt.Vec2fArray([Gf.Vec2f(0,0), Gf.Vec2f(1,1), Gf.Vec2f(0,1)]))\n print(\"Vec2fArray set ok:\", attr.Get())\nexcept Exception as e:\n print(\"Vec2fArray FAIL:\", str(e)[:200])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-bc063afcca814a6c", + "content": "Traceback (most recent call last):\n File \"\", line 5, in \nAttributeError: 'Mesh' object has no attribute 'GetPoints'. Did you mean: 'GetPointsAttr'?\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 55743, + "completion_tokens": 885, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 56628 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "95808891", + "pi_parent_id": "0d06d8bc", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R4V6uFHSbCyTT89ezPQsmDsP", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 44, + "timestamp": "2026-10-21T12:11:10.840Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "GetPointsAttr exists. But wait: was CreatePointsAttr actually storing the value? In the earlier run, CreatePointsAttr and CreateFaceVertexIndicesAttr worked without errors. Let me redo the test using GetPointsAttr().Get().", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-81b090f25a287d38", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf, Gf, Vt\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\nm.CreatePointsAttr([Gf.Vec3f(0,0,0), Gf.Vec3f(1,0,0), Gf.Vec3f(0,1,0)])\nprint(\"points:\", m.GetPointsAttr().Get())\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\npr = pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, \"vertex\", 1)\nattr = pr.GetAttr()\nprint(\"attr type:\", attr.TypeName(), \"array:\", attr.IsArray())\nfor label, val in [\n (\"VtValue(list)\", Vt.Value([Gf.Vec2f(0,0), Gf.Vec2f(1,1), Gf.Vec2f(0,1)])),\n (\"Vec2fArray\", Vt.Vec2fArray([Gf.Vec2f(0,0), Gf.Vec2f(1,1), Gf.Vec2f(0,1)])),\n]:\n try:\n attr.Set(val)\n print(label, \"ok:\", attr.Get())\n except Exception as e:\n print(label, \"FAIL:\", str(e).splitlines()[0][:160])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-81b090f25a287d38", + "content": "points: [(0, 0, 0), (1, 0, 0), (0, 1, 0)]\nTraceback (most recent call last):\n File \"\", line 9, in \nAttributeError: 'Attribute' object has no attribute 'TypeName'. Did you mean: 'GetTypeName'?\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 56696, + "completion_tokens": 404, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 57100 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "f831b50a", + "pi_parent_id": "86d9ab9f", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RKsjosHsETyEgYBKWqmIAHtA", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 45, + "timestamp": "2026-10-21T12:11:27.931Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Points work. Attribute uses the GetTypeName() naming (old-style API). Interesting \u2014 this is an older pxr API style (GetTypeName, not TypeName). Let me continue the test.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8c9a225a736d37a1", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf, Gf, Vt\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\nm.CreatePointsAttr([Gf.Vec3f(0,0,0), Gf.Vec3f(1,0,0), Gf.Vec3f(0,1,0)])\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\npr = pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, \"vertex\", 1)\nattr = pr.GetAttr()\nprint(\"attr type:\", attr.GetTypeName(), \"array:\", attr.IsArray())\nfor label, val in [\n (\"VtValue(list)\", Vt.Value([Gf.Vec2f(0,0), Gf.Vec2f(1,1), Gf.Vec2f(0,1)])),\n (\"Vec2fArray\", Vt.Vec2fArray([Gf.Vec2f(0,0), Gf.Vec2f(1,1), Gf.Vec2f(0,1)])),\n]:\n try:\n attr.Set(val)\n print(label, \"ok:\", attr.Get())\n except Exception as e:\n print(label, \"FAIL:\", str(e).splitlines()[0][:160])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8c9a225a736d37a1", + "content": "Traceback (most recent call last):\n File \"\", line 8, in \nAttributeError: 'Attribute' object has no attribute 'IsArray'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 57196, + "completion_tokens": 383, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 57579 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "2fc94e5b", + "pi_parent_id": "b0a25644", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RADjH4MlN5chYX9t44S2zPzq", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 46, + "timestamp": "2026-10-21T12:11:49.511Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "This is an old API. Let's check what Usd.Attribute has: GetTypeName exists, but IsArray doesn't? Maybe it's `attr.IsArray()` \u2014 no, that's not it... In the old API, is there no is_array? Actually, the modern UsdAttribute has IsArray(). The old one has... Let me introspect. I'm wasting time on API details, so let me quickly print out dir(attr) and then run the Set test.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9072aecc492781b3", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf, Gf, Vt\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\npr = pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, \"vertex\", 1)\nattr = pr.GetAttr()\nprint([a for a in dir(attr) if not a.startswith('_') and ('ype' in a or 'Array' in a or 'Set' in a or 'Get' in a)][:30])\nfor label, val in [\n (\"VtValue(list)\", Vt.Value([Gf.Vec2f(0,0), Gf.Vec2f(1,1), Gf.Vec2f(0,1)])),\n (\"Vec2fArray\", Vt.Vec2fArray([Gf.Vec2f(0,0), Gf.Vec2f(1,1), Gf.Vec2f(0,1)])),\n]:\n try:\n attr.Set(val)\n print(label, \"ok:\", attr.Get())\n except Exception as e:\n print(label, \"FAIL:\", str(e).splitlines()[0][:160])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9072aecc492781b3", + "content": "['ClearArraySizeConstraint', 'Get', 'GetAllAuthoredMetadata', 'GetAllMetadata', 'GetArraySizeConstraint', 'GetAssetInfo', 'GetAssetInfoByKey', 'GetBaseName', 'GetBracketingTimeSamples', 'GetColorSpace', 'GetConnections', 'GetCustomData', 'GetCustomDataByKey', 'GetDescription', 'GetDisplayGroup', 'GetDisplayName', 'GetDocumentation', 'GetFallbackValue', 'GetHardLimits', 'GetLimits', 'GetMetadata', 'GetMetadataByDictKey', 'GetName', 'GetNamespace', 'GetNamespaceDelimiter', 'GetNestedDisplayGroups', 'GetNumTimeSamples', 'GetPath', 'GetPrim', 'GetPrimPath']\nTraceback (most recent call last):\n File \"\", line 9, in \nAttributeError: module 'pxr.Vt' has no attribute 'Value'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 57638, + "completion_tokens": 425, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 58063 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "6ee5f03c", + "pi_parent_id": "b479f078", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-ReIk3oujT9PIx1GayvkNswBi", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 47, + "timestamp": "2026-10-21T12:12:08.217Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`Vt.Value` doesn't exist in this build! This is a pretty stripped-down pxr. So `attr.Set` only accepts native types or typed arrays (Vt.Vec2fArray). Let's just try Vt.Vec2fArray with attr.Set.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-86b56b840c3200a6", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, Sdf, Gf, Vt\ns = Usd.Stage.CreateInMemory()\nm = UsdGeom.Mesh.Define(s, \"/m\")\npv = UsdGeom.PrimvarsAPI(m.GetPrim())\npr = pv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, \"vertex\", 1)\nattr = pr.GetAttr()\ntry:\n attr.Set(Vt.Vec2fArray([Gf.Vec2f(0,0), Gf.Vec2f(1,1), Gf.Vec2f(0,1)]))\n print(\"Vec2fArray ok:\", attr.Get())\nexcept Exception as e:\n print(\"FAIL:\", str(e).splitlines()[0][:200])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-86b56b840c3200a6", + "content": "FAIL:", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 58274, + "completion_tokens": 273, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 58547 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "9d9cef0f", + "pi_parent_id": "f2861f2c", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RiRnJFP6v04I0Q1EWcQ3mpEC", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 48, + "timestamp": "2026-10-21T12:12:49.366Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "`attr.Set` for arrays is broken in this pxr build, so I'll author the `uv` primvar by inserting it into the exported SDF text (still valid USD, authoring done in PXR):", + "reasoning_content": "(Internal thought) Even Vt.Vec2fArray fails (silently \u2014 an error is being printed? \"FAIL:\" was displayed with an empty message. Probably a Boost.Python ArgumentError with no message, or an error was emitted to stderr). This build's generic `attr.Set(VtValue)` path is broken for arrays.\n\nDecision: post-process the exported .usd file as text to add the uv primvar. The exported file is ASCII USD. Insert the following inside the `def Mesh \"TrayMesh\"` block:\n\n```\n point float2[] primvars:uv = [ (u0, v0), ... ] ( interpolation = \"faceVtx\" )\n```\n\nWait \u2014 in the SDF prim block, primvars are declared like:\n```\ndef Mesh \"TrayMesh\" (\n ...\n)\n{\n ...attributes...\n point float2[] primvars:uv = [...] (\n interpolation = \"faceVtx\"\n )\n}\n```\nThat's valid SDF. The interpolation value \"faceVtx\" is valid in SDF (Sdf.Interpolation). The Python API's token check was weirdly restrictive, but the text format supports faceVtx.\n\nFind the block's end position: the `def Mesh \"TrayMesh\"` block starts with ` def Mesh \"TrayMesh\" (`, and the block ends at a line with 4-space indentation ` }`. Let's implement a simple text insertion: find the index of the 'def Mesh \"TrayMesh\"' line, and find the first subsequent line starting with ' }' (4 spaces + }), and insert the uv attribute line before it.\n\nAlso \u2014 normals: CreateNormalsAttr(nrmv) \u2014 did it work? It should have worked (no error). Interpolation of normals via CreateNormalsAttr: in the standard pxr, CreateNormalsAttr sets the interpolation to... 'faceVtx'! If this build's mesh schema default for normals is faceVtx via the schema (not via SetInterpolation), it might work. Because schema defaults bypass the buggy check. I'll verify by checking the exported text.\n\nLet's update the build script: replace the pv uv creation code with a text post-processing step after export (before the final header fix \u2014 the header fix is currently done after export; order: export \u2192 insert uv \u2192 fix header). Let me restructure: after stage.Export(OUT_USD), do the following:\n\n```python\nlines = open(OUT_USD).read().splitlines(keepends=True)\n# find the mesh block\nstart = next(i for i, l in enumerate(lines) if 'def Mesh \"TrayMesh\"' in l)\nend = next(i for i in range(start, len(lines)) if lines[i].rstrip() == ' }')\nuv_line = \" point float2[] primvars:uv = [ \" + \", \".join(f\"({u:.6f}, {v:.6f})\" for u, v in uvlist) + \" ] (\\n interpolation = \\\"faceVtx\\\"\\n )\\n\"\nlines.insert(end, uv_line)\nopen(OUT_USD, \"w\").write(\"\".join(lines))\n```\n\nHmm, the indentation of the def block: ` def Mesh \"TrayMesh\" (` \u2014 the braces of the prim block are at 4 spaces. Attributes are at 8 spaces. The closing ` }` is at 4 spaces. OK.\n\nAlso, remove the failed pv code. Then run export + uv insertion + header fix.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8b4767abaf84b695", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nsrc = src.replace('''pv = UsdGeom.PrimvarsAPI(mesh.GetPrim())\npv.CreatePrimvar(\"uv\", Sdf.ValueTypeNames.Float2, \"vertex\", 1)\npv.GetPrimvar(\"uv\").Set(uvlist)\n''', \"\")\nsrc = src.replace('''stage.Export(OUT_USD)\n\n# this OpenUSD build: set upAxis / units via the #usda layer header''',\n'''stage.Export(OUT_USD)\n\n# add the uv primvar (this pxr build cannot set array values via attr.Set)\nlines = open(OUT_USD).read().splitlines(keepends=True)\nstart = next(i for i, l in enumerate(lines) if 'def Mesh \"TrayMesh\"' in l)\nend = next(i for i in range(start, len(lines)) if lines[i].rstrip() == \" }\")\nuv_values = \", \".join(\"({0:.7f}, {1:.7f})\".format(u, v) for u, v in uvlist)\nuv_block = (' point float2[] primvars:uv = [ {0} ] (\\\\n'\n ' interpolation = \"faceVtx\"\\\\n'\n ' )\\\\n').format(uv_values)\nlines.insert(end, uv_block)\nopen(OUT_USD, \"w\").write(\"\".join(lines))\n\n# this OpenUSD build: set upAxis / units via the #usda layer header''')\nopen('build_asset.py','w').write(src)\nEOF\npython3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8b4767abaf84b695", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 203, in \n ps = mat.CreateUsdPreviewSurface()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Material' object has no attribute 'CreateUsdPreviewSurface'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 58564, + "completion_tokens": 1191, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 59755 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "3ab61a4c", + "pi_parent_id": "2f70fc35", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RLeEh6k2N7ZVLAHm6lXmEbyN", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 49, + "timestamp": "2026-10-21T12:13:11.268Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "This is an old UsdShade API (pre-22.05). The old API: `mat.CreateShader()` with createApiSchemas... Let me try introspecting UsdShade.Material in this build.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-ae28d3ebe78d10e6", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 -c \"\nfrom pxr import UsdShade\nm = UsdShade.Material\nprint([a for a in dir(m) if not a.startswith('_')])\" | tr ',' '\\n' | head -60" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-ae28d3ebe78d10e6", + "content": "['ClearBaseMaterial'\n 'ComputeDisplacementSource'\n 'ComputeInterfaceInputConsumersMap'\n 'ComputeOutputSource'\n 'ComputeSurfaceSource'\n 'ComputeVolumeSource'\n 'ConnectableAPI'\n 'CreateDisplacementAttr'\n 'CreateDisplacementOutput'\n 'CreateInput'\n 'CreateMasterMaterialVariant'\n 'CreateOutput'\n 'CreateSurfaceAttr'\n 'CreateSurfaceOutput'\n 'CreateVolumeAttr'\n 'CreateVolumeOutput'\n 'Define'\n 'Get'\n 'GetBaseMaterial'\n 'GetBaseMaterialPath'\n 'GetDisplacementAttr'\n 'GetDisplacementOutput'\n 'GetDisplacementOutputs'\n 'GetEditContextForVariant'\n 'GetInput'\n 'GetInputs'\n 'GetInterfaceInputs'\n 'GetMaterialVariant'\n 'GetOutput'\n 'GetOutputs'\n 'GetPath'\n 'GetPrim'\n 'GetSchemaAttributeNames'\n 'GetSchemaClassPrimDefinition'\n 'GetSchemaKind'\n 'GetSurfaceAttr'\n 'GetSurfaceOutput'\n 'GetSurfaceOutputs'\n 'GetVolumeAttr'\n 'GetVolumeOutput'\n 'GetVolumeOutputs'\n 'HasBaseMaterial'\n 'IsAPISchema'\n 'IsAppliedAPISchema'\n 'IsConcrete'\n 'IsMultipleApplyAPISchema'\n 'IsTyped'\n 'SetBaseMaterial'\n 'SetBaseMaterialPath']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 59846, + "completion_tokens": 130, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 59976 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "6dee19a5", + "pi_parent_id": "e4e7433a", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RU6krBmnL7aM5S6tRpU9p80N", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 50, + "timestamp": "2026-10-21T12:13:46.886Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "This is a pre-22.05 UsdShade \u2014 I'll build the material graph with `UsdShade.Shader` nodes directly:", + "reasoning_content": "This is an old UsdShade (pre-22.05): no CreateUsdPreviewSurface, no CreateTexture. UsdShader exists, so use it:\n\n```python\ndef new_node(name, id_):\n sh = UsdShade.Shader.Define(stage, f\"/Tray/TrayMaterial/{name}\")\n sh.CreateIdAttr(id_)\n sh.CreateTypeAttr(\"proto\") # maybe\n return sh\n```\n\nUsdShade.Shader API in the old build: CreateIdAttr, CreateTypeNameAttr? Let me check the methods on UsdShade.Shader. Then create inputs with CreateInput(name, Sdf.ValueTypeNames), and connect with source.ConnectOutput(name, target.GetPrim(), inputName) or via UsdShade.ConnectAPI... The old way is:\n\n```python\ntex = UsdShade.Shader.Define(stage, path)\ntex.CreateIdAttr(\"UsdUVTexture\")\ntex.CreateTypeNameAttr(\"usd\")\ntex.CreateInput(\"file\", Sdf.ValueTypeNames.Asset) # asset type\ntex.GetAttribute(\"inputs:file\").Set(\"textures/tray_color.png\")\ntex.CreateInput(\"colorSpace\", Sdf.ValueTypeNames.Token)\ntex.GetAttribute(\"inputs:colorSpace\").Set(\"srgb\")\ntex.CreateOutput(\"rgb\", Sdf.ValueTypeNames.Color3f) # maybe outputs are auto\n```\n\nThen ps:\n```python\nps = UsdShade.Shader.Define(stage, \"/Tray/TrayMaterial/PreviewSurface\")\nps.CreateIdAttr(\"UsdPreviewSurface\")\nps.CreateTypeNameAttr(\"usd\")\nps.CreateInput(\"baseColor\", Sdf.ValueTypeNames.Color3f)\nps.CreateInput(\"roughness\", Sdf.ValueTypeNames.Float)\nps.CreateInput(\"normal\", Sdf.ValueTypeNames.Normal?) # vec3f\nps.CreateInput(\"metallic\", Sdf.ValueTypeNames.Float) \u2192 set 0\n```\nConnect: `tex.ConnectOutput(\"rgb\", ps.GetPrim(), \"baseColor\")`.\n\nAnd the material: mat.CreateSurfaceOutput() returns a UsdShadeOutput; `mat.ConnectOutputToInput(out, ps.GetPrim(), \"surface\")` or ps.ConnectOutput(\"surface\"?...). The preview surface has a \"surface\" output: `ps.CreateOutput(\"surface\", Sdf.ValueTypeNames.Token)`.\n\nAfter that, the material's surface output connects to ps's surface output.\n\nLet me quickly check the methods on UsdShade.Shader and the methods on UsdShadeOutput.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b4881bec66be92c3", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import UsdShade, Sdf\nsh = UsdShade.Shader\nprint(\"Shader:\", [a for a in dir(sh) if 'Create' in a or 'Connect' in a or 'GetInput' in a])\nout = UsdShade.Output\nprint(\"Output:\", [a for a in dir(out) if not a.startswith('_')])\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b4881bec66be92c3", + "content": "Shader: ['ConnectableAPI', 'CreateIdAttr', 'CreateImplementationSourceAttr', 'CreateInput', 'CreateOutput', 'GetInput', 'GetInputs']\nOutput: ['CanConnect', 'ClearSdrMetadata', 'ClearSdrMetadataByKey', 'ClearSource', 'ClearSources', 'ConnectToSource', 'DisconnectSource', 'GetAttr', 'GetBaseName', 'GetConnectedSource', 'GetConnectedSources', 'GetFullName', 'GetPrim', 'GetRawConnectedSourcePaths', 'GetRenderType', 'GetSdrMetadata', 'GetSdrMetadataByKey', 'GetTypeName', 'GetValueProducingAttributes', 'HasConnectedSource', 'HasRenderType', 'HasSdrMetadata', 'HasSdrMetadataByKey', 'IsOutput', 'IsSourceConnectionFromBaseMaterial', 'Set', 'SetConnectedSources', 'SetRenderType', 'SetSdrMetadata', 'SetSdrMetadataByKey']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 60293, + "completion_tokens": 710, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 61003 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "bcc1f323", + "pi_parent_id": "95a77500", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RrhIccG4YMZdvyRuDUhKWPX2", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 51, + "timestamp": "2026-10-21T12:15:02.283Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "OK. UsdShade.Shader has CreateIdAttr, CreateImplementationSourceAttr, CreateInput, CreateOutput, GetInput. There's no CreateTypeNameAttr \u2014 the \"type\" attribute is implicit (default is \"usd\"? Or do I have to set the id?). In old USD, the shader prim has \"id\", \"type\", and \"implementationSource\". The type token for Omni materials is \"omni\", and the default type is... For a UsdPreviewSurface with id \"UsdPreviewSurface\", type \"usd\" is correct (default is 'usd'? I think in SdrSchema the default value for 'type' was \"usd\" \u2014 actually, the default value of the SdrSchema 'type' attribute is 'usd'). I'll rely on the default, but just to be safe, I'll also set the implementationSource... CreateImplementationSourceAttr might let me set \"constant\" or \"spg\". Not needed.\n\nConnections: UsdShade.Output.ConnectToSource(UsdShadeInput)? Or input.ConnectToSource? In the old API: `UsdShade.Input` has `ConnectToSource(source)`. And mat.CreateSurfaceOutput() \u2192 UsdShadeOutput; connect it like this: `surface_out.ConnectToSource(ps_surface_out)`? Hmm, or connect the preview surface's \"surface\" output to the material's surface output: in the standard graph: outputs:surface of the Material prim \u2190 the surface output of the preview surface shader. So: ps.CreateOutput(\"surface\", ...); matSurfaceOut = mat.CreateSurfaceOutput(); then matSurfaceOut.ConnectToSource? Does UsdShadeOutput inherit from UsdShade.Output? UsdShadeOutput is a subclass of UsdShadeOutput \u2192 it has ConnectToSource.\n\nFor inputs: ps.GetInput(\"baseColor\") \u2192 UsdShadeInput, which has ConnectToSource(output). Or `tex.ConnectOutput(\"rgb\", ps.GetPrim(), \"inputs:baseColor\")` \u2014 does UsdShade.Shader inherit from ConnectableAPI and have ConnectOutput(name, prim, inputName)? ConnectableAPI.ConnectOutput(outputName, prim, inputName) exists in the old API. I'll use that.\n\nThe old UsdUVTexture's inputs: \"file\" (asset), \"colorSpace\"? In the old UsdUVTexture (pre-22.05): the inputs are file, offset, size, wrapS, wrapT, channels... The \"colorSpace\" input was added around 22.11. This build might not have colorSpace. I'll just set file, and possibly wrapS/wrapT to \"repeat\" (default is repeat). Without colorSpace, the texture is treated as linear by default... hmm, for sRGB PNGs, the renderer will sample them as linear \u2192 colors will be brighter (gamma applied?). Actually, without a color space hint, UsdUVTexture's default is \"auto\" \u2192 sRGB for png? The default for the colorSpace parameter is 'auto', which resolves to sRGB for png. In the old version without colorSpace, the shading API's default behavior is... In the SdrShader \"UsdUVTexture\", the \"file\" input has metadata, and the color space is derived from the format (auto). I think that's fine.\n\nRoughness texture: sample the grayscale .r.\n\nSo, I'll rewrite the material section of build_asset.py.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a101e6db175d5752", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nold = '''# =================================================================== material\nmat = UsdShade.Material.Define(stage, \"/Tray/TrayMaterial\")\nmat.CreateSurfaceOutput()\nps = mat.CreateUsdPreviewSurface()\ntex_c = mat.CreateTexture(\"textures/tray_color.png\", \"srgb\")\ntex_r = mat.CreateTexture(\"textures/tray_rough.png\", \"raw\")\ntex_n = mat.CreateTexture(\"textures/tray_normal.png\", \"raw\")\nps.ConnectInput(\"baseColor\", tex_c.ConnectOutput(\"rgb\"))\nps.ConnectInput(\"roughness\", tex_r.ConnectOutput(\"r\"))\nps.ConnectInput(\"normal\", tex_n.ConnectOutput(\"rgb\"))\nps.CreateInput(\"metallic\", 0.0)\nmat.Bind(mesh)\n'''\nnew = '''# =================================================================== material\nM = \"/Tray/TrayMaterial\"\nmat = UsdShade.Material.Define(stage, M)\nmat.CreateSurfaceOutput()\n\ndef shader(name, sid):\n sh = UsdShade.Shader.Define(stage, M + \"/\" + name)\n sh.CreateIdAttr(sid)\n return sh\n\ntex_c = shader(\"TexColor\", \"UsdUVTexture\")\ntex_c.CreateInput(\"file\", Sdf.ValueTypeNames.Asset)\ntex_c.GetInput(\"file\").Set(\"textures/tray_color.png\")\ntex_c.CreateOutput(\"rgb\", Sdf.ValueTypeNames.Color3f)\n\ntex_r = shader(\"TexRough\", \"UsdUVTexture\")\ntex_r.CreateInput(\"file\", Sdf.ValueTypeNames.Asset)\ntex_r.GetInput(\"file\").Set(\"textures/tray_rough.png\")\ntex_r.CreateOutput(\"r\", Sdf.ValueTypeNames.Float)\n\ntex_n = shader(\"TexNormal\", \"UsdUVTexture\")\ntex_n.CreateInput(\"file\", Sdf.ValueTypeNames.Asset)\ntex_n.GetInput(\"file\").Set(\"textures/tray_normal.png\")\ntex_n.CreateOutput(\"rgb\", Sdf.ValueTypeNames.Color3f)\n\nps = shader(\"PreviewSurface\", \"UsdPreviewSurface\")\nps.CreateInput(\"baseColor\", Sdf.ValueTypeNames.Color3f)\nps.CreateInput(\"roughness\", Sdf.ValueTypeNames.Float)\nps.CreateInput(\"normal\", Sdf.ValueTypeNames.Float3)\nps.CreateInput(\"metallic\", Sdf.ValueTypeNames.Float)\nps.GetInput(\"metallic\").Set(0.0)\nps.CreateOutput(\"surface\", Sdf.ValueTypeNames.Token)\n\ntex_c.ConnectOutput(\"rgb\", ps.GetPrim(), \"baseColor\")\ntex_r.ConnectOutput(\"r\", ps.GetPrim(), \"roughness\")\ntex_n.ConnectOutput(\"rgb\", ps.GetPrim(), \"normal\")\nmat.GetSurfaceOutput().ConnectToSource(ps.GetOutput(\"surface\"))\nmat.Bind(mesh)\n'''\nassert old in src\nsrc = src.replace(old, new)\nopen('build_asset.py','w').write(src)\nEOF\npython3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a101e6db175d5752", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 233, in \n tex_c.ConnectOutput(\"rgb\", ps.GetPrim(), \"baseColor\")\n ^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Shader' object has no attribute 'ConnectOutput'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 61212, + "completion_tokens": 1411, + "cached_tokens": 48608, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 62623 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "417c20f9", + "pi_parent_id": "fa7e4f89", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RA96dIxfAcIcWfCzwcGGttKX", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 52, + "timestamp": "2026-10-21T12:15:18.691Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "The Shader in this build doesn't have ConnectOutput (ConnectableAPI methods?). Check the UsdShade.Input methods and how to connect: `ps.GetInput(\"baseColor\").ConnectToSource(tex_c.GetOutput(\"rgb\"))`? GetOutput returns a UsdShadeOutput. Check the Input methods.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-ad59a363c29f2f6e", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 -c \"\nfrom pxr import UsdShade\nprint([a for a in dir(UsdShade.Input) if not a.startswith('_')])\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-ad59a363c29f2f6e", + "content": "['CanConnect', 'ClearConnectability', 'ClearSdrMetadata', 'ClearSdrMetadataByKey', 'ClearSource', 'ClearSources', 'ConnectToSource', 'DisconnectSource', 'Get', 'GetAttr', 'GetBaseName', 'GetConnectability', 'GetConnectedSource', 'GetConnectedSources', 'GetDisplayGroup', 'GetDocumentation', 'GetFullName', 'GetPrim', 'GetRawConnectedSourcePaths', 'GetRenderType', 'GetSdrMetadata', 'GetSdrMetadataByKey', 'GetTypeName', 'GetValueProducingAttribute', 'GetValueProducingAttributes', 'HasConnectedSource', 'HasRenderType', 'HasSdrMetadata', 'HasSdrMetadataByKey', 'IsInput', 'IsInterfaceInputName', 'IsSourceConnectionFromBaseMaterial', 'Set', 'SetConnectability', 'SetConnectedSources', 'SetDisplayGroup', 'SetDocumentation', 'SetRenderType', 'SetSdrMetadata', 'SetSdrMetadataByKey']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 62714, + "completion_tokens": 133, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 62847 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "37945bd5", + "pi_parent_id": "7b7bcb39", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RMzvWQHND2zZBG06JDiqgvaN", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 53, + "timestamp": "2026-10-21T12:15:55.297Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`UsdShade.Input.ConnectToSource(source)` exists. Therefore:\n\n```python\nps.GetInput(\"baseColor\").ConnectToSource(tex_c.GetOutput(\"rgb\"))\n```\nAnd for the material's surface output: mat.GetSurfaceOutput() \u2192 UsdShadeOutput; but to connect the preview surface's output to the material's output, should I use `ps.GetOutput(\"surface\").ConnectToSource?` No wait \u2014 the direction of the connection is: the material's surface output's source is the ps's surface output. The UsdShadeOutput for the material's surface has ConnectToSource(source). Therefore: `mat.GetSurfaceOutput().ConnectToSource(ps.GetOutput(\"surface\"))`.\n\nI'll fix it by replacing the 3 lines of tex_c.ConnectOutput...", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b2e2d9827d6be257", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nsrc = src.replace('''tex_c.ConnectOutput(\"rgb\", ps.GetPrim(), \"baseColor\")\ntex_r.ConnectOutput(\"r\", ps.GetPrim(), \"roughness\")\ntex_n.ConnectOutput(\"rgb\", ps.GetPrim(), \"normal\")\nmat.GetSurfaceOutput().ConnectToSource(ps.GetOutput(\"surface\"))''',\n'''ps.GetInput(\"baseColor\").ConnectToSource(tex_c.GetOutput(\"rgb\"))\nps.GetInput(\"roughness\").ConnectToSource(tex_r.GetOutput(\"r\"))\nps.GetInput(\"normal\").ConnectToSource(tex_n.GetOutput(\"rgb\"))\nmat.GetSurfaceOutput().ConnectToSource(ps.GetOutput(\"surface\"))''')\nopen('build_asset.py','w').write(src)\nEOF\npython3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b2e2d9827d6be257", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 237, in \n mat.Bind(mesh)\n ^^^^^^^^\nAttributeError: 'Material' object has no attribute 'Bind'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 63067, + "completion_tokens": 374, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 63441 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "bbe4ab20", + "pi_parent_id": "8ee5f833", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RDCWsBf0pyYyvR1TYfmxCJsw", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 54, + "timestamp": "2026-10-21T12:16:47.643Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "mat.Bind doesn't exist in the old API. Use `mat.GetPrim().GetAPI?`... No wait, the standard approach: `UsdGeom.Mesh` has no Bind; binding is done via the `material:binding` relationship on the target prim:\n\n```python\nmesh.GetPrim().GetAttribute? \n```\nActually, in PXR: `mat.CreateBindingRel(mesh.GetPrim())` \u2014 the old API: `UsdShade.Material.CreateBindingRel(primOrPath)`? Hmm, in the newer API: mat.CreateBindingRel() creates a relationship, and then you add a target via `rel.CreateTarget(...)`? Hmm, no: `UsdShadeMaterial.CreateBindingRel(*targets)`. In the old API: `mat.CreateBindingRel(mesh.GetPrim())` might exist. Let me check the Material methods: the dir listing from earlier didn't show CreateBindingRel! The listing was: ClearBaseMaterial, Compute*, ConnectableAPI, CreateDisplacementAttr, CreateDisplacementOutput, CreateInput, CreateMasterMaterialVariant, CreateOutput, CreateSurfaceAttr, CreateSurfaceOutput, CreateVolumeAttr, CreateVolumeOutput, Define, Get*, SetBaseMaterial... There's no CreateBindingRel!\n\nSo the binding has to be done at the Sdf level: on the mesh prim, add the relationship `material:binding` with target \"/Tray/TrayMaterial\". Via the API: `mesh.GetPrim().GetRelationshipInMasters?` Old style: `Usd.Prim.CreateRelationship(\"material:binding\", [Usd.TfToken(M)])`? The old API has `prim.CreateRelationship(name, targets)`. Let me check Usd.Prim's methods.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-ae861048aff6f861", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 -c \"\nfrom pxr import Usd\nprint([a for a in dir(Usd.Prim) if 'elation' in a or 'inding' in a])\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-ae861048aff6f861", + "content": "['CreateRelationship', 'FindAllRelationshipTargetPaths', 'GetAuthoredRelationships', 'GetRelationship', 'GetRelationshipAtPath', 'GetRelationships', 'HasRelationship']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 63519, + "completion_tokens": 428, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 63947 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "a3cfb120", + "pi_parent_id": "f041a0e7", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rqi7zsFPmnd4rOvUYHJCYfvw", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 55, + "timestamp": "2026-10-21T12:17:14.872Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`prim.CreateRelationship(name, targets)` exists (the old API). Use it like this:\n\n```python\nmesh.GetPrim().CreateRelationship(\"material:binding\", [TfToken? or str?])\n```\n\nThe old CreateRelationship(name, targets: list of TfToken/str?). Let me test with a string. Actually, the signature in the old API is: CreateRelationship(name, targets=None, forwardToNone=None), where targets is a list of TfToken or UsdPrim? For now let's just try it.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-90c059393ce63b98", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nfrom pxr import Usd, UsdGeom, UsdShade\ns = Usd.Stage.CreateInMemory()\nroot = UsdGeom.Xform.Define(s, \"/R\")\nmesh = UsdGeom.Mesh.Define(s, \"/R/M\")\nmat = UsdShade.Material.Define(s, \"/R/Mat\")\nmesh.GetPrim().CreateRelationship(\"material:binding\", [\"/R/Mat\"])\nprint(mesh.GetPrim().GetAuthoredRelationships())\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-90c059393ce63b98", + "content": "Traceback (most recent call last):\n File \"\", line 6, in \nBoost.Python.ArgumentError: Python argument types in\n Prim.CreateRelationship(Prim, str, list)\ndid not match C++ signature:\n CreateRelationship(pxrInternal_v0_26_8__pxrReserved__::UsdPrim {lvalue}, std::vector, std::allocator >, std::allocator, std::allocator > > > nameElts, bool custom=True)\n CreateRelationship(pxrInternal_v0_26_8__pxrReserved__::UsdPrim {lvalue}, pxrInternal_v0_26_8__pxrReserved__::TfToken name, bool custom=True)\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 63998, + "completion_tokens": 255, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 64253 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "5a45caf5", + "pi_parent_id": "daa3e052", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RqB8KaCnlzB3jy6htsHCsid6", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 56, + "timestamp": "2026-10-21T12:17:31.260Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Interesting \u2014 this \"old\" API's CreateRelationship has a nameElts (list of strings) + custom signature, and doesn't have targets. So this is actually a newer-ish pxr that has a different binding approach. In recent OpenUSD (23.11 and later?), the signature of `Usd.Prim.CreateRelationship` was changed? Hmm, no...\n\nActually, wait \u2014 \"CreateRelationship(std::vector nameElts, bool custom)\" \u2014 this is the signature in the latest OpenUSD (2025.01 and later? where relationships became more flexible?). No, actually, the latest CreateRelationship is still (name: TfToken, targets: list[TfToken], forwardToNone). The variant that takes nameElts (list of strings) and no targets is a newer addition for \"custom relationship name with no targets\".\n\nSo in this build: `prim.CreateRelationship([\"material\", \"binding\"])` creates a relationship with the name \"material:binding\" and no targets. Then to add targets: `rel = prim.GetRelationship(\"material:binding\")` \u2192 `rel.CreateTarget?` Let me check the Usd.Relationship methods in this build: in the new API, `rel.AddTarget(path)`. Let's take a look.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a9c13af48077d4bc", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 -c \"\nfrom pxr import Usd\nprint([a for a in dir(Usd.Relationship) if not a.startswith('_')])\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a9c13af48077d4bc", + "content": "['AddTarget', 'ClearAssetInfo', 'ClearAssetInfoByKey', 'ClearCustomData', 'ClearCustomDataByKey', 'ClearDisplayGroup', 'ClearDisplayName', 'ClearDocumentation', 'ClearHidden', 'ClearMetadata', 'ClearMetadataByDictKey', 'ClearTargets', 'FlattenTo', 'GetAllAuthoredMetadata', 'GetAllMetadata', 'GetAssetInfo', 'GetAssetInfoByKey', 'GetBaseName', 'GetCustomData', 'GetCustomDataByKey', 'GetDescription', 'GetDisplayGroup', 'GetDisplayName', 'GetDocumentation', 'GetForwardedTargets', 'GetMetadata', 'GetMetadataByDictKey', 'GetName', 'GetNamespace', 'GetNamespaceDelimiter', 'GetNestedDisplayGroups', 'GetPath', 'GetPrim', 'GetPrimPath', 'GetPropertyStack', 'GetPropertyStackWithLayerOffsets', 'GetStage', 'GetTargets', 'HasAssetInfo', 'HasAssetInfoKey', 'HasAuthoredAssetInfo', 'HasAuthoredAssetInfoKey', 'HasAuthoredCustomData', 'HasAuthoredCustomDataKey', 'HasAuthoredDisplayGroup', 'HasAuthoredDisplayName', 'HasAuthoredDocumentation', 'HasAuthoredHidden', 'HasAuthoredMetadata', 'HasAuthoredMetadataDictKey', 'HasAuthoredTargets', 'HasCustomData', 'HasCustomDataKey', 'HasMetadata', 'HasMetadataDictKey', 'IsAuthored', 'IsAuthoredAt', 'IsCustom', 'IsDefined', 'IsHidden', 'IsValid', 'RemoveTarget', 'SetAssetInfo', 'SetAssetInfoByKey', 'SetCustom', 'SetCustomData', 'SetCustomDataByKey', 'SetDisplayGroup', 'SetDisplayName', 'SetDocumentation', 'SetHidden', 'SetMetadata', 'SetMetadataByDictKey', 'SetNestedDisplayGroups', 'SetTargets', 'SplitName']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 64468, + "completion_tokens": 334, + "cached_tokens": 63504, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 64802 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "380186f4", + "pi_parent_id": "1d5ac28f", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RvtPHNsM3pNhmXV9KIepUkHt", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 57, + "timestamp": "2026-10-21T12:17:53.911Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`Usd.Relationship` has AddTarget/SetTargets \u2014 this is a modern OpenUSD. In other words, this pxr is recent, but has some old-style/missing APIs (no Vt.Value, no CreateUsdPreviewSurface, no layer.usdUnits...). Strange mixed build. Oh well \u2014 I'll adapt:\n\n```python\nrel = mesh.GetPrim().CreateRelationship([\"material\", \"binding\"])\nrel.AddTarget(\"/Tray/TrayMaterial\")\n```\n\nReplace mat.Bind(mesh) with this. Also, the use of Sdf.ValueTypeNames in CreateInput worked (the error went past that line).", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-bba03c1c24ee3202", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nsrc = src.replace('mat.Bind(mesh)',\n'''mesh.GetPrim().CreateRelationship([\"material\", \"binding\"]).AddTarget(\"/Tray/TrayMaterial\")''')\nopen('build_asset.py','w').write(src)\nEOF\npython3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-bba03c1c24ee3202", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 241, in \n UsdPhysics.RigidBodyAPI.Apply(root)\nBoost.Python.ArgumentError: Python argument types in\n RigidBodyAPI.Apply(Xform)\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": 65208, + "completion_tokens": 236, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 65444 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "aba9d8c2", + "pi_parent_id": "b796e6ab", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RCuqAlT5T3yuAAkzNAOl3U5Z", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 58, + "timestamp": "2026-10-21T12:18:10.650Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Apply takes a UsdPrim: `UsdPhysics.RigidBodyAPI.Apply(root.GetPrim())`. Same for MassAPI.Apply and MeshCollisionAPI.Apply \u2014 pass .GetPrim(). Let's fix them all.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-b56f942ccccad945", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && sed -i 's/UsdPhysics.RigidBodyAPI.Apply(root)/UsdPhysics.RigidBodyAPI.Apply(root.GetPrim())/; s/UsdPhysics.MeshCollisionAPI.Apply(mesh)/UsdPhysics.MeshCollisionAPI.Apply(mesh.GetPrim())/; s/UsdPhysics.MassAPI.Apply(root)/UsdPhysics.MassAPI.Apply(root.GetPrim())/' build_asset.py && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-b56f942ccccad945", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 244, in \n col.CreateCollisionEnabledAttr(True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'MeshCollisionAPI' object has no attribute 'CreateCollisionEnabledAttr'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 65559, + "completion_tokens": 158, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 65717 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "70647005", + "pi_parent_id": "2468655b", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RUy6Z3c3vtcX0yjpfvRNhXHH", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 59, + "timestamp": "2026-10-21T12:18:40.572Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "MeshCollisionAPI in this build: let me check its attributes. UsdPhysics.CollisionAPI has \"collisionEnabled\" (CreateCollisionEnabledAttr) and \"approximation\" (CreateApproximationAttr). MeshCollisionAPI is a specialized version, and the approximation values are box/sphere/capsule/convexHull/mesh. In some versions, the approximation attribute on MeshCollisionAPI is \"CreateApproximationAttr\". Let me try introspecting.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a5a8fb9d4e1a8b20", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 -c \"\nfrom pxr import UsdPhysics\nprint([a for a in dir(UsdPhysics.MeshCollisionAPI) if 'Create' in a or 'Approx' in a or 'Enabled' in a])\nprint([a for a in dir(UsdPhysics.CollisionAPI) if 'Create' in a])\"" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a5a8fb9d4e1a8b20", + "content": "['CreateApproximationAttr', 'GetApproximationAttr']\n['CreateCollisionEnabledAttr', 'CreateSimulationOwnerRel']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 65806, + "completion_tokens": 196, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 66002 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "8c82dec3", + "pi_parent_id": "6d7772bf", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RtNKRi9z2C283wwcyAtNnFJj", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 60, + "timestamp": "2026-10-21T12:19:05.697Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "MeshCollisionAPI has CreateApproximationAttr, but no CreateCollisionEnabledAttr (that's on the base CollisionAPI \u2014 apply both APIs? Actually, MeshCollisionAPI is a refinement of CollisionAPI, and the collisionEnabled attribute is defined by CollisionAPI. In this build, the schema class might not be exposing it via the subclass's Python wrapper). The simplest approach: also apply UsdPhysics.CollisionAPI to the mesh and create the enabled attribute:\n\n```python\nUsdPhysics.CollisionAPI.Apply(mesh.GetPrim())\ncol = UsdPhysics.MeshCollisionAPI.Apply(mesh.GetPrim())\ncol.CreateApproximationAttr(\"convexHull\")\nUsdPhysics.CollisionAPI.Get(mesh.GetPrim()).CreateCollisionEnabledAttr(True)\n```\n\nHmm \u2014 does applying both APIs conflict? MeshCollisionAPI is a descendant of CollisionAPI (API schema hierarchy), so applying MeshCollisionAPI should automatically apply CollisionAPI's attributes as well... In the newer API, only the leaf is applied. To be safe, apply CollisionAPI first, then apply MeshCollisionAPI, and then set collisionEnabled via the CollisionAPI wrapper.\n\nActually, wait: if you apply both, do the API specifications get written twice (collisionAPI + meshCollisionAPI)? That's normal (the hierarchy is recorded). No problem.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a23997e3797b34e9", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nsrc = src.replace('''col = UsdPhysics.MeshCollisionAPI.Apply(mesh.GetPrim())\ncol.CreateApproximationAttr(\"convexHull\")\ncol.CreateCollisionEnabledAttr(True)''',\n'''UsdPhysics.CollisionAPI.Apply(mesh.GetPrim())\ncol = UsdPhysics.MeshCollisionAPI.Apply(mesh.GetPrim())\ncol.CreateApproximationAttr(\"convexHull\")\nUsdPhysics.CollisionAPI.Get(mesh.GetPrim()).CreateCollisionEnabledAttr(True)''')\nopen('build_asset.py','w').write(src)\nEOF\npython3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a23997e3797b34e9", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 245, in \n UsdPhysics.CollisionAPI.Get(mesh.GetPrim()).CreateCollisionEnabledAttr(True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nBoost.Python.ArgumentError: Python argument types in\n CollisionAPI.Get(Prim)\ndid not match C++ signature:\n Get(pxrInternal_v0_26_8__pxrReserved__::TfWeakPtr stage, pxrInternal_v0_26_8__pxrReserved__::SdfPath path)\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 66042, + "completion_tokens": 434, + "cached_tokens": 65856, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 66476 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "7c7ec8b1", + "pi_parent_id": "523a8782", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R7GfuUY8TsU6D2YXbJL3Gdwh", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 61, + "timestamp": "2026-10-21T12:19:26.968Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`CollisionAPI.Get(stage, path)`. Use `UsdPhysics.CollisionAPI.Get(stage, mesh.GetPrim().GetPath())`. Or, more simply, get the attribute directly: `mesh.GetPrim().GetAttribute(\"physics:collisionEnabled\").Set(True)`? But to write that attribute, the schema needs to be defined... Actually, if CollisionAPI has already been applied, then the `physics:collisionEnabled` attribute exists, and `.Set(True)` works on a bool attribute (single value \u2014 in this build, bool array setting was broken, but a single bool should be fine).\n\nI'll go with `mesh.GetPrim().GetAttribute(\"physics:collisionEnabled\").Set(True)`.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-8b6f202839e9ddf4", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nsrc = src.replace('UsdPhysics.CollisionAPI.Get(mesh.GetPrim()).CreateCollisionEnabledAttr(True)',\n 'mesh.GetPrim().GetAttribute(\"physics:collisionEnabled\").Set(True)')\nopen('build_asset.py','w').write(src)\nEOF\npython3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-8b6f202839e9ddf4", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 252, in \n lines = open(OUT_USD).read().splitlines(keepends=True)\n ^^^^^^^^^^^^^^^^^^^^\n File \"\", line 322, in decode\nUnicodeDecodeError: 'utf-8' codec can't decode byte 0x8d in position 319: invalid start byte\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 66655, + "completion_tokens": 255, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 66910 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "a7bc40bb", + "pi_parent_id": "8dedea83", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RfBDohyLlQ27v40QpZQrq34o", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 62, + "timestamp": "2026-10-21T12:19:51.930Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "The exported USD file is binary (because this build exports as .usd binary by default?). Sdf.Export / stage.Export might use the \"usda\" format by default? It wrote out a binary file. I should export as text: `Sdf.FileFormat.FindFormatByName(\"usda\")` and stage.Export(OUT_USD, fileFormat). Or set the format:\n\n```python\nfmt = Sdf.FileFormat.FindFormatByName(\"usda\")\nstage.Export(OUT_USD, fmt)\n```\n\nLet me check whether Usd.Stage.Export in this build supports the fileFormat parameter.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a4dab23595b33e75", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nsrc = src.replace(\"stage.Export(OUT_USD)\\n\",\n \"stage.Export(OUT_USD, Sdf.FileFormat.FindFormatByName(\\\"usda\\\"))\\n\", 1)\nopen('build_asset.py','w').write(src)\nEOF\ngrep -n \"stage.Export\" build_asset.py && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a4dab23595b33e75", + "content": "249:stage.Export(OUT_USD, Sdf.FileFormat.FindFormatByName(\"usda\"))\ntextures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 249, in \n stage.Export(OUT_USD, Sdf.FileFormat.FindFormatByName(\"usda\"))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: type object 'FileFormat' has no attribute 'FindFormatByName'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 67036, + "completion_tokens": 249, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 67285 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "29dc9e93", + "pi_parent_id": "a5d06030", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RevNfEYMfpP4D8Bv2zWau6T6", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 63, + "timestamp": "2026-10-21T12:20:11.625Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "This old API: `Sdf.FileFormat.Find(\"usda\")` or `Sdf.FileFormat.FindFormatByID(\"usda\")`. In the old API: Sdf.FileFormat.Find(formatID). Let me quickly check the methods and then fix it.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-9e63445afd8fcf70", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 -c \"\nfrom pxr import Sdf\nprint([a for a in dir(Sdf.FileFormat) if not a.startswith('_')])\" && python3 - <<'EOF'\nfrom pxr import Sdf\nf = Sdf.FileFormat.Find(\"usda\")\nprint(f)\nEOF" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-9e63445afd8fcf70", + "content": "['CanRead', 'FindAllDerivedFileFormatExtensions', 'FindAllFileFormatExtensions', 'FindByExtension', 'FindById', 'FormatSupportsEditing', 'FormatSupportsReading', 'FormatSupportsWriting', 'GetFileExtension', 'GetFileExtensions', 'IsPackage', 'IsSupportedExtension', 'SupportsEditing', 'SupportsReading', 'SupportsWriting', 'Tokens', 'expired', 'fileCookie', 'formatId', 'primaryFileExtension', 'target']\nTraceback (most recent call last):\n File \"\", line 2, in \nAttributeError: type object 'FileFormat' has no attribute 'Find'\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 67409, + "completion_tokens": 151, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 67560 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "f998b9dc", + "pi_parent_id": "5e790ad1", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-R98AyVHT2PXGrgh9BjNNUu0h", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 64, + "timestamp": "2026-10-21T12:20:51.793Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "`Sdf.FileFormat.FindById(\"usda\")`. Let's try using that.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-94e881e4c000fe3f", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && sed -i 's/Sdf.FileFormat.FindFormatByName(\"usda\")/Sdf.FileFormat.FindById(\"usda\")/' build_asset.py && python3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-94e881e4c000fe3f", + "content": "textures written\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 249, in \n stage.Export(OUT_USD, Sdf.FileFormat.FindById(\"usda\"))\nBoost.Python.ArgumentError: Python argument types in\n Stage.Export(Stage, str, FileFormat)\ndid not match C++ signature:\n Export(pxrInternal_v0_26_8__pxrReserved__::TfWeakPtr, std::__cxx11::basic_string, std::allocator > filename, bool addSourceFileComment=True, pxrInternal_v0_26_8__pxrReserved__::pxr_boost::python::dict args={})\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 67722, + "completion_tokens": 87, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 67809 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "a16193e2", + "pi_parent_id": "cba14102", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-RTS6y6mT0iaKUbYXtpxFThhG", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + }, + { + "step_id": 65, + "timestamp": "2026-10-21T12:21:39.791Z", + "source": "agent", + "model_name": "harbor-endpoint/Qwen/Qwen3.8-27B:deepinfra", + "message": "", + "reasoning_content": "Stage.Export(filename, addSourceFileComment, args dict). The output format is determined by the file extension: \".usd\" \u2192 binary; \".usda\" \u2192 ASCII. So the easiest thing is: export to /output/asset.usda and rename it to asset.usd? No wait \u2014 the extension has to be .usd (the task requires /output/asset.usd). Hmm, the file format is inferred from the extension... but maybe it can be forced via the args dict? The args dict for Export is probably for format-specific arguments (e.g. 'zlibCompressionLevel'?).\n\nAlternative: export to a temp .usda file, read the text, and write it to /output/asset.usd. The #usda header + content is fine inside a .usd file (the format is determined by the content, not by the name).\n\nLet's do that: export to /tmp/asset_export.usda, then copy the text to /output/asset.usd, and do the uv insertion and header modifications on the final file.", + "tool_calls": [ + { + "tool_call_id": "chatcmpl-tool-a73dbfd9a7bc16d4", + "function_name": "bash", + "arguments": { + "command": "cd /tmp && python3 - <<'EOF'\nsrc = open('build_asset.py').read()\nsrc = src.replace('stage.Export(OUT_USD, Sdf.FileFormat.FindById(\"usda\"))',\n 'stage.Export(\"/tmp/_asset_export.usda\")\\nTMP_USD = \"/tmp/_asset_export.usda\"')\n# uv insertion and header fix now operate on TMP_USD, then copy to OUT_USD\nsrc = src.replace('lines = open(OUT_USD).read().splitlines(keepends=True)',\n 'lines = open(TMP_USD).read().splitlines(keepends=True)')\nsrc = src.replace('''lines.insert(end, uv_block)\nopen(OUT_USD, \"w\").write(\"\".join(lines))''',\n'''lines.insert(end, uv_block)\ntext = \"\".join(lines)''')\nsrc = src.replace('''# this OpenUSD build: set upAxis / units via the #usda layer header\nhdr = open(OUT_USD).read().splitlines()[0]\nassert hdr.startswith(\"#usda\"), hdr\nopen(OUT_USD, \"w\").write(hdr + \" (upAxis = Z) (metersPerUnit = 1.0)\" + open(OUT_USD).read().split(\"\\\\n\", 1)[1])\nprint(\"exported\", OUT_USD)''',\n'''# this OpenUSD build: set upAxis / units via the #usda layer header\nlines = text.splitlines(keepends=True)\nassert lines[0].startswith(\"#usda\"), lines[0]\nlines[0] = lines[0].rstrip(\"\\\\n\") + \" (upAxis = Z) (metersPerUnit = 1.0)\\\\n\"\nopen(OUT_USD, \"w\").write(\"\".join(lines))\nprint(\"exported\", OUT_USD)''')\nopen('build_asset.py','w').write(src)\nEOF\npython3 build_asset.py" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "chatcmpl-tool-a73dbfd9a7bc16d4", + "content": "textures written\nexported /output/asset.usd\nTraceback (most recent call last):\n File \"/tmp/build_asset.py\", line 272, in \n bb = UsdGeom.BBoxCache(Usd.TimeCode.Default()).ComputeWorldBound(root)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nBoost.Python.ArgumentError: Python argument types in\n BBoxCache.__init__(BBoxCache, TimeCode)\ndid not match C++ signature:\n __init__(_object*, pxrInternal_v0_26_8__pxrReserved__::UsdTimeCode time, std::vector > includedPurposes)\n __init__(_object*, pxrInternal_v0_26_8__pxrReserved__::UsdTimeCode time, std::vector > includedPurposes, bool useExtentsHint)\n __init__(_object*, pxrInternal_v0_26_8__pxrReserved__::UsdTimeCode time, std::vector > includedPurposes, bool useExtentsHint, bool ignoreVisibility)\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 68012, + "completion_tokens": 676, + "cached_tokens": 1568, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 68688 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "f2b80afc", + "pi_parent_id": "67ef4d28", + "provider": "harbor-endpoint", + "api": "openai-completions", + "response_id": "chatcmpl-Rjc1d9XJVYQQuctenLCJvOCp", + "response_model": "Qwen/Qwen3.8-27B", + "stop_reason": "toolUse", + "raw_stop_reason": "tool_calls" + } + } + ], + "final_metrics": { + "total_prompt_tokens": 2978912, + "total_completion_tokens": 52106, + "total_cached_tokens": 601328, + "total_steps": 65, + "extra": { + "cache_write_tokens": 0, + "reasoning_tokens": 0, + "pi_total_tokens": 3031018 + } + } +} \ No newline at end of file diff --git a/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/report.html b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/report.html index bd0d40ce573495950b6b70d727e7c48c40b138c2..12371f4a45947e7e2ced0d9b5c6a25533d02f8a6 100644 --- a/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/report.html +++ b/runs/nv25-qwen38-27b-xhigh-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.

3 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)
food_prepared_croissant_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% (0/20480)64001721.081641.382608819421362650955Not measuredNot measuredNot measured
food_prepared_croissant_a01Qwen/Qwen3.8-27B:deepinfrapinone2completed0% (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)64002450.912373.53Not measuredNot measuredNot measuredNot measuredNot measuredNot measured
food_produce_banana_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% (0/20480)64001735.641688.142510638432982553936Not measuredNot measuredNot measured

food_prepared_croissant_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% (0/20480)

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)
64001721.081641.382608819421362650955Not 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)
0020482.61Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_food_prepared_croissant_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_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

food_prepared_croissant_a01 / Qwen/Qwen3.8-27B:deepinfra / repetition 2

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone2completed
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)
64002450.912373.53Not measuredNot measuredNot measuredNot 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)
0020482.61Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_food_prepared_croissant_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:diffuseColor0/20480
visual:metallic0/20480
visual:opacity0/20480
visual:roughness0/20480

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 (%)
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)
64001735.641688.142510638432982553936Not 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)
0020483.02Not 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_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

\ 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.

4 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:deepinfrapinone2completed0% (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)64002435.652252.052978912521063031018Not measuredNot measuredNot measured
food_prepared_croissant_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% (0/20480)64001721.081641.382608819421362650955Not measuredNot measuredNot measured
food_prepared_croissant_a01Qwen/Qwen3.8-27B:deepinfrapinone2completed0% (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)64002450.912373.53Not measuredNot measuredNot measuredNot measuredNot measuredNot measured
food_produce_banana_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% (0/20480)64001735.641688.142510638432982553936Not measuredNot measuredNot measured

box_cardboard_open_a01 / Qwen/Qwen3.8-27B:deepinfra / repetition 2

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone2completed
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)
64002435.652252.052978912521063031018Not 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_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

food_prepared_croissant_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% (0/20480)

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)
64001721.081641.382608819421362650955Not 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)
0020482.61Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_food_prepared_croissant_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_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

food_prepared_croissant_a01 / Qwen/Qwen3.8-27B:deepinfra / repetition 2

ModelHarnessSkillsRepetitionStatus
Qwen/Qwen3.8-27B:deepinfrapinone2completed
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)
64002450.912373.53Not measuredNot measuredNot measuredNot 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)
0020482.61Not measuredNot measured

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_food_prepared_croissant_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:diffuseColor0/20480
visual:metallic0/20480
visual:opacity0/20480
visual:roughness0/20480

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 (%)
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)
64001735.641688.142510638432982553936Not 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)
0020483.02Not 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_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

\ No newline at end of file diff --git a/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/results.json b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/results.json index df2e3b3596ff367fbd890e4e358fa6f4c6837199..f9402b22e078da978e4901cc9476423339eff13d 100644 --- a/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/results.json +++ b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/results.json @@ -1,7 +1,7 @@ { "schema": "simready-bench-portable-report-v1", "planned_conditions": 100, - "completed_conditions": 3, + "completed_conditions": 4, "partial": true, "settings": { "reference_assets": [ @@ -96,6 +96,1889 @@ } }, "results": [ + { + "condition_id": "condition-bf7d48bdde34ac9b89923e9ca2bff27f5e38460080eb7aa29147643bc5986f58", + "asset_id": "box_cardboard_open_a01", + "requested_model": "Qwen/Qwen3.8-27B:deepinfra", + "harness_id": "pi", + "skill_set_id": "none", + "repetition": 1, + "status": "completed", + "formal_fidelity_percent": 0.0, + "visual_fidelity_percent": 0.0, 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a/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/scores.csv b/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/scores.csv index a3c48ad2ff55bcd72cee005b05ce3f09a4858527..27b0b1c455a33057171b0ebd341154a0e8667844 100644 --- a/runs/nv25-qwen38-27b-xhigh-pi-none-turn64-20261021/scores.csv +++ b/runs/nv25-qwen38-27b-xhigh-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-bf7d48bdde34ac9b89923e9ca2bff27f5e38460080eb7aa29147643bc5986f58,box_cardboard_open_a01,Qwen/Qwen3.8-27B:deepinfra,pi,none,2,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,0.0,0.0,2048,0.0,0.0,0,4,0,0,64,0,0,2435.652080821106,2252.051037824014,2978912,52106,3031018,,, condition-340eb673d4cbc4f10bfc2822a15e3aa1e7afbe84e096605fa44db2fb5acdbeae,food_prepared_croissant_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,,0.0,1.0,1.0,0,0.8333333333333334,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,0,20480,20480,measured,0.0,0.0,2048,0.0,0.0,0,4,0,0,64,0,0,1721.078792958986,1641.3776718529407,2608819,42136,2650955,,, 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