{"schema_version":"ATIF-v1.8","session_id":"ab836b20-8c39-4d4a-99a8-43b561fa9eb1","agent":{"name":"computer-1","version":"1.1.0","model_name":"openai/gpt-6-astra"},"steps":[{"step_id":1,"timestamp":"2026-09-26T02:19:33.412586+00:00","source":"user","message":"# Stone Guardian boxing practice\n\nRig the supplied unrigged character and recreate the performance shown in the\nreference previews in `reference/`. Create the skeleton, skin binding and finger articulation\nyourself. Do not substitute another mesh or bake a movie/mesh cache in place of\nan editable rig. Supplied topology, rest vertices, UVs and materials must remain\nunchanged; adding armatures, weights, controllers, lights and cameras is allowed.\n\nOpen `/home/ubuntu/task/input/base.blend`. The motion and geometric tolerance\ncontract is `/home/ubuntu/task/input/motion-contract.json`; the cameras are in\n`camera-manifest.json`. The video camera follows the public world-space centers\nin `reference-camera-path.json`; preserve the character's world-space travel,\nnot just its position in the moving image. Use frames 1–166 at 24 fps. Required phases:\n- Frames 1–166: Boxing practice.\n\nKeep the spine anatomically centered and connected from pelvis to head. Place\nlimb joints inside the corresponding anatomy. Skin the whole character,\nincluding both hands: fingers must curl toward the palm, wrists must align with\nthe forearms during strikes, and guard poses must have the reference hand shape.\nAvoid tears, stretched cuffs, collapsed joints, detached fingers, skating and\nabrupt transitions. Preserve the supplied world orientation and scale.\n\nBone names/counts, bone roll, IK/FK and native constraints/NLA are your choice.\nLabel the skeleton's semantic landmarks using each Bone's string custom property\n`bb_landmarks`: a JSON object mapping a name in `landmarks` to a fraction along\nthe bone (0=head, 1=tail). Example: `{\"left_elbow\": 0.0}`. Every required label\nmust appear exactly once; multiple labels may share a bone. This annotation is\nonly a correspondence map, not a substitute for working deformation.\n\nThe grader checks rest landmarks and spine geometry without rotational fitting,\nnormalized weight coverage and causal mesh deformation, posed edge strain,\nordered body/finger motion (up to 3 frames of timing variation within each\nphase), and frame-to-frame continuity. The worst named phase determines body\nand hand credit. Numeric tolerances/weights are in the public contract, with\nnumeric gates and caps in `grading-policy.json`; paired\nvisual checks contribute 20% for reference fidelity and deformation quality.\nSevere missing/broken rigging caps the total; a missing rig scores zero.\nSampled geometry and eight visual checkpoints do not certify every collision.\n\nSave `/home/ubuntu/task/output/submission/result.blend`. Rendering a video is\nnot a scored delivery requirement for these tasks; the verifier renders evidence\nfrom the saved scene. Keep the interactive Blender process alive while using\nbackground helpers; stop only background process IDs you started.\n"},{"step_id":2,"timestamp":"2026-09-26T02:19:37.124783+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_0_1","function_name":"bash_command","arguments":{"command":"ls -lh /home/ubuntu/task/input; ls -lh /home/ubuntu/task/reference; cat /home/ubuntu/task/input/motion-contract.json","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_0_1","content":"{\"exit_code\":0,\"stdout\":\"lrwxrwxrwx 1 ubuntu ubuntu 4 Sep 26 02:19 /home/ubuntu/task/input -> /app\\n{\\n  \\\"engine_timeout_seconds\\\": 3600,\\n  \\\"evidence_views\\\": [\\n    \\\"checkpoint-1\\\",\\n    \\\"checkpoint-2\\\",\\n    \\\"checkpoint-3\\\",\\n    \\\"checkpoint-4\\\",\\n    \\\"checkpoint-5\\\",\\n    \\\"checkpoint-6\\\",\\n    \\\"checkpoint-7\\\",\\n    \\\"checkpoint-8\\\"\\n  ],\\n  \\\"fps\\\": 24,\\n  \\\"frame_end\\\": 166,\\n  \\\"frame_start\\\": 1,\\n  \\\"hand_landmarks\\\": {\\n    \\\"left\\\": {\\n      \\\"digits\\\": [\\n        \\\"left_index_base\\\",\\n        \\\"left_index_tip\\\",\\n        \\\"left_middle_base\\\",\\n        \\\"left_middle_tip\\\",\\n        \\\"left_ring_base\\\",\\n        \\\"left_ring_tip\\\",\\n        \\\"left_pinky_base\\\",\\n        \\\"left_pinky_tip\\\",\\n        \\\"left_thumb_base\\\",\\n        \\\"left_thumb_tip\\\"\\n      ],\\n      \\\"wrist\\\": \\\"left_wrist\\\"\\n    },\\n    \\\"right\\\": {\\n      \\\"digits\\\": [\\n        \\\"right_index_base\\\",\\n        \\\"right_index_tip\\\",\\n        \\\"right_middle_base\\\",\\n        \\\"right_middle_tip\\\",\\n        \\\"right_ring_base\\\",\\n        \\\"right_ring_tip\\\",\\n        \\\"right_pinky_base\\\",\\n        \\\"right_pinky_tip\\\",\\n        \\\"right_thumb_base\\\",\\n        \\\"right_thumb_tip\\\"\\n      ],\\n      \\\"wrist\\\": \\\"right_wrist\\\"\\n    }\\n  },\\n  \\\"landmarks\\\": [\\n    \\\"pelvis\\\",\\n    \\\"lower_spine\\\",\\n    \\\"chest\\\",\\n    \\\"neck\\\",\\n    \\\"head\\\",\\n    \\\"left_shoulder\\\",\\n    \\\"left_elbow\\\",\\n    \\\"left_wrist\\\",\\n    \\\"left_hip\\\",\\n    \\\"left_knee\\\",\\n    \\\"left_ankle\\\",\\n    \\\"left_toe\\\",\\n    \\\"left_index_base\\\",\\n    \\\"left_index_tip\\\",\\n    \\\"left_middle_base\\\",\\n    \\\"left_middle_tip\\\",\\n    \\\"left_ring_base\\\",\\n    \\\"left_ring_tip\\\",\\n    \\\"left_pinky_base\\\",\\n    \\\"left_pinky_tip\\\",\\n    \\\"left_thumb_base\\\",\\n    \\\"left_thumb_tip\\\",\\n    \\\"right_shoulder\\\",\\n    \\\"right_elbow\\\",\\n    \\\"right_wrist\\\",\\n    \\\"right_hip\\\",\\n    \\\"right_knee\\\",\\n    \\\"right_ankle\\\",\\n    \\\"right_toe\\\",\\n    \\\"right_index_base\\\",\\n    \\\"right_index_tip\\\",\\n    \\\"right_middle_base\\\",\\n    \\\"right_middle_tip\\\",\\n    \\\"right_ring_base\\\",\\n    \\\"right_ring_tip\\\",\\n    \\\"right_pinky_base\\\",\\n    \\\"right_pinky_tip\\\",\\n    \\\"right_thumb_base\\\",\\n    \\\"right_thumb_tip\\\"\\n  ],\\n  \\\"mesh_objects\\\": [\\n    \\\"CharacterMesh00\\\",\\n    \\\"CharacterMesh01\\\",\\n    \\\"CharacterMesh02\\\"\\n  ],\\n  \\\"metric_weights\\\": {\\n    \\\"body_motion\\\": 0.15,\\n    \\\"deformation_integrity\\\": 0.2,\\n    \\\"hand_motion\\\": 0.15,\\n    \\\"rest_joint_alignment\\\": 0.15,\\n    \\\"skin_binding\\\": 0.1,\\n    \\\"spine_alignment\\\": 0.15,\\n    \\\"temporal_continuity\\\": 0.1\\n  },\\n  \\\"sample_step\\\": 4,\\n  \\\"segments\\\": [\\n    {\\n      \\\"end\\\": 166,\\n      \\\"name\\\": \\\"Boxing practice\\\",\\n      \\\"start\\\": 1\\n    }\\n  ],\\n  \\\"spine_landmarks\\\": [\\n    \\\"pelvis\\\",\\n    \\\"lower_spine\\\",\\n    \\\"chest\\\",\\n    \\\"neck\\\",\\n    \\\"head\\\"\\n  ],\\n  \\\"timing_window_frames\\\": 3,\\n  \\\"tolerances\\\": {\\n    \\\"finger_hand_length\\\": 0.2,\\n    \\\"joint_height\\\": 0.03,\\n    \\\"motion_height\\\": 0.05,\\n    \\\"spine_height\\\": 0.02,\\n    \\\"strain_excess\\\": 0.25,\\n    \\\"velocity_height\\\": 0.015\\n  }\\n}\\n\",\"stderr\":\"ls: cannot access '/home/ubuntu/task/reference': No such file or directory\\n\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":1489,"completion_tokens":54}},{"step_id":3,"timestamp":"2026-09-26T02:19:42.959809+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_1_1","function_name":"bash_command","arguments":{"command":"ls -lah /app; find /app/reference -maxdepth 2 -type f; cat /app/camera-manifest.json; cat /app/reference-camera-path.json | head -c 2500; which blender","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_1_1","content":"{\"exit_code\":0,\"stdout\":\"total 16M\\ndrwxr-xr-x 1 ubuntu ubuntu  186 Sep 26 02:18 .\\ndrwxr-xr-x 1 root   root    103 Sep 26 02:19 ..\\n-rw-rw-r-- 1 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},\\n  \\\"schema_version\\\": 1,\\n  \\\"views\\\": [\\n    {\\n      \\\"clip\\\": [\\n        0.001,\\n        271.7780113220215\\n      ],\\n      \\\"frame\\\": 1,\\n      \\\"id\\\": \\\"checkpoint-1\\\",\\n      \\\"location\\\": [\\n        0.67594975233078,\\n        -7.33988094329834,\\n        2.1492362022399902\\n      ],\\n      \\\"ortho_scale\\\": 2.342172861099243,\\n      \\\"projection\\\": \\\"ORTHO\\\",\\n      \\\"resolution\\\": [\\n        480,\\n        480\\n      ],\\n      \\\"rotation_quaternion\\\": [\\n        0.7577401995658875,\\n        0.652556300163269,\\n        0.0,\\n        0.0\\n      ]\\n    },\\n    {\\n      \\\"clip\\\": [\\n        0.001,\\n        271.7780113220215\\n      ],\\n      \\\"frame\\\": 25,\\n      \\\"id\\\": \\\"checkpoint-2\\\",\\n      \\\"location\\\": [\\n        4.933644771575928,\\n        -7.604189872741699,\\n        2.1387205123901367\\n      ],\\n      \\\"ortho_scale\\\": 2.342172861099243,\\n      \\\"projection\\\": \\\"ORTHO\\\",\\n      \\\"resolution\\\": [\\n        480,\\n        480\\n      ],\\n      \\\"rotation_quaternion\\\": [\\n        0.7325186133384705,\\n        0.6408043503761292,\\n        0.1512734442949295,\\n        0.1729241907596588\\n      ]\\n    },\\n    {\\n      \\\"clip\\\": [\\n        0.001,\\n        271.7780113220215\\n      ],\\n      \\\"frame\\\": 48,\\n      \\\"id\\\": \\\"checkpoint-3\\\",\\n      \\\"location\\\": [\\n        1.394776701927185,\\n        -8.109886169433594,\\n        2.172976016998291\\n      ],\\n      \\\"ortho_scale\\\": 2.342172861099243,\\n      \\\"projection\\\": \\\"ORTHO\\\",\\n      \\\"resolution\\\": [\\n        480,\\n        480\\n      ],\\n      \\\"rotation_quaternion\\\": [\\n        0.7577401995658875,\\n        0.652556300163269,\\n        0.0,\\n        0.0\\n      ]\\n    },\\n    {\\n      \\\"clip\\\": [\\n        0.001,\\n        271.7780113220215\\n      ],\\n      \\\"frame\\\": 72,\\n      \\\"id\\\": \\\"checkpoint-4\\\",\\n      \\\"location\\\": [\\n        4.713891506195068,\\n        -8.015606880187988,\\n        2.1229567527770996\\n      ],\\n      \\\"ortho_scale\\\": 2.342172861099243,\\n      \\\"projection\\\": \\\"ORTHO\\\",\\n      \\\"resolution\\\": [\\n        480,\\n        480\\n      ],\\n      \\\"rotation_quaternion\\\": [\\n        0.7325186133384705,\\n        0.6408043503761292,\\n        0.1512734442949295,\\n        0.1729241907596588\\n      ]\\n    },\\n    {\\n      \\\"clip\\\": [\\n        0.001,\\n        271.7780113220215\\n      ],\\n      \\\"frame\\\": 95,\\n      \\\"id\\\": \\\"checkpoint-5\\\",\\n      \\\"location\\\": [\\n        -0.0010450780391693115,\\n        -8.103156089782715,\\n        2.0522689819335938\\n      ],\\n      \\\"ortho_scale\\\": 2.342172861099243,\\n      \\\"projection\\\": \\\"ORTHO\\\",\\n      \\\"resolution\\\": [\\n        480,\\n        480\\n      ],\\n      \\\"rotation_quaternion\\\": [\\n        0.7577401995658875,\\n        0.6525562405586243,\\n        0.0,\\n        0.0\\n      ]\\n    },\\n    {\\n      \\\"clip\\\": [\\n        0.001,\\n        271.7780113220215\\n      ],\\n      \\\"frame\\\": 119,\\n      \\\"id\\\": \\\"checkpoint-6\\\",\\n      \\\"location\\\": [\\n        4.041184425354004,\\n        -7.635332107543945,\\n        2.193326711654663\\n      ],\\n      \\\"ortho_scale\\\": 2.342172861099243,\\n      \\\"projection\\\": \\\"ORTHO\\\",\\n      \\\"resolution\\\": [\\n        480,\\n        480\\n      ],\\n      \\\"rotation_quaternion\\\": [\\n        0.7325186133384705,\\n        0.6408043503761292,\\n        0.1512734442949295,\\n        0.1729241907596588\\n      ]\\n    },\\n    {\\n      \\\"clip\\\": [\\n        0.001,\\n        271.7780113220215\\n      ],\\n      \\\"frame\\\": 142,\\n      \\\"id\\\": \\\"checkpoint-7\\\",\\n      \\\"location\\\": [\\n        0.16037937998771667,\\n        -7.419150352478027,\\n        2.165318489074707\\n    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/usr/local/bin/blender\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":2556,"completion_tokens":68,"cached_tokens":1486}},{"step_id":4,"timestamp":"2026-09-26T02:19:47.500915+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep2.webp"}}]}]},"metrics":{"prompt_tokens":5781,"completion_tokens":18,"cached_tokens":2553}},{"step_id":5,"timestamp":"2026-09-26T02:19:58.923532+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_3_1","function_name":"bash_command","arguments":{"command":"cat > /tmp/inspect.py <<'PY'\nimport bpy,json\nfrom mathutils import Vector\nbpy.ops.wm.open_mainfile(filepath='/app/base.blend')\nr={}\nfor o in bpy.data.objects:\n print(o.name,o.type, list(o.location),list(o.rotation_euler),list(o.scale))\n if o.type=='MESH':\n  vs=[list(o.matrix_world@v.co) for v in o.data.vertices]\n  r[o.name]={'vertices':vs,'edges':[list(e.vertices) for e in o.data.edges]}\n  print(len(vs), 'bounds',[(min(v[i] for v in vs),max(v[i] for v in vs)) for i in range(3)])\njson.dump(r,open('/tmp/mesh.json','w'))\nPY\nblender -b -t 2 --python /tmp/inspect.py > /tmp/inspect.log 2>&1\ncat /tmp/inspect.log\npython - <<'PY'\nfrom PIL import Image,ImageDraw\nim=Image.new('RGB',(1920,960),'white')\nfor i in range(8):\n a=Image.open(f'/app/reference/reference-checkpoint-{i+1}.png'); a.thumbnail((480,480)); im.paste(a,((i%4)*480,(i//4)*480))\nim.save('/tmp/refs.png')\nPY","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_3_1","content":"{\"exit_code\":127,\"stdout\":\"CharacterMesh00 MESH [0.0, 0.0, 0.0] [0.0, -0.0, 0.0] [0.009999999776482582, 0.009999999776482582, 0.009999999776482582]\\n16610 bounds [(0.525431215763092, 0.7201094031333923), (-0.22965526580810547, -0.03701799362897873), (0.9804205298423767, 1.1398950815200806)]\\nCharacterMesh01 MESH [0.0, 0.0, 0.0] [0.0, -0.0, 0.0] [0.009999999776482582, 0.009999999776482582, 0.009999999776482582]\\n16610 bounds [(-0.7147770524024963, -0.5191085934638977), (-0.22585956752300262, -0.03373422846198082), (0.9815601110458374, 1.1453746557235718)]\\nCharacterMesh02 MESH [0.0, 0.0, 0.0] [0.0, 0.0, 0.0] [0.009999999776482582, 0.009999999776482582, 0.009999999776482582]\\n54405 bounds [(-0.6079485416412354, 0.6150609254837036), (-0.1642378717660904, 0.2838393449783325), (-3.625962108344538e-07, 1.8000000715255737)]\\nBlender 4.5.12 LTS (hash 84afd5f785f7 built 2026-07-21 01:31:22)\\nRead blend: \\\"/app/base.blend\\\"\\n\\nBlender quit\\n\",\"stderr\":\"/tmp/computer-1-bash.2YhD2r/command.sh: line 16: python: command not found\\n\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":7375,"completion_tokens":357,"cached_tokens":5778}},{"step_id":6,"timestamp":"2026-09-26T02:20:04.493304+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_4_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nfrom PIL import Image\nim=Image.new('RGB',(1920,960),'white')\nfor i in range(8):\n a=Image.open(f'/app/reference/reference-checkpoint-{i+1}.png'); a.thumbnail((480,480)); im.paste(a,((i%4)*480,(i//4)*480))\nim.resize((1440,720)).save('/tmp/refs.png')\nPY\nxdg-open /tmp/refs.png","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_4_1","content":"{\"exit_code\":127,\"stdout\":\"\",\"stderr\":\"/tmp/computer-1-bash.j1sBki/command.sh: line 8: xdg-open: command not found\\n\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":8251,"completion_tokens":132,"cached_tokens":7372}},{"step_id":7,"timestamp":"2026-09-26T02:20:09.533065+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_5_1","function_name":"bash_command","arguments":{"command":"which display; display /tmp/refs.png >/tmp/display.log 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_5_1","content":"{\"exit_code\":0,\"stdout\":\"/usr/bin/display\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":8453,"completion_tokens":60,"cached_tokens":8248}},{"step_id":8,"timestamp":"2026-09-26T02:20:14.792256+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep6.webp"}}]}]},"metrics":{"prompt_tokens":8559,"completion_tokens":18,"cached_tokens":8450}},{"step_id":9,"timestamp":"2026-09-26T02:20:19.364861+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_7_1","function_name":"bash_command","arguments":{"command":"cat /tmp/display.log; echo $DISPLAY; pgrep -a display; ls /tmp/.X11-unix","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_7_1","content":"{\"exit_code\":0,\"stdout\":\"display-im6.q16: unable to open X server `' @ error/display.c/DisplayImageCommand/412.\\n\\nX1\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":10153,"completion_tokens":47,"cached_tokens":8556}},{"step_id":10,"timestamp":"2026-09-26T02:20:23.097115+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_8_1","function_name":"bash_command","arguments":{"command":"DISPLAY=:1 display /tmp/refs.png >/tmp/display.log 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_8_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":10269,"completion_tokens":41,"cached_tokens":10150}},{"step_id":11,"timestamp":"2026-09-26T02:20:27.434172+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep9.webp"}}]}]},"metrics":{"prompt_tokens":10350,"completion_tokens":18,"cached_tokens":10266}},{"step_id":12,"timestamp":"2026-09-26T02:20:55.268687+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_10_1","function_name":"bash_command","arguments":{"command":"cat >/tmp/rest.py <<'PY'\nexec(open('/tmp/inspect.py').read().split('r={}')[0])\nfrom mathutils import Vector\nsc=bpy.context.scene\nsc.render.engine='BLENDER_WORKBENCH';sc.display.shading.light='STUDIO';sc.display.shading.color_type='MATERIAL';sc.display.shading.show_shadows=True;sc.display.shading.show_cavity=True\nsc.render.resolution_x=800;sc.render.resolution_y=800;sc.render.resolution_percentage=100\ncam=bpy.data.objects.new('InspectCam',bpy.data.cameras.new('InspectCam'));sc.collection.objects.link(cam);sc.camera=cam;cam.data.type='ORTHO';cam.data.ortho_scale=2.05\nfor name,loc,target,scale in [('front',(0,-5,.9),(0,0,.9),2.05),('side',(5,0,.9),(0,0,.9),2.05),('hand',(.63,-4,1.08),(.63,0,1.08),.3),('handtop',(.63,-.12,4),(.63,-.12,1.07),.3)]:\n cam.location=loc;cam.rotation_euler=(Vector(target)-cam.location).to_track_quat('-Z','Y').to_euler();cam.data.ortho_scale=scale;sc.render.filepath='/tmp/'+name+'.png';bpy.ops.render.render(write_still=True)\nPY\nblender -b -t 4 --python /tmp/rest.py >/tmp/rest.log 2>&1\nffmpeg -v error -i /app/reference/performance.mp4 -vf \"select='not(mod(n,6))',scale=240:240,tile=7x4\" -frames:v 1 /tmp/video.png\nDISPLAY=:1 display /tmp/front.png >/tmp/d2.log 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_10_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":11944,"completion_tokens":470,"cached_tokens":10347}},{"step_id":13,"timestamp":"2026-09-26T02:21:00.514634+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep11.webp"}}]}]},"metrics":{"prompt_tokens":12454,"completion_tokens":18,"cached_tokens":11941}},{"step_id":14,"timestamp":"2026-09-26T02:21:12.583996+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_12_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nfrom PIL import Image,ImageDraw\nim=Image.new('RGB',(1400,800),'white')\na=Image.open('/tmp/side.png');im.paste(a.resize((600,600)),(0,0))\nfor i,n in enumerate(['hand','handtop']):im.paste(Image.open('/tmp/'+n+'.png').resize((400,400)),(600+i*400,0))\nim.save('/tmp/detail.png')\na=Image.open('/tmp/video.png');a.resize((1400,800)).save('/tmp/video2.png')\nPY\nDISPLAY=:1 display /tmp/detail.png >/tmp/d3.log 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_12_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":14048,"completion_tokens":267,"cached_tokens":12451}},{"step_id":15,"timestamp":"2026-09-26T02:21:18.232236+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep13.webp"}}]}]},"metrics":{"prompt_tokens":14355,"completion_tokens":18,"cached_tokens":14045}},{"step_id":16,"timestamp":"2026-09-26T02:21:29.035543+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_14_1","function_name":"bash_command","arguments":{"command":"DISPLAY=:1 display /tmp/video2.png >/tmp/d4.log 2>&1 &\npython3 - <<'PY'\nimport numpy, scipy, matplotlib\nprint('yes')\nPY","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_14_1","content":"{\"exit_code\":1,\"stdout\":\"\",\"stderr\":\"Traceback (most recent call last):\\n  File \\\"<stdin>\\\", line 1, in <module>\\nModuleNotFoundError: No module named 'scipy'\\n\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":15949,"completion_tokens":93,"cached_tokens":14352}},{"step_id":17,"timestamp":"2026-09-26T02:21:35.470123+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep15.webp"}}]}]},"metrics":{"prompt_tokens":16122,"completion_tokens":18,"cached_tokens":15946}},{"step_id":18,"timestamp":"2026-09-26T02:21:43.294127+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_16_1","function_name":"bash_command","arguments":{"command":"tail -50 /app/reference-camera-path.json; cat /app/grading-policy.json; python3 - <<'PY'\nimport matplotlib\nprint('matplotlib yes')\nPY","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_16_1","content":"{\"exit_code\":1,\"stdout\":\"    ],\\n    \\\"91\\\": [\\n      0.015449821949005127,\\n      -0.22200897336006165,\\n      0.9227325916290283\\n    ],\\n    \\\"92\\\": [\\n      0.012539654970169067,\\n      -0.2648574113845825,\\n      0.9113551378250122\\n    ],\\n    \\\"93\\\": [\\n      0.010934025049209595,\\n      -0.2868786156177521,\\n      0.8838368058204651\\n    ],\\n    \\\"94\\\": [\\n      0.008680254220962524,\\n      -0.30159708857536316,\\n      0.8715866208076477\\n    ],\\n    \\\"95\\\": [\\n      -0.0010450780391693115,\\n      -0.2959135174751282,\\n      0.8811824917793274\\n    ],\\n    \\\"96\\\": [\\n      -0.01340782642364502,\\n      -0.2890983819961548,\\n      0.9038798809051514\\n    ],\\n    \\\"97\\\": [\\n      -0.023814916610717773,\\n      -0.28186294436454773,\\n      0.9219175577163696\\n    ],\\n    \\\"98\\\": [\\n      -0.02397817373275757,\\n      -0.25570765137672424,\\n      0.9389939904212952\\n    ],\\n    \\\"99\\\": [\\n      -0.013988912105560303,\\n      -0.2145487517118454,\\n      0.9533064365386963\\n    ]\\n  },\\n  \\\"note\\\": \\\"The reference camera follows these world-space centers; distinguish camera following from character root travel.\\\",\\n  \\\"span\\\": 1.9518107175827026\\n}\\n{\\n  \\\"deterministic_components\\\": {\\n    \\\"body_motion\\\": 0.15,\\n    \\\"deformation_integrity\\\": 0.2,\\n    \\\"hand_motion\\\": 0.15,\\n    \\\"rest_joint_alignment\\\": 0.15,\\n    \\\"skin_binding\\\": 0.1,\\n    \\\"spine_alignment\\\": 0.15,\\n    \\\"temporal_continuity\\\": 0.1\\n  },\\n  \\\"deterministic_weight\\\": 0.8,\\n  \\\"llm_judge_weight\\\": 0.2,\\n  \\\"quality_gates\\\": {\\n    \\\"rest_joint_alignment\\\": {\\n      \\\"minimum\\\": 0.4,\\n      \\\"final_cap\\\": 0.4\\n    },\\n    \\\"spine_alignment\\\": {\\n      \\\"minimum\\\": 0.5,\\n      \\\"final_cap\\\": 0.4\\n    },\\n    \\\"skin_binding\\\": {\\n      \\\"minimum\\\": 0.95,\\n      \\\"final_cap\\\": 0.3\\n    },\\n    \\\"deformation_integrity\\\": {\\n      \\\"minimum\\\": 0.5,\\n      \\\"final_cap\\\": 0.4\\n    },\\n    \\\"body_motion\\\": {\\n      \\\"minimum\\\": 0.4,\\n      \\\"final_cap\\\": 0.4\\n    },\\n    \\\"hand_motion\\\": {\\n      \\\"minimum\\\": 0.4,\\n      \\\"final_cap\\\": 0.6\\n    },\\n    \\\"temporal_continuity\\\": {\\n      \\\"minimum\\\": 0.4,\\n      \\\"final_cap\\\": 0.6\\n    }\\n  },\\n  \\\"score_caps\\\": {\\n    \\\"missing_submission\\\": 0.0,\\n    \\\"corrupt_submission\\\": 0.0,\\n    \\\"missing_functional_rig\\\": 0.0,\\n    \\\"protected_scene_changed\\\": 0.2,\\n    \\\"timeline_contract\\\": 0.6\\n  },\\n  \\\"pass_policy\\\": {\\n    \\\"mode\\\": \\\"raw\\\"\\n  }\\n}\\n\",\"stderr\":\"Traceback (most recent call last):\\n  File \\\"<stdin>\\\", line 1, in <module>\\nModuleNotFoundError: No module named 'matplotlib'\\n\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":17716,"completion_tokens":142,"cached_tokens":16119}},{"step_id":19,"timestamp":"2026-09-26T02:21:57.892985+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_17_1","function_name":"bash_command","arguments":{"command":"cat > /tmp/plot.py <<'PY'\nimport json,numpy as np\nfrom PIL import Image,ImageDraw\nr=json.load(open('/tmp/mesh.json'));v=np.array(r['CharacterMesh00']['vertices'])\nim=Image.new('RGB',(1350,650),'#fff');d=ImageDraw.Draw(im)\nfor j,(ax,ay,title) in enumerate([(0,2,'x z'),(0,1,'x y'),(1,2,'y z')]):\n lo=v[:,[ax,ay]].min(0)-.01;hi=v[:,[ax,ay]].max(0)+.01\n def pix(x,y):return (j*450+30+(x-lo[0])/(hi[0]-lo[0])*390,600-(y-lo[1])/(hi[1]-lo[1])*550)\n for p in v[::3]:\n  x,y=pix(p[ax],p[ay]);d.ellipse((x-1,y-1,x+1,y+1),fill='#777')\n for x in np.arange(np.ceil(lo[0]*100)/100,hi[0],.02):\n  a,b=pix(x,lo[1]);d.line((a,40,a,600),fill='#aaddee');d.text((a-10,610),f'{x:.2f}',fill='black')\n for y in np.arange(np.ceil(lo[1]*100)/100,hi[1],.02):\n  a,b=pix(lo[0],y);d.line((a,b,a+390,b),fill='#aaddee');d.text((a,b),f'{y:.2f}',fill='black')\n d.text((j*450+150,10),title,fill='black')\nim.save('/tmp/handplot.png')\nPY\npython3 /tmp/plot.py\nDISPLAY=:1 display /tmp/handplot.png >/tmp/d5.log 2>&1 &\n/usr/bin/python3 -m pip install scipy --quiet --target /tmp/pylibs","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_17_1","content":"{\"exit_code\":1,\"stdout\":\"\",\"stderr\":\"Traceback (most recent call last):\\n  File \\\"/tmp/plot.py\\\", line 1, in <module>\\n    import json,numpy as np\\n  File \\\"/usr/lib/python3/dist-packages/numpy/__init__.py\\\", line 159, in <module>\\n    from . import ma\\n  File \\\"/usr/lib/python3/dist-packages/numpy/ma/__init__.py\\\", line 42, in <module>\\n    from . import core\\n  File \\\"/usr/lib/python3/dist-packages/numpy/ma/core.py\\\", line 24, in <module>\\n    import inspect\\n  File \\\"/tmp/inspect.py\\\", line 1, in <module>\\n    import bpy,json\\nModuleNotFoundError: No module named 'bpy'\\n/usr/bin/python3: No module named pip\\n\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":18853,"completion_tokens":586,"cached_tokens":17713}},{"step_id":20,"timestamp":"2026-09-26T02:22:02.102195+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_18_1","function_name":"bash_command","arguments":{"command":"mv /tmp/inspect.py /tmp/inspect_bl.py; sed -i s@/tmp/inspect.py@/tmp/inspect_bl.py@ /tmp/rest.py; python3 /tmp/plot.py; DISPLAY=:1 display /tmp/handplot.png >/tmp/d5.log 2>&1 &;","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_18_1","content":"{\"exit_code\":2,\"stdout\":\"\",\"stderr\":\"/tmp/computer-1-bash.bfbmMy/command.sh: line 1: syntax error near unexpected token `;'\\n/tmp/computer-1-bash.bfbmMy/command.sh: line 1: `mv /tmp/inspect.py /tmp/inspect_bl.py; sed -i s@/tmp/inspect.py@/tmp/inspect_bl.py@ /tmp/rest.py; python3 /tmp/plot.py; DISPLAY=:1 display /tmp/handplot.png >/tmp/d5.log 2>&1 &;'\\n\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":19661,"completion_tokens":88,"cached_tokens":18850}},{"step_id":21,"timestamp":"2026-09-26T02:22:07.377788+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_19_1","function_name":"bash_command","arguments":{"command":"mv /tmp/inspect.py /tmp/inspect_bl.py; sed -i s@/tmp/inspect.py@/tmp/inspect_bl.py@ /tmp/rest.py; python3 /tmp/plot.py; DISPLAY=:1 display /tmp/handplot.png >/tmp/d5.log 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_19_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":19906,"completion_tokens":87,"cached_tokens":19658}},{"step_id":22,"timestamp":"2026-09-26T02:22:12.590990+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep20.webp"}}]}]},"metrics":{"prompt_tokens":20033,"completion_tokens":18,"cached_tokens":19903}},{"step_id":23,"timestamp":"2026-09-26T02:22:32.896280+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_21_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nimport numpy as np,json\nv=np.array(json.load(open('/tmp/mesh.json'))['CharacterMesh02']['vertices'])\nfor z in [.15,.57,.98,1.05,1.15,1.3,1.4,1.5,1.56,1.65,1.72]:\n a=v[(abs(v[:,2]-z)<.012)&(abs(v[:,0])<.06)]\n if len(a):print(z,'center bounds',np.min(a,0),np.max(a,0),np.median(a,0))\nPY","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_21_1","content":"{\"exit_code\":0,\"stdout\":\"0.57 center bounds [-0.04605452 -0.10794747  0.5582884 ] [0.02288004 0.23286618 0.58170491] [-0.00450615 -0.087126    0.57038724]\\n0.98 center bounds [-0.04989566 -0.11867701  0.96864539] [0.05848968 0.21958305 0.9918623 ] [ 0.04361363 -0.11125742  0.98454201]\\n1.05 center bounds [-0.05157609 -0.11959336  1.0394088 ] [ 0.05165453 -0.10016582  1.0619477 ] [ 0.01087597 -0.10774712  1.05039918]\\n1.15 center bounds [-0.05420784 -0.15412875  1.139166  ] [0.05968032 0.17366388 1.16160548] [ 0.00155747 -0.1499934   1.15239382]\\n1.3 center bounds [-0.05525847 -0.1031031   1.29241621] [0.05364135 0.21244897 1.31116283] [8.60373839e-05 1.90316871e-01 1.30374444e+00]\\n1.4 center bounds [-0.05827511 -0.16068411  1.38809776] [0.05970679 0.2478061  1.41142619] [-0.01274416 -0.09271839  1.40003389]\\n1.5 center bounds [-0.05956499 -0.16114654  1.48810661] [0.05918669 0.26190683 1.51198792] [ 0.00494859 -0.14329255  1.49921227]\\n1.56 center bounds [-0.05962078 -0.15505083  1.54814529] [0.05950254 0.22957763 1.57153606] [-0.03036882  0.20320043  1.56021082]\\n1.65 center bounds [-0.05914228 -0.15101762  1.63868999] [0.05786655 0.14077295 1.66181409] [0.01663974 0.10373349 1.6503756 ]\\n1.72 center bounds [-0.05949406 -0.14214739  1.70819795] [0.05547687 0.06761039 1.7300235 ] [-6.64352365e-06 -1.31443501e-01  1.72435427e+00]\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":21627,"completion_tokens":739,"cached_tokens":20030}},{"step_id":24,"timestamp":"2026-09-26T02:23:03.350807+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_22_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nimport json\nc=json.load(open('/app/reference-camera-path.json'))['centers']\nfor i in range(1,167,6):print(i,[round(x,3) for x in c[str(i)]])\nPY","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_22_1","content":"{\"exit_code\":0,\"stdout\":\"1 [0.676, 0.467, 0.978]\\n7 [0.677, 0.458, 0.979]\\n13 [0.82, 0.349, 0.921]\\n19 [1.009, 0.295, 0.913]\\n25 [1.03, 0.203, 0.968]\\n31 [1.131, 0.272, 0.888]\\n37 [1.316, -0.024, 0.98]\\n43 [1.441, -0.133, 0.788]\\n49 [1.443, -0.315, 1.002]\\n55 [1.434, -0.327, 1.072]\\n61 [1.362, -0.133, 1.03]\\n67 [1.02, -0.126, 1.046]\\n73 [0.772, -0.174, 0.949]\\n79 [0.491, -0.066, 0.991]\\n85 [0.096, -0.027, 0.798]\\n91 [0.015, -0.222, 0.923]\\n97 [-0.024, -0.282, 0.922]\\n103 [0.12, -0.117, 0.977]\\n109 [0.176, 0.0, 1.021]\\n115 [0.147, 0.142, 1.038]\\n121 [0.138, 0.22, 1.034]\\n127 [0.148, 0.357, 0.971]\\n133 [0.188, 0.294, 1.069]\\n139 [0.178, 0.33, 1.019]\\n145 [0.136, 0.395, 0.959]\\n151 [0.14, 0.308, 1.051]\\n157 [0.157, 0.294, 1.001]\\n163 [0.119, 0.309, 0.969]\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":23081,"completion_tokens":849,"cached_tokens":21624}},{"step_id":25,"timestamp":"2026-09-26T02:23:12.442321+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_23_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nimport json,numpy as np\nv=np.array(json.load(open('/tmp/mesh.json'))['CharacterMesh00']['vertices'])\nfor n,x,z in [('thumbtip',.69,1.126),('indextip',.71,1.054),('middletip',.695,1.022),('ringtip',.663,1.003),('pinkytip',.62,.991),('indexbase',.644,1.083),('middlebase',.62,1.06),('ringbase',.6,1.045),('pinkybase',.574,1.035),('thumbbase',.603,1.119)]:\n a=v[(abs(v[:,0]-x)<.006)&(abs(v[:,2]-z)<.006)];print(n,np.mean(a,0),len(a))\nPY","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_23_1","content":"{\"exit_code\":0,\"stdout\":\"thumbtip [ 0.68992132 -0.12314843  1.12618408] 87\\nindextip [ 0.71040901 -0.1960388   1.05438053] 100\\nmiddletip [ 0.69512809 -0.21533281  1.02208811] 92\\nringtip [ 0.6634086  -0.21275899  1.00350352] 101\\npinkytip [ 0.6201625  -0.19771477  0.99096723] 94\\nindexbase [ 0.64386418 -0.12039886  1.08270452] 95\\nmiddlebase [ 0.61996294 -0.1213331   1.05996098] 90\\nringbase [ 0.60018699 -0.12692841  1.04457827] 90\\npinkybase [ 0.57370941 -0.12185543  1.03524013] 93\\nthumbbase [ 0.60300699 -0.07761688  1.12056835] 165\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":24447,"completion_tokens":267,"cached_tokens":23078}},{"step_id":26,"timestamp":"2026-09-26T02:23:36.001603+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_24_1","function_name":"bash_command","arguments":{"command":"mkdir -p /home/ubuntu/task/output/submission; cat > /tmp/build.py <<'PY'\nimport bpy,math,json,os\nfrom mathutils import Vector,Matrix,Quaternion\nbpy.ops.wm.open_mainfile(filepath='/app/base.blend')\nmeshes=[o for o in bpy.context.scene.objects if o.type=='MESH']\nbpy.ops.object.select_all(action='DESELECT')\narm=bpy.data.armatures.new('Guardian • anatomical skeleton');rig=bpy.data.objects.new('Guardian_Rig',arm);bpy.context.collection.objects.link(rig);rig.select_set(True);bpy.context.view_layer.objects.active=rig\nbpy.ops.object.mode_set(mode='EDIT')\nspec={}\ndef bone(n,h,t,p=None,labels={}):\n b=arm.edit_bones.new(n);b.head=h;b.tail=t\n if p:b.parent=arm.edit_bones[p];b.use_connect=(b.head-b.parent.tail).length<.00001\n spec[n]=(Vector(h),Vector(t),p,labels)\n return b\nbone('pelvis',(0,.065,.98),(0,.065,1.12),labels={'pelvis':0})\nbone('spine',(0,.065,1.12),(0,.07,1.34),'pelvis',{'lower_spine':0})\nbone('chest',(0,.07,1.34),(0,.055,1.52),'spine',{'chest':0})\nbone('neck',(0,.055,1.52),(0,.015,1.64),'chest',{'neck':0})\nbone('head',(0,.015,1.64),(0,.005,1.78),'neck',{'head':0})\nfingers={'index':[(.641,-.12,1.083),(.714,-.2,1.053)],'middle':[(.618,-.128,1.063),(.696,-.218,1.022)],'ring':[(.597,-.132,1.047),(.664,-.214,1.002)],'pinky':[(.574,-.13,1.036),(.622,-.20,.990)],'thumb':[(.6,-.079,1.118),(.701,-.125,1.127)]}\nfor s,side in [(1,'left'),(-1,'right')]:\n def v(x,y,z):return (s*x,y,z)\n bone(side+'_clavicle',(0,.055,1.49),v(.229,.055,1.469),'chest')\n bone(side+'_upperarm',v(.229,.055,1.469),v(.401,.01,1.263),side+'_clavicle',{side+'_shoulder':0})\n bone(side+'_forearm',v(.401,.01,1.263),v(.555,-.066,1.093),side+'_upperarm',{side+'_elbow':0})\n bone(side+'_hand',v(.555,-.066,1.093),v(.62,-.133,1.057),side+'_forearm',{side+'_wrist':0})\n for digit,(a,b) in fingers.items():\n  a=Vector(v(*a));b=Vector(v(*b));pa=side+'_hand'\n  for k,(t0,t1) in enumerate([(0,.44),(.44,.76),(.76,1)]):\n   n=side+'_'+digit+str(k+1);labels={}\n   if k==0:labels[side+'_'+digit+'_base']=0\n   if k==2:labels[side+'_'+digit+'_tip']=1\n   bn=bone(n,a.lerp(b,t0),a.lerp(b,t1),pa,labels)\n   # z axis faces palm, positive local X curls towards palm\n   normal=Vector((-.22*s,-.72,.66));bn.align_roll(normal)\n   pa=n\n bone(side+'_thigh',v(.113,.066,.977),v(.18,.04,.573),'pelvis',{side+'_hip':0})\n bone(side+'_shin',v(.18,.04,.573),v(.214,.068,.154),side+'_thigh',{side+'_knee':0})\n bone(side+'_foot',v(.214,.068,.154),v(.232,-.115,.055),side+'_shin',{side+'_ankle':0,side+'_toe':1})\nbpy.ops.object.mode_set(mode='OBJECT')\nfor n,(h,t,p,labels) in spec.items():\n if labels:arm.bones[n]['bb_landmarks']=json.dumps(labels)\nrig.show_in_front=True;arm.display_type='OCTAHEDRAL'\nfor o in meshes:\n bpy.ops.object.select_all(action='DESELECT');o.select_set(True);rig.select_set(True);bpy.context.view_layer.objects.active=rig\n bpy.ops.object.parent_set(type='ARMATURE_AUTO')\n print('WEIGHTS',o.name,len(o.vertex_groups),sum(not v.groups for v in o.data.vertices),flush=True)\n for m in o.modifiers:\n  if m.type=='ARMATURE':m.use_deform_preserve_volume=True\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/rig.blend')\njson.dump({n:[list(h),list(t),p,lab] for n,(h,t,p,lab) in spec.items()},open('/tmp/bones.json','w'))\nPY\nblender -b -t 4 --python /tmp/build.py >/tmp/build.log 2>&1","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_24_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":25021,"completion_tokens":1292,"cached_tokens":24444}},{"step_id":27,"timestamp":"2026-09-26T02:23:39.568452+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_25_1","function_name":"bash_command","arguments":{"command":"cat /tmp/build.log","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_25_1","content":"{\"exit_code\":0,\"stdout\":\"Warning: Bone Heat Weighting: failed to find solution for one or more bones\\nWEIGHTS CharacterMesh02 49 54405\\nWEIGHTS CharacterMesh00 49 0\\nWEIGHTS CharacterMesh01 49 0\\nInfo: Saved as \\\"rig.blend\\\"\\nBlender 4.5.12 LTS (hash 84afd5f785f7 built 2026-07-21 01:31:22)\\nRead blend: \\\"/app/base.blend\\\"\\n\\nBlender quit\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":26353,"completion_tokens":28,"cached_tokens":25018}},{"step_id":28,"timestamp":"2026-09-26T02:23:49.884012+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_26_1","function_name":"bash_command","arguments":{"command":"cat >/tmp/components.py <<'PY'\nimport json,numpy as np\nr=json.load(open('/tmp/mesh.json'))['CharacterMesh02'];v=np.array(r['vertices']);adj=[[] for a in v]\nfor a,b in r['edges']:adj[a].append(b);adj[b].append(a)\nseen=set();cs=[]\nfor i in range(len(v)):\n if i in seen:continue\n todo=[i];seen.add(i);c=[]\n while todo:\n  j=todo.pop();c.append(j)\n  for k in adj[j]:\n   if k not in seen:seen.add(k);todo.append(k)\n cs.append(c)\ncs.sort(key=len,reverse=True)\nfor i,c in enumerate(cs[:80]):\n a=v[c];print(i,len(c),np.round(a.min(0),3),np.round(a.max(0),3),np.round(a.mean(0),3))\njson.dump(cs,open('/tmp/components.json','w'))\nPY\npython3 /tmp/components.py","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_26_1","content":"{\"exit_code\":0,\"stdout\":\"0 258 [-0.21  -0.069  0.62 ] [-0.011  0.181  0.917] [-0.083  0.069  0.78 ]\\n1 128 [0.016 0.065 0.664] [0.205 0.182 0.915] [0.104 0.147 0.807]\\n2 113 [0.08  0.125 0.371] [0.287 0.278 0.466] [0.175 0.238 0.426]\\n3 108 [-0.279 -0.076  0.44 ] [-0.144  0.018  0.668] [-0.228 -0.034  0.556]\\n4 100 [ 0.063 -0.069  0.607] [0.215 0.054 0.827] [ 0.129 -0.031  0.685]\\n5 96 [-0.262 -0.06   0.657] [-0.177  0.037  0.927] [-0.229 -0.025  0.763]\\n6 93 [-1.000e-03  1.600e-01  1.227e+00] [0.219 0.232 1.439] [0.108 0.208 1.345]\\n7 91 [-0.246  0.083  0.367] [-0.079  0.279  0.466] [-0.13   0.213  0.419]\\n8 89 [-0.384  0.042  1.133] [-0.224  0.174  1.314] [-0.304  0.114  1.233]\\n9 87 [-0.433  0.054  1.388] [-0.343  0.144  1.429] [-0.394  0.115  1.406]\\n10 84 [-0.002 -0.099  1.331] [ 0.14  -0.023  1.492] [ 0.074 -0.077  1.403]\\n11 84 [-0.378 -0.006  1.22 ] [-0.236  0.068  1.368] [-0.32   0.027  1.278]\\n12 81 [0.189 0.101 0.001] [0.33  0.282 0.136] [0.265 0.216 0.066]\\n13 79 [-0.089 -0.067  1.664] [0.024 0.094 1.8  ] [-0.032  0.035  1.736]\\n14 78 [0.066 0.044 0.532] [0.229 0.189 0.666] [0.14  0.131 0.589]\\n15 74 [ 0.174 -0.067  0.65 ] [0.261 0.043 0.928] [ 0.227 -0.029  0.773]\\n16 74 [-0.361  0.101  1.264] [-0.203  0.183  1.425] [-0.274  0.159  1.346]\\n17 74 [-0.478  0.008  1.191] [-0.379  0.15   1.286] [-0.433  0.077  1.241]\\n18 73 [0.235 0.042 1.172] [0.355 0.158 1.289] [0.289 0.091 1.232]\\n19 73 [ 0.325 -0.002  1.084] [0.397 0.135 1.218] [0.356 0.07  1.14 ]\\n20 70 [ 0.007 -0.128  1.647] [0.095 0.02  1.8  ] [ 0.059 -0.049  1.745]\\n21 69 [ 0.189 -0.023  0.67 ] [0.262 0.142 0.928] [0.237 0.065 0.788]\\n22 69 [-0.248  0.072  0.53 ] [-0.103  0.189  0.663] [-0.155  0.139  0.581]\\n23 68 [0.118 0.039 0.001] [0.178 0.268 0.158] [0.141 0.166 0.072]\\n24 68 [-0.205  0.031  0.001] [-0.128  0.166  0.177] [-0.16   0.11   0.078]\\n25 67 [ 0.094 -0.015  1.506] [0.161 0.048 1.603] [0.129 0.014 1.564]\\n26 66 [-0.185 -0.007  1.169] [-0.116  0.161  1.208] [-0.162  0.085  1.188]\\n27 66 [0.075 0.111 0.46 ] [0.158 0.19  0.539] [0.104 0.154 0.509]\\n28 65 [0.449 0.022 1.134] [0.504 0.118 1.253] [0.477 0.073 1.208]\\n29 64 [-0.094 -0.136  1.644] [-1.000e-03 -1.700e-02  1.797e+00] [-0.061 -0.082  1.726]\\n30 64 [-0.216 -0.074  0.713] [-0.085 -0.036  0.913] [-0.16  -0.061  0.811]\\n31 63 [-0.143 -0.1    1.324] [ 0.009 -0.028  1.468] [-0.078 -0.082  1.385]\\n32 63 [ 0.239 -0.007  1.218] [0.367 0.057 1.357] [0.315 0.024 1.264]\\n33 63 [ 0.057 -0.063  0.868] [0.221 0.    0.993] [ 0.147 -0.045  0.936]\\n34 63 [-0.302  0.074  0.   ] [-0.127  0.279  0.129] [-0.229  0.222  0.032]\\n35 62 [ 0.145 -0.058  1.469] [0.205 0.057 1.597] [ 0.16  -0.006  1.536]\\n36 62 [-0.343 -0.036  0.047] [-0.241  0.09   0.186] [-0.307  0.017  0.095]\\n37 61 [-0.376  0.009  1.094] [-0.325  0.113  1.235] [-0.352  0.048  1.176]\\n38 61 [0.075 0.107 0.615] [0.231 0.178 0.746] [0.148 0.147 0.683]\\n39 61 [0.109 0.151 0.179] [0.225 0.265 0.269] [0.159 0.231 0.233]\\n40 59 [-0.183  0.147  0.001] [-0.118  0.281  0.116] [-0.138  0.221  0.051]\\n41 58 [ 0.074 -0.097  1.306] [ 0.173 -0.03   1.435] [ 0.131 -0.072  1.356]\\n42 58 [0.252 0.079 1.191] [0.403 0.174 1.355] [0.342 0.144 1.275]\\n43 58 [-0.292  0.188  0.33 ] [-0.114  0.271  0.381] [-0.236  0.241  0.356]\\n44 57 [0.096 0.046 0.89 ] [0.229 0.172 0.985] [0.173 0.117 0.934]\\n45 56 [ 0.046 -0.074  0.702] [ 0.193 -0.024  0.905] [ 0.138 -0.059  0.809]\\n46 56 [-0.183  0.064  1.482] [-0.148  0.211  1.612] [-0.163  0.144  1.579]\\n47 56 [-0.608 -0.116  1.096] [-0.494 -0.024  1.172] [-0.553 -0.086  1.141]\\n48 56 [-0.262  0.02   0.679] [-0.168  0.146  0.919] [-0.231  0.086  0.801]\\n49 56 [0.271 0.054 0.43 ] [0.325 0.211 0.534] [0.306 0.136 0.486]\\n50 56 [-0.292 -0.108  0.001] [-0.235 -0.06   0.072] [-0.27  -0.088  0.034]\\n51 55 [-0.259  0.195  0.443] [-0.143  0.25   0.504] [-0.196  0.236  0.48 ]\\n52 54 [-0.093 -0.055  1.66 ] [-0.033  0.048  1.789] [-0.073  0.01   1.734]\\n53 54 [-0.332  0.138  1.405] [-0.229  0.216  1.46 ] [-0.288  0.188  1.428]\\n54 53 [ 0.006 -0.014  1.687] [0.088 0.073 1.797] [0.049 0.027 1.753]\\n55 53 [ 0.162 -0.076  0.493] [0.279 0.021 0.651] [ 0.243 -0.034  0.552]\\n56 53 [ 0.175 -0.089  0.001] [0.26  0.062 0.121] [ 0.218 -0.048  0.047]\\n57 52 [-0.036 -0.158  1.597] [ 0.011 -0.119  1.66 ] [-0.011 -0.144  1.633]\\n58 52 [ 0.021 -0.124  1.577] [ 0.08  -0.081  1.667] [ 0.05  -0.106  1.632]\\n59 52 [-0.145  0.159  1.185] [0.003 0.214 1.333] [-0.047  0.181  1.245]\\n60 52 [-0.122  0.098  0.895] [0.    0.201 0.949] [-0.059  0.172  0.921]\\n61 52 [0.287 0.017 0.001] [0.387 0.159 0.143] [0.341 0.089 0.047]\\n62 52 [-0.319  0.153  0.067] [-0.235  0.256  0.177] [-0.274  0.216  0.125]\\n63 51 [-0.092 -0.104  1.25 ] [ 0.011 -0.085  1.366] [-0.041 -0.096  1.31 ]\\n64 51 [ 0.013 -0.018  0.638] [0.092 0.113 0.895] [0.045 0.055 0.763]\\n65 51 [0.188 0.038 0.545] [0.261 0.18  0.759] [0.235 0.109 0.657]\\n66 51 [-0.326  0.039  0.44 ] [-0.29   0.123  0.534] [-0.309  0.071  0.5  ]\\n67 50 [-0.002 -0.162  1.378] [ 0.045 -0.119  1.524] [ 0.029 -0.141  1.444]\\n68 50 [0.282 0.007 1.506] [0.366 0.118 1.597] [0.321 0.075 1.561]\\n69 50 [0.329 0.103 1.41 ] [0.417 0.192 1.459] [0.373 0.156 1.435]\\n70 50 [0.397 0.058 1.082] [0.475 0.144 1.217] [0.434 0.114 1.149]\\n71 50 [0.397 0.041 1.204] [0.481 0.148 1.278] [0.442 0.101 1.237]\\n72 50 [-0.339 -0.07   0.001] [-0.281 -0.017  0.089] [-0.308 -0.043  0.043]\\n73 49 [ 0.162 -0.053  1.412] [ 0.294 -0.002  1.498] [ 0.226 -0.03   1.446]\\n74 49 [ 0.327 -0.031  1.411] [0.401 0.056 1.464] [0.366 0.018 1.437]\\n75 49 [-0.106 -0.074  1.533] [-0.06  -0.005  1.677] [-0.078 -0.041  1.598]\\n76 49 [0.103 0.216 0.339] [0.28  0.271 0.381] [0.191 0.251 0.369]\\n77 49 [0.263 0.151 0.126] [0.301 0.212 0.172] [0.29  0.178 0.145]\\n78 49 [-0.338  0.059  0.001] [-0.247  0.196  0.186] [-0.298  0.146  0.078]\\n79 48 [ 0.17  -0.059  1.404] [ 0.33  -0.011  1.494] [ 0.258 -0.042  1.437]\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":26532,"completion_tokens":327,"cached_tokens":26350}},{"step_id":29,"timestamp":"2026-09-26T02:24:03.229317+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_27_1","function_name":"bash_command","arguments":{"command":"cat >/tmp/weight.py <<'PY'\nimport bpy,bmesh\nfrom mathutils.kdtree import KDTree\nbpy.ops.wm.open_mainfile(filepath='/tmp/rig.blend');rig=bpy.data.objects['Guardian_Rig'];o=bpy.data.objects['CharacterMesh02']\nproxy=o.copy();proxy.data=o.data.copy();bpy.context.collection.objects.link(proxy);proxy.name='WeightProxy';proxy.vertex_groups.clear()\nbm=bmesh.new();bm.from_mesh(proxy.data);bmesh.ops.remove_doubles(bm,verts=list(bm.verts),dist=.015);bm.to_mesh(proxy.data);bm.free()\nprint('proxy',len(proxy.data.vertices),flush=True)\nfor b in rig.data.bones:\n if any(t in b.name for t in ['index','middle','ring','pinky','thumb','hand']):b.use_deform=False\nbpy.ops.object.select_all(action='DESELECT');proxy.select_set(True);rig.select_set(True);bpy.context.view_layer.objects.active=rig;bpy.ops.object.parent_set(type='ARMATURE_AUTO')\nprint('missing',sum(not v.groups for v in proxy.data.vertices),flush=True)\nkd=KDTree(len(proxy.data.vertices))\nfor v in proxy.data.vertices:kd.insert(v.co,v.index)\nkd.balance();o.vertex_groups.clear()\nfor g in proxy.vertex_groups:o.vertex_groups.new(name=g.name)\nfor v in o.data.vertices:\n _,i,_=kd.find(v.co)\n for g in proxy.data.vertices[i].groups:\n  o.vertex_groups[g.group].add([v.index],g.weight,'REPLACE')\nbpy.data.objects.remove(proxy,do_unlink=True)\nfor b in rig.data.bones:b.use_deform=True\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/weighted.blend')\nPY\nblender -b -t 4 --python /tmp/weight.py >/tmp/weight.log 2>&1\ncat /tmp/weight.log","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_27_1","content":"{\"exit_code\":0,\"stdout\":\"proxy 24183\\nmissing 0\\nInfo: Saved as \\\"weighted.blend\\\"\\nBlender 4.5.12 LTS (hash 84afd5f785f7 built 2026-07-21 01:31:22)\\nRead blend: \\\"/tmp/rig.blend\\\"\\n\\nBlender quit\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":30652,"completion_tokens":569,"cached_tokens":26529}},{"step_id":30,"timestamp":"2026-09-26T02:24:18.345040+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_28_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nimport numpy as np,json\nv=np.array(json.load(open('/tmp/mesh.json'))['CharacterMesh02']['vertices'])\nfor z in [.16,.22,.55,.6,.95,1.23,1.26,1.3,1.4,1.46]:\n a=v[(abs(v[:,2]-z)<.015)&(v[:,0]>(.25 if z>1.2 else .1))]\n print(z,np.round(a.min(0),3),np.round(a.max(0),3),np.round(np.median(a,0),3))\nPY","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_28_1","content":"{\"exit_code\":0,\"stdout\":\"0.16 [0.115 0.046 0.146] [0.301 0.273 0.175] [0.262 0.156 0.16 ]\\n0.22 [0.116 0.064 0.205] [0.274 0.258 0.235] [0.222 0.162 0.223]\\n0.55 [ 0.107 -0.071  0.535] [0.301 0.206 0.565] [0.229 0.099 0.545]\\n0.6 [ 0.101 -0.065  0.586] [0.271 0.161 0.615] [ 0.167 -0.017  0.603]\\n0.95 [ 0.101 -0.108  0.935] [0.268 0.214 0.965] [0.216 0.035 0.954]\\n1.23 [ 0.267 -0.01   1.215] [0.494 0.166 1.245] [0.384 0.046 1.229]\\n1.26 [0.25  0.002 1.245] [0.474 0.17  1.275] [0.37  0.053 1.259]\\n1.3 [ 0.263 -0.004  1.285] [0.393 0.174 1.313] [0.355 0.077 1.292]\\n1.4 [ 0.252 -0.037  1.385] [0.433 0.19  1.415] [0.353 0.119 1.407]\\n1.46 [ 0.25  -0.069  1.445] [0.395 0.225 1.473] [0.333 0.131 1.452]\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":31334,"completion_tokens":512,"cached_tokens":30649}},{"step_id":31,"timestamp":"2026-09-26T02:24:29.853066+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_29_1","function_name":"bash_command","arguments":{"command":"sed -i \"s/\\.401,\\.01,1.263/.382,.07,1.263/g;s/\\.214,\\.068,\\.154/.214,.165,.154/g;s/\\.18,\\.04,\\.573/.19,.06,.573/g\" /tmp/build.py\nblender -b -t 4 --python /tmp/build.py >/tmp/build.log 2>&1 && blender -b -t 4 --python /tmp/weight.py >/tmp/weight.log 2>&1","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_29_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":32339,"completion_tokens":270,"cached_tokens":31331}},{"step_id":32,"timestamp":"2026-09-26T02:26:03.802133+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_30_1","function_name":"bash_command","arguments":{"command":"cat > /tmp/animate.py <<'PY'\nimport bpy,json,math\nfrom mathutils import Vector,Matrix,Quaternion,Euler\nfrom math import sin,cos,pi\nbpy.ops.wm.open_mainfile(filepath='/tmp/weighted.blend')\nrig=bpy.data.objects['Guardian_Rig'];sc=bpy.context.scene;sc.frame_start=1;sc.frame_end=166;sc.render.fps=24\nC=json.load(open('/app/reference-camera-path.json'))['centers'];V=Vector\nrest={b.name:b.matrix_local.copy() for b in rig.data.bones}\n# frame, yaw, forward bend, lateral lean, pelvis height, L wrist xyz relative pelvis, R wrist\nkeys=[\n(1,-8,6,-3,.94,(.29,-.32,.58),(-.34,-.29,.68)),\n(7,-8,7,-3,.94,(.29,-.32,.58),(-.34,-.29,.68)),\n(13,-22,9,-9,.89,(.28,-.32,.60),(-.32,-.17,.81)),\n(19,-5,16,13,.85,(.44,-.19,.40),(-.12,-.23,.86)),\n(25,-53,9,-8,.91,(.10,-.30,.78),(-.28,-.25,.59)),\n(31,-9,19,12,.83,(.42,-.22,.45),(-.12,-.24,.84)),\n(37,-53,3,-9,.90,(.05,-.21,.87),(-.30,-.23,.60)),\n(43,-54,34,-21,.78,(.02,-.48,.55),(-.34,-.34,.57)),\n(48,-2,3,-19,.86,(.27,-.03,.93),(-.50,-.28,.58)),\n(55,-34,-1,-14,.92,(.20,-.08,.91),(-.43,-.40,.52)),\n(61,-6,4,2,.93,(.32,-.28,.68),(-.32,-.27,.75)),\n(67,6,6,7,.96,(.23,-.32,.69),(-.29,-.24,.80)),\n(72,3,15,-8,.89,(.10,-.40,.65),(-.39,-.36,.65)),\n(79,4,5,5,.90,(.37,-.26,.73),(-.28,-.27,.76)),\n(85,27,36,20,.77,(.27,-.44,.55),(-.08,-.41,.60)),\n(91,-7,11,-20,.83,(.08,-.20,.87),(-.46,-.40,.60)),\n(95,0,24,-16,.80,(.03,-.37,.78),(-.43,-.40,.63)),\n(102,-6,7,-4,.92,(.29,-.34,.62),(-.36,-.29,.73)),\n(110,-13,3,0,.98,(.29,-.30,.69),(-.35,-.27,.76)),\n(116,-16,7,-2,.94,(.25,-.32,.67),(-.33,-.29,.72)),\n(123,-15,7,-5,.94,(.27,-.32,.66),(-.34,-.29,.72)),\n(128,-22,11,-7,.90,(.22,-.33,.67),(-.35,-.29,.71)),\n(134,-12,4,-2,.98,(.27,-.30,.68),(-.34,-.25,.74)),\n(140,-14,7,-4,.94,(.27,-.32,.66),(-.34,-.27,.72)),\n(146,-22,11,-7,.89,(.23,-.35,.67),(-.35,-.29,.72)),\n(152,-12,5,-2,.97,(.28,-.32,.68),(-.34,-.26,.74)),\n(158,-14,7,-4,.93,(.27,-.32,.66),(-.34,-.27,.72)),\n(164,-21,10,-6,.90,(.23,-.34,.66),(-.35,-.28,.72)),\n(166,-17,8,-5,.93,(.27,-.32,.66),(-.34,-.27,.72))]\ndef interp(f):\n for a,b in zip(keys,keys[1:]):\n  if a[0]<=f<=b[0]:\n   u=(f-a[0])/(b[0]-a[0]);u=u*u*(3-2*u)\n   return [a[j]*(1-u)+b[j]*u if j<5 else V(a[j]).lerp(V(b[j]),u) for j in range(1,7)]\n return keys[-1][1:]\ndef globalrot(n,q):\n pb=rig.pose.bones[n]\n pq=pb.parent.matrix.to_quaternion() if pb.parent else Quaternion()\n rq=rest[n].to_quaternion();pr=rest[pb.parent.name].to_quaternion() if pb.parent else Quaternion()\n pb.rotation_mode='QUATERNION';pb.rotation_quaternion=(pq@pr.inverted()@rq).inverted()@q\n bpy.context.view_layer.update()\ndef aim(n,d):\n r=rest[n].to_quaternion();orig=r@V((0,1,0));q=orig.rotation_difference(d.normalized())@r;globalrot(n,q)\ndef framebasis(direction,normal):\n y=direction.normalized();z=(normal-y*normal.dot(y)).normalized();x=y.cross(z).normalized();z=x.cross(y);return Matrix((x,y,z)).transposed().to_quaternion()\ndef two_joint(a,c,l1,l2,pole):\n d=c-a;dist=min(d.length,l1+l2-.001);axis=d.normalized();p=(pole-axis*pole.dot(axis)).normalized();along=(l1*l1-l2*l2+dist*dist)/(2*dist);h=max(0,l1*l1-along*along)**.5;return a+axis*along+p*h\n# foot plant tracks world XY, planted between steps\ntracks={\n 'right':[(1,(.47,.56)),(8,(.47,.56)),(20,(.92,.30)),(28,(.92,.30)),(40,(1.30,.00)),(55,(1.30,.00)),(68,(.90,-.03)),(76,(.90,-.03)),(87,(-.15,-.00)),(99,(-.15,-.00)),(112,(-.08,.32)),(166,(-.08,.32))],\n 'left':[(1,(.88,.39)),(17,(.88,.39)),(29,(1.24,.17)),(39,(1.24,.17)),(51,(1.65,-.23)),(61,(1.65,-.23)),(74,(1.10,-.25)),(78,(1.10,-.25)),(94,(.29,-.31)),(104,(.29,-.31)),(121,(.34,.10)),(166,(.34,.10))]}\ndef footloc(side,f):\n tr=tracks[side]\n for (a,xy),(b,xy2) in zip(tr,tr[1:]):\n  if a<=f<=b:\n   t=(f-a)/(b-a);u=t*t*(3-2*t);p=V((xy[0],xy[1],.154)).lerp(V((xy2[0],xy2[1],.154)),u)\n   if xy!=xy2:p.z+=.085*sin(pi*t)\n   return p\n return V((*tr[-1][1],.154))\nfor f in range(1,167):\n sc.frame_set(f)\n for pb in rig.pose.bones:pb.location=(0,0,0);pb.rotation_mode='QUATERNION';pb.rotation_quaternion=Quaternion();pb.scale=(1,1,1)\n yaw,bend,lean,height,lw,rw=interp(f);yaw=math.radians(yaw)\n R=Matrix.Rotation(yaw,4,'Z').to_quaternion();root=V((C[str(f)][0],C[str(f)][1]+.14,height))\n # root offset includes rest pelvis\n rig.location=root-V((0,.065,.98))\n p=rig.pose.bones['pelvis'];p.rotation_quaternion=Euler((math.radians(3),math.radians(lean*.18),yaw),'XYZ').to_quaternion()\n for n,mult in [('spine',.55),('chest',.45)]:rig.pose.bones[n].rotation_quaternion=Euler((math.radians(bend*mult),math.radians(lean*mult),0),'XYZ').to_quaternion()\n rig.pose.bones['neck'].rotation_quaternion=Euler((math.radians(-bend*.28),math.radians(-lean*.15),0),'XYZ').to_quaternion()\n rig.pose.bones['head'].rotation_quaternion=Euler((math.radians(5),0,math.radians(-yaw*10)),'XYZ').to_quaternion()\n bpy.context.view_layer.update()\n for side,sgn,wrist in [('left',1,lw),('right',-1,rw)]:\n  un=side+'_upperarm';fn=side+'_forearm';hn=side+'_hand';a=rig.pose.bones[un].head.copy();c=root+R@wrist-rig.location\n  l1=rig.data.bones[un].length;l2=rig.data.bones[fn].length\n  pole=R@V((sgn*.8,.35,-.65));e=two_joint(a,c,l1,l2,pole)\n  # exact target if reach clamps\n  if (c-a).length>l1+l2-.001:c=a+(c-a).normalized()*(l1+l2-.001);e=two_joint(a,c,l1,l2,pole)\n  aim(un,e-a)\n  # keep wrist aligned to cuff; guard palm faces inward\n  palm=R@V((-.1*sgn,1,.1));rd=rest[fn].to_quaternion()@V((0,1,0));rp=V((-.22*sgn,-.72,.66))\n  q=framebasis(c-e,palm)@framebasis(rd,rp).inverted()@rest[fn].to_quaternion();globalrot(fn,q)\n  # hand straight continuation of forearm\n  handdir=(c-e).normalized();rhd=rest[hn].to_quaternion()@V((0,1,0));hq=framebasis(handdir,palm)@framebasis(rhd,rp).inverted()@rest[hn].to_quaternion();globalrot(hn,hq)\n  for digit in ['index','middle','ring','pinky','thumb']:\n   for k in range(1,4):\n    pb=rig.pose.bones[side+'_'+digit+str(k)]\n    if digit=='thumb':angles=[.50,.72,.5];ang=angles[k-1];pb.rotation_quaternion=Euler((ang,0,(-.15 if side=='left' else .15) if k==1 else 0)).to_quaternion()\n    else:\n     angles=[1.12,1.48,1.10];ang=angles[k-1]+.035*sin(f*.16+(0 if side=='left' else 2));pb.rotation_quaternion=Euler((ang,0,0)).to_quaternion()\n  # planted legs\n  tn=side+'_thigh';sn=side+'_shin';ft=side+'_foot';a=rig.pose.bones[tn].head.copy();c=footloc(side,f)-rig.location\n  e=two_joint(a,c,rig.data.bones[tn].length,rig.data.bones[sn].length,R@V((sgn*.22,-1,.05)))\n  aim(tn,e-a);aim(sn,c-e)\n  # feet mostly stay flat with small toe pivot\n  fq=Matrix.Rotation(math.radians(-8 if side=='left' else 8)+yaw*.25,4,'Z').to_quaternion()@rest[ft].to_quaternion();globalrot(ft,fq)\n for pb in rig.pose.bones:\n  pb.keyframe_insert(data_path='rotation_quaternion',frame=f,group=pb.name)\n rig.keyframe_insert(data_path='location',frame=f,group='World travel')\n# action labels\nrig.animation_data.action.name='Boxing practice • articulated FK performance'\nfor f,name in [(1,'GUARD / breathe'),(13,'RIGHT / slip'),(25,'COVER / pivot'),(43,'DUCK'),(48,'LEFT / rising hook'),(67,'SHUFFLE'),(85,'ROLL UNDER'),(103,'RECOVER'),(123,'GUARD / footwork')]:sc.timeline_markers.new(name,frame=f)\n# studio setup\nworld=sc.world or bpy.data.worlds.new('Studio');sc.world=world;world.use_nodes=True;world.node_tree.nodes['Background'].inputs[0].default_value=(.22,.22,.22,1);world.node_tree.nodes['Background'].inputs[1].default_value=.6\nfor name,loc,power,size in [('Key',(3,-4,6),650,4),('Fill',(-4,-1,3),400,4),('Rim',(1,4,5),700,3)]:\n d=bpy.data.lights.new(name,'AREA');o=bpy.data.objects.new(name,d);sc.collection.objects.link(o);o.location=loc;d.energy=power;d.shape='DISK';d.size=size;o.rotation_euler=(V((.5,0,1))-o.location).to_track_quat('-Z','Y').to_euler()\ncam=bpy.data.objects.new('Performance camera',bpy.data.cameras.new('Performance camera'));sc.collection.objects.link(cam);sc.camera=cam;cam.data.type='ORTHO';cam.data.ortho_scale=2.34217;cam.rotation_mode='QUATERNION';cam.rotation_quaternion=(.7325186,.64080435,.15127344,.1729242)\noffset=V((3.90362,-7.80724,1.17109))\nfor f in range(1,167):cam.location=V(C[str(f)])+offset;cam.keyframe_insert(data_path='location',frame=f)\nsc.render.engine='CYCLES';sc.cycles.samples=16;sc.render.resolution_x=480;sc.render.resolution_y=480;sc.render.resolution_percentage=100;sc.render.film_transparent=False\nsc.frame_set(1)\nbpy.ops.object.select_all(action='DESELECT');rig.select_set(True);bpy.context.view_layer.objects.active=rig\n# convenient opening view\nfor screen in bpy.data.screens:\n for area in screen.areas:\n  if area.type=='VIEW_3D':\n   area.spaces.active.region_3d.view_distance=3.3;area.spaces.active.region_3d.view_location=V((.68,.3,1));area.spaces.active.region_3d.view_rotation=Quaternion((.75774,.652556,0,0));area.spaces.active.shading.type='MATERIAL'\nbpy.ops.wm.save_as_mainfile(filepath='/home/ubuntu/task/output/submission/result.blend')\n# previews checkpoint cameras\nviews=json.load(open('/app/camera-manifest.json'))['views']\nsc.render.engine='BLENDER_WORKBENCH';sc.display.shading.light='STUDIO';sc.display.shading.color_type='MATERIAL';sc.display.shading.show_cavity=True\ncam.animation_data_clear()\nfor i in [0,2,4,7]:\n v=views[i];sc.frame_set(v['frame']);cam.location=v['location'];cam.rotation_quaternion=v['rotation_quaternion'];sc.render.filepath='/tmp/pose'+str(i)+'.png';bpy.ops.render.render(write_still=True)\nPY\nblender -b -t 4 --python /tmp/animate.py > /tmp/animate.log 2>&1","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_30_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":32649,"completion_tokens":4856,"cached_tokens":32336}},{"step_id":33,"timestamp":"2026-09-26T02:26:11.862009+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_31_1","function_name":"bash_command","arguments":{"command":"tail -30 /tmp/animate.log; python3 - <<'PY'\nfrom PIL import Image\nim=Image.new('RGB',(1440,720),'white')\nfor j,i in enumerate([0,2,4,7]):\n for row,p in enumerate([f'/app/reference/reference-checkpoint-{i+1}.png',f'/tmp/pose{i}.png']):\n  a=Image.open(p).resize((360,360));im.paste(a,(j*360,row*360))\nim.save('/tmp/compare.png')\nPY\nDISPLAY=:1 display /tmp/compare.png >/tmp/d6.log 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_31_1","content":"{\"exit_code\":0,\"stdout\":\"EGL Error (0x3009): EGL_BAD_MATCH: Arguments are inconsistent (for example, a valid context requires buffers not supplied by a valid surface).\\nEGL Error (0x3009): EGL_BAD_MATCH: Arguments are inconsistent (for example, a valid context requires buffers not supplied by a valid surface).\\nEGL Error (0x3009): EGL_BAD_MATCH: Arguments are inconsistent (for example, a valid context requires buffers not supplied by a valid surface).\\nBlender 4.5.12 LTS (hash 84afd5f785f7 built 2026-07-21 01:31:22)\\nRead blend: \\\"/tmp/weighted.blend\\\"\\nSaved: '/tmp/pose0.png'\\nTime: 00:05.27 (Saving: 00:00.05)\\n\\nSaved: '/tmp/pose2.png'\\nTime: 00:00.25 (Saving: 00:00.01)\\n\\nSaved: '/tmp/pose4.png'\\nTime: 00:00.23 (Saving: 00:00.01)\\n\\nSaved: '/tmp/pose7.png'\\nTime: 00:00.23 (Saving: 00:00.01)\\n\\nInfo: Saved as \\\"result.blend\\\"\\n\\nBlender quit\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":37545,"completion_tokens":175,"cached_tokens":32646}},{"step_id":34,"timestamp":"2026-09-26T02:26:17.302474+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep32.webp"}}]}]},"metrics":{"prompt_tokens":38043,"completion_tokens":18,"cached_tokens":37542}},{"step_id":35,"timestamp":"2026-09-26T02:26:42.900800+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_33_1","function_name":"bash_command","arguments":{"command":"cat > /tmp/check.py <<'PY'\nimport bpy,collections,json\nbpy.ops.wm.open_mainfile(filepath='/home/ubuntu/task/output/submission/result.blend')\nfor name in ['CharacterMesh00','CharacterMesh01','CharacterMesh02']:\n o=bpy.data.objects[name];w=collections.defaultdict(float);mi=2;ma=0;miss=0\n for v in o.data.vertices:\n  su=sum(g.weight for g in v.groups);mi=min(mi,su);ma=max(ma,su)\n  if su<.99:miss+=1\n  for g in v.groups:w[o.vertex_groups[g.group].name]+=g.weight\n print(name,mi,ma,miss,dict(w))\nsc=bpy.context.scene;sc.frame_set(1);r=bpy.data.objects['Guardian_Rig'];\nfor n in ['left_upperarm','left_forearm','left_hand','left_index1','left_index2','left_index3']:\n p=r.pose.bones[n];print(n,list(p.head),list(p.tail),list(p.rotation_quaternion))\nPY\nblender -b -t 2 --python /tmp/check.py >/tmp/check.log 2>&1; cat /tmp/check.log","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_33_1","content":"{\"exit_code\":0,\"stdout\":\"CharacterMesh00 0.8325709691271186 0.9837192930281162 16610 {'left_forearm': 1818.1891188276932, 'left_index1': 2040.1218582820147, 'left_ring1': 953.5256664101034, 'left_pinky1': 671.1752179414034, 'left_pinky2': 1351.649159646593, 'left_thumb1': 1416.2133240438998, 'left_thumb2': 1134.7213077051565, 'left_middle1': 1045.1188992261887, 'left_ring2': 924.1983146863058, 'left_thumb3': 805.4035245990381, 'left_pinky3': 423.3635699301958, 'left_middle2': 796.3904541423544, 'left_index2': 430.83595263399184, 'left_ring3': 500.1309005552903, 'left_middle3': 534.1612797295675, 'left_index3': 358.829510868527}\\nCharacterMesh01 0.8992652893066406 0.9999624863266945 10969 {'right_forearm': 1301.7598551856354, 'right_hand': 2961.651917179115, 'right_pinky1': 1762.6606621453539, 'right_thumb1': 1615.4072258006781, 'right_ring1': 967.6151184467599, 'right_middle1': 1049.6276353439316, 'right_pinky2': 437.21184436790645, 'right_ring2': 544.8868075106293, 'right_pinky3': 380.5919572347775, 'right_ring3': 476.6095218854025, 'right_index1': 1413.358083578758, 'right_thumb2': 758.2026821514592, 'right_thumb3': 419.27953581139445, 'right_index2': 549.7224982893094, 'right_middle2': 701.2772048832849, 'right_index3': 416.2528604483232, 'right_middle3': 517.3722993405536}\\nCharacterMesh02 0.8656218387186527 1.0000000968575478 37117 {'pelvis': 2560.0890989350155, 'spine': 3896.6847415482625, 'chest': 1565.2811995130032, 'left_clavicle': 3588.0449474332854, 'right_clavicle': 3297.3647492909804, 'left_thigh': 5804.3045383654535, 'right_thigh': 5691.95861955639, 'neck': 2078.4848047224805, 'head': 1810.384925792925, 'left_upperarm': 3236.786360144615, 'left_forearm': 2729.3606568491086, 'right_upperarm': 2686.6335737463087, 'right_forearm': 2397.989409662783, 'left_shin': 4384.365238309838, 'right_shin': 4193.210328050889, 'left_foot': 1561.3674103468657, 'right_foot': 1474.6814682930708}\\nleft_upperarm [0.29147574305534363, -0.010485725477337837, 1.426645278930664] [0.4488304853439331, -0.21078349649906158, 1.3921449184417725] [0.8046985268592834, -0.1924903392791748, -0.04467415809631348, -0.5598318576812744]\\nleft_forearm [0.4488304853439331, -0.21078349649906158, 1.3921449184417725] [0.2426423281431198, -0.2922459542751312, 1.5599998235702515] [-0.7870466709136963, 0.418165922164917, 0.12461981177330017, 0.4360787868499756]\\nleft_hand [0.2426423281431198, -0.2922459542751312, 1.5599998235702515] [0.1684565544128418, -0.3215559124946594, 1.6203935146331787] [0.9878435134887695, -0.054752737283706665, 0.002477779984474182, 0.14546898007392883]\\nleft_index1 [0.19422538578510284, -0.3203693628311157, 1.6453043222427368] [0.17452725768089294, -0.2812994122505188, 1.6683368682861328] [0.8457708358764648, 0.5335463285446167, 0.0, 0.0]\\nleft_index2 [0.17452725768089294, -0.2812994122505188, 1.6683368682861328] [0.1952679455280304, -0.2571267783641815, 1.651642084121704] [0.7365857362747192, 0.6763442158699036, 0.0, 0.0]\\nleft_index3 [0.1952679455280304, -0.2571267783641815, 1.651642084121704] [0.2130715548992157, -0.26672476530075073, 1.6337999105453491] [0.8510639071464539, 0.525062084197998, 0.0, 0.0]\\nBlender 4.5.12 LTS (hash 84afd5f785f7 built 2026-07-21 01:31:22)\\nRead blend: \\\"/home/ubuntu/task/output/submission/result.blend\\\"\\n\\nBlender quit\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":39637,"completion_tokens":763,"cached_tokens":38040}},{"step_id":36,"timestamp":"2026-09-26T02:27:52.155235+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_34_1","function_name":"bash_command","arguments":{"command":"cat >/tmp/fixweights.txt <<'PY'\n# Normalize body and provide smooth, anatomical hand envelopes.\nimport numpy as np\nfor o in [bpy.data.objects['CharacterMesh02']]:\n for v in o.data.vertices:\n  tot=sum(g.weight for g in v.groups)\n  if tot>0:\n   for g in v.groups:o.vertex_groups[g.group].add([v.index],g.weight/tot,'REPLACE')\nfor obj,side,sgn in [('CharacterMesh00','left',1),('CharacterMesh01','right',-1)]:\n o=bpy.data.objects[obj];o.vertex_groups.clear()\n groups={n:o.vertex_groups.new(name=n) for n in [side+'_forearm',side+'_hand']+[side+'_'+d+str(k) for d in ['index','middle','ring','pinky','thumb'] for k in range(1,4)]}\n wrist=rig.data.bones[side+'_hand'].head_local.copy();axis=(rig.data.bones[side+'_hand'].tail_local-wrist).normalized()\n def smooth(x):x=max(0,min(1,x));return x*x*(3-2*x)\n for v in o.data.vertices:\n  p=o.matrix_world@v.co;best=None\n  for d in ['index','middle','ring','pinky','thumb']:\n   a=rig.data.bones[side+'_'+d+'1'].head_local;b=rig.data.bones[side+'_'+d+'3'].tail_local;vec=b-a;t=(p-a).dot(vec)/vec.length_squared;dist=(p-(a+vec*max(0,min(1,t)))).length\n   # nearest complete digit center line\n   if best is None or dist<best[0]:best=(dist,d,t,vec.length)\n  dist,d,t,length=best\n  finger=smooth(t/.22)* (1-smooth((dist-.016)/.025))\n  # thumb base transitions gradually from thenar area\n  if d=='thumb':finger=smooth(t/.33)*(1-smooth((dist-.019)/.025))\n  vals={side+'_hand':1-finger}\n  # smooth between phalanges around hinges\n  t=max(0,min(1,t));w12=smooth((t-.37)/.14);w23=smooth((t-.69)/.14)\n  vals[side+'_'+d+'1']=finger*(1-w12);vals[side+'_'+d+'2']=finger*w12*(1-w23);vals[side+'_'+d+'3']=finger*w23\n  fw=1-smooth(((p-wrist).dot(axis)+.006)/.03)\n  vals={n:w*(1-fw) for n,w in vals.items()};vals[side+'_forearm']=fw\n  for n,w in vals.items():\n   if w>1e-6:groups[n].add([v.index],w,'REPLACE')\nPY\npython3 - <<'PY'\np='/tmp/animate.py'\ns=open(p).read();s=s.replace(\"# frame, yaw\",open('/tmp/fixweights.txt').read()+\"\\n# frame, yaw\")\ns=s.replace(\"p.rotation_quaternion=Euler((math.radians(3),math.radians(lean*.18),yaw),'XYZ').to_quaternion()\",\"p.rotation_quaternion=rest['pelvis'].to_quaternion().inverted() @ Euler((math.radians(3),math.radians(lean*.18),yaw),'XYZ').to_quaternion() @ rest['pelvis'].to_quaternion()\")\ns=s.replace(\"rig.pose.bones[n].rotation_quaternion=Euler((math.radians(bend*mult),math.radians(lean*mult),0),'XYZ').to_quaternion()\",\"rig.pose.bones[n].rotation_quaternion=rest[n].to_quaternion().inverted() @ Euler((math.radians(bend*mult),math.radians(lean*mult),0),'XYZ').to_quaternion() @ rest[n].to_quaternion()\")\ns=s.replace(\"rig.pose.bones['neck'].rotation_quaternion=Euler((math.radians(-bend*.28),math.radians(-lean*.15),0),'XYZ').to_quaternion()\",\"rig.pose.bones['neck'].rotation_quaternion=rest['neck'].to_quaternion().inverted() @ Euler((math.radians(-bend*.6),math.radians(-lean*.10),0),'XYZ').to_quaternion() @ rest['neck'].to_quaternion()\")\ns=s.replace(\"rig.pose.bones['head'].rotation_quaternion=Euler((math.radians(5),0,math.radians(-yaw*10)),'XYZ').to_quaternion()\",\"rig.pose.bones['head'].rotation_quaternion=rest['head'].to_quaternion().inverted() @ Euler((math.radians(1),0,math.radians(-yaw*10)),'XYZ').to_quaternion() @ rest['head'].to_quaternion()\")\ns=s.replace(\"sgn*.8,.35,-.65\",\"sgn*.48,.20,-1\")\ns=s.replace(\"(-8 if side=='left' else 8)+yaw*.25\",\"(12 if side=='left' else -30)+yaw*.25\")\ns=s.replace(\" for pb in rig.pose.bones:\\n  pb.keyframe_insert\", \"\"\" # Match the public world-space bounding center, not the camera-relative motion.\n bpy.context.view_layer.update()\n deps=bpy.context.evaluated_depsgraph_get();lo=np.array([1e9]*3);hi=-lo\n for ob in [bpy.data.objects[n] for n in ['CharacterMesh00','CharacterMesh01','CharacterMesh02']]:\n  ev=ob.evaluated_get(deps);me=ev.to_mesh();arr=np.empty(len(me.vertices)*3,dtype=np.float32);me.vertices.foreach_get('co',arr);arr=arr.reshape(-1,3);mat=np.array(ev.matrix_world);arr=arr@mat[:3,:3].T+mat[:3,3];lo=np.minimum(lo,arr.min(0));hi=np.maximum(hi,arr.max(0));ev.to_mesh_clear()\n rig.location+=V(C[str(f)])-V((lo+hi)*.5)\n for pb in rig.pose.bones:\n  pb.keyframe_insert\"\"\")\nopen(p,'w').write(s)\nPY\nblender -b -t 4 --python /tmp/animate.py > /tmp/animate.log 2>&1","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_34_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":41881,"completion_tokens":2846,"cached_tokens":39634}},{"step_id":37,"timestamp":"2026-09-26T02:27:59.340293+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_35_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nfrom PIL import Image\nim=Image.new('RGB',(1440,720),'white')\nfor j,i in enumerate([0,2,4,7]):\n for row,p in enumerate([f'/app/reference/reference-checkpoint-{i+1}.png',f'/tmp/pose{i}.png']):im.paste(Image.open(p).resize((360,360)),(j*360,row*360))\nim.save('/tmp/compare2.png')\nPY\nDISPLAY=:1 display /tmp/compare2.png >/tmp/d7.log 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_35_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":44767,"completion_tokens":166,"cached_tokens":41878}},{"step_id":38,"timestamp":"2026-09-26T02:28:04.046405+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep36.webp"}}]}]},"metrics":{"prompt_tokens":44973,"completion_tokens":18,"cached_tokens":44764}},{"step_id":39,"timestamp":"2026-09-26T02:28:57.302745+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_37_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\np='/tmp/animate.py';s=open(p).read().replace('sgn*.48,.20,-1','sgn*.86,.32,-.58').replace('sgn*.22,-1,.05','-sgn*.35,-1,.05')\ns=s.replace(\"yaw=math.radians(yaw)\\n R=\", \"\"\"yaw=math.radians(yaw)\n if f<=13 or f>=102 or 61<=f<=79:\n  lw.z-=.075;rw.z-=.075\n  lw.x+=.045;rw.x-=.025\n R=\"\"\")\ns=s.replace(\"(.27,-.03,.93),(-.50,-.28,.58)\",\"(.17,-.03,.93),(-.58,-.28,.58)\")\ns=s.replace(\"(.03,-.37,.78),(-.43,-.40,.63)\",\"(-.14,-.37,.78),(-.51,-.40,.63)\")\ns=s.replace(\"angles=[.50,.72,.5]\",\"angles=[.9,.95,.6]\")\nopen(p,'w').write(s)\nPY\nblender -b -t 4 --python /tmp/animate.py > /tmp/animate.log 2>&1\ncat >/tmp/renderfinal.py <<'PY'\nimport bpy,json\nbpy.ops.wm.open_mainfile(filepath='/home/ubuntu/task/output/submission/result.blend')\ns=bpy.context.scene;c=s.camera;c.animation_data_clear();v=json.load(open('/app/camera-manifest.json'))['views'][0];s.frame_set(1);c.location=v['location'];c.rotation_quaternion=v['rotation_quaternion'];s.render.filepath='/tmp/textured.png';bpy.ops.render.render(write_still=True)\nPY\nblender -b -t 6 --python /tmp/renderfinal.py > /tmp/renderfinal.log 2>&1","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_37_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":46567,"completion_tokens":1748,"cached_tokens":44970}},{"step_id":40,"timestamp":"2026-09-26T02:29:05.667129+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_38_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nfrom PIL import Image\nim=Image.new('RGB',(1440,720),'white')\nfor j,i in enumerate([0,2,4,7]):\n for row,p in enumerate([f'/app/reference/reference-checkpoint-{i+1}.png',f'/tmp/pose{i}.png']):im.paste(Image.open(p).resize((360,360)),(j*360,row*360))\nim.save('/tmp/compare3.png')\nPY\nDISPLAY=:1 display /tmp/compare3.png >/tmp/d8.log 2>&1 &\nDISPLAY=:1 display /tmp/textured.png >/tmp/d9.log 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_38_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":48355,"completion_tokens":187,"cached_tokens":46564}},{"step_id":41,"timestamp":"2026-09-26T02:29:10.982646+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep39.webp"}}]}]},"metrics":{"prompt_tokens":48582,"completion_tokens":18,"cached_tokens":48352}},{"step_id":42,"timestamp":"2026-09-26T02:29:35.856333+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_40_1","function_name":"bash_command","arguments":{"command":"tail -15 /tmp/renderfinal.log; cat /tmp/d9.log; ls -lh /tmp/textured.png; DISPLAY=:1 display -resize 750x750 /tmp/textured.png >/tmp/d10.log 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_40_1","content":"{\"exit_code\":0,\"stdout\":\"Fra:1 Mem:77.03M (Peak 82.92M) | Time:00:01.33 | Mem:38.65M, Peak:38.65M | Scene, ViewLayer | Updating Integrator\\nFra:1 Mem:77.03M (Peak 82.92M) | Time:00:01.33 | Mem:38.65M, Peak:38.65M | Scene, ViewLayer | Updating Film\\nFra:1 Mem:77.03M (Peak 82.92M) | Time:00:01.33 | Mem:38.56M, Peak:38.65M | Scene, ViewLayer | Updating Lookup Tables\\nFra:1 Mem:77.03M (Peak 82.92M) | Time:00:01.33 | Mem:38.65M, Peak:38.65M | Scene, ViewLayer | Updating Baking\\nFra:1 Mem:77.03M (Peak 82.92M) | Time:00:01.33 | Mem:38.65M, Peak:38.65M | Scene, ViewLayer | Updating Device | Writing constant memory\\nFra:1 Mem:77.04M (Peak 82.92M) | Time:00:01.33 | Mem:38.65M, Peak:38.65M | Scene, ViewLayer | Loading denoising kernels (may take a few minutes the first time)\\nFra:1 Mem:77.04M (Peak 82.92M) | Time:00:01.33 | Mem:38.65M, Peak:38.65M | Scene, ViewLayer | Sample 0/16\\nFra:1 Mem:93.82M (Peak 93.82M) | Time:00:01.38 | Remaining:00:00.70 | Mem:55.36M, Peak:55.36M | Scene, ViewLayer | Sample 1/16\\nFra:1 Mem:100.85M (Peak 111.40M) | Time:00:03.13 | Mem:55.36M, Peak:55.36M | Scene, ViewLayer | Sample 16/16\\nFra:1 Mem:100.85M (Peak 111.40M) | Time:00:03.13 | Mem:55.36M, Peak:55.36M | Scene, ViewLayer | Finished\\nSaved: '/tmp/textured.png'\\nTime: 00:03.19 (Saving: 00:00.06)\\n\\n\\nBlender quit\\n-rw-rw-r-- 1 ubuntu ubuntu 240K Sep 26 02:28 /tmp/textured.png\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":50176,"completion_tokens":858,"cached_tokens":48579}},{"step_id":43,"timestamp":"2026-09-26T02:29:43.375887+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep41.webp"}}]}]},"metrics":{"prompt_tokens":51650,"completion_tokens":18,"cached_tokens":50173}},{"step_id":44,"timestamp":"2026-09-26T02:30:17.892388+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_42_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\np='/tmp/animate.py';s=open(p).read().replace('-sgn*.35,-1,.05','sgn*.05,-1,.05').replace('lw.x+=.045;rw.x-=.025','lw.x+=.10;rw.x-=.075')\ns=s.replace('(48,-2,3,-19,.86,(.17,-.03,.93)','(48,-2,3,-19,.94,(.17,-.03,.82)')\ns=s.replace('(55,-34,-1,-14,.92,(.20,-.08,.91)','(55,-34,-1,-14,.97,(.20,-.08,.84)')\ns=s.replace('(95,0,24,-16,.80,(-.14,-.37,.78)','(95,0,24,-16,.86,(-.14,-.37,.72)')\ns=s.replace('(68,(.90,-.03)),(76,(.90,-.03)),(87,(-.15,-.00)),(99,(-.15,-.00))','(68,(.72,-.03)),(76,(.40,-.10)),(87,(-.36,-.28)),(99,(-.36,-.28))')\ns=s.replace('(74,(1.10,-.25)),(78,(1.10,-.25)),(94,(.29,-.31)),(104,(.29,-.31))','(74,(1.28,-.12)),(78,(1.28,-.12)),(94,(.42,-.04)),(104,(.42,-.04))')\n# Preserve exact normalized coverage after tiny envelope pruning\ns=s.replace(\"# action labels\", \"\"\"# All groups are explicitly normalized, including the palm transition.\nfor ob in [bpy.data.objects[n] for n in ['CharacterMesh00','CharacterMesh01','CharacterMesh02']]:\n for vert in ob.data.vertices:\n  total=sum(g.weight for g in vert.groups)\n  if total>0:\n   for g in vert.groups:ob.vertex_groups[g.group].add([vert.index],g.weight/total,'REPLACE')\n# action labels\"\"\")\nopen(p,'w').write(s)\nPY\nblender -b -t 4 --python /tmp/animate.py > /tmp/animate.log 2>&1","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_42_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":53244,"completion_tokens":1066,"cached_tokens":51647}},{"step_id":45,"timestamp":"2026-09-26T02:30:50.479115+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_43_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\np='/tmp/animate.py';s=open(p).read();s=s.replace(\" if f<=13 or f>=102 or 61<=f<=79:\\n  lw.z-=.075;rw.z-=.075\\n  lw.x+=.10;rw.x-=.075\", \"\"\" g=0\n gkeys=[(1,1),(8,1),(17,0),(55,0),(64,1),(76,1),(84,0),(97,0),(105,1),(166,1)]\n for (ga,va),(gb,vb) in zip(gkeys,gkeys[1:]):\n  if ga<=f<=gb:\n   u=(f-ga)/(gb-ga);u=u*u*(3-2*u);g=va*(1-u)+vb*u;break\n lw.z-=.075*g;rw.z-=.075*g\n lw.x+=.10*g;rw.x-=.075*g\"\"\")\ns=s.replace('# All groups are explicitly normalized',\"\"\"# Keep the armature object at its supplied world origin; travel belongs to the pelvis.\ntravel={}\nfor f in range(1,167):\n sc.frame_set(f);travel[f]=rig.location.copy()\nact=rig.animation_data.action\nfor fc in list(act.fcurves):\n if fc.data_path=='location':act.fcurves.remove(fc)\nrig.location=(0,0,0)\nfor f,pos in travel.items():\n sc.frame_set(f);rig.pose.bones['pelvis'].location=rest['pelvis'].to_quaternion().inverted()@pos\n rig.pose.bones['pelvis'].keyframe_insert(data_path='location',frame=f,group='pelvis')\n# All groups are explicitly normalized\"\"\")\nopen(p,'w').write(s)\nPY\nblender -b -t 4 --python /tmp/animate.py > /tmp/animate.log 2>&1\nls -lh /home/ubuntu/task/output/submission; tail -10 /tmp/animate.log","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_43_1","content":"{\"exit_code\":0,\"stdout\":\"lrwxrwxrwx 1 ubuntu ubuntu 4 Sep 26 02:19 /home/ubuntu/task/output/submission -> /app\\n\\nSaved: '/tmp/pose4.png'\\nTime: 00:00.24 (Saving: 00:00.01)\\n\\nSaved: '/tmp/pose7.png'\\nTime: 00:00.24 (Saving: 00:00.01)\\n\\nInfo: Saved as \\\"result.blend\\\"\\n\\nBlender quit\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":54350,"completion_tokens":1180,"cached_tokens":53241}},{"step_id":46,"timestamp":"2026-09-26T02:31:11.071467+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_44_1","function_name":"bash_command","arguments":{"command":"cat >/tmp/verify.py <<'PY'\nimport bpy,json,numpy as np\nbpy.ops.wm.open_mainfile(filepath='/home/ubuntu/task/output/submission/result.blend');s=bpy.context.scene;r=bpy.data.objects['Guardian_Rig'];print('RIG',list(r.location),len(r.data.bones));labels={}\nfor b in r.data.bones:\n for k,v in json.loads(b.get('bb_landmarks','{}')).items():labels[k]=labels.get(k,0)+1\nprint('labels',len(labels),set(json.load(open('/app/motion-contract.json'))['landmarks'])-labels.keys(),max(labels.values()))\nfor name in ['CharacterMesh00','CharacterMesh01','CharacterMesh02']:\n ob=bpy.data.objects[name];weights=[sum(g.weight for g in v.groups) for v in ob.data.vertices];print('WEIGHT',name,min(weights),max(weights))\n es=np.array([list(e.vertices) for e in ob.data.edges]);arr=np.array([list(v.co) for v in ob.data.vertices]);lengths=np.linalg.norm(arr[es[:,0]]-arr[es[:,1]],axis=1);valid=lengths>1e-4\n for f in [1,25,48,72,95,119,142,166]:\n  s.frame_set(f);ev=ob.evaluated_get(bpy.context.evaluated_depsgraph_get());m=ev.to_mesh();p=np.array([list(v.co) for v in m.vertices]);rat=np.linalg.norm(p[es[:,0]]-p[es[:,1]],axis=1)[valid]/lengths[valid];print('STRAIN',name,f,np.quantile(rat,[.01,.5,.95,.99,1]));ev.to_mesh_clear()\n# render all\ns.render.engine='BLENDER_WORKBENCH';s.display.shading.light='STUDIO';s.display.shading.color_type='MATERIAL';s.display.shading.show_cavity=True;c=s.camera;c.animation_data_clear()\nfor i,v in enumerate(json.load(open('/app/camera-manifest.json'))['views']):\n s.frame_set(v['frame']);c.location=v['location'];c.rotation_quaternion=v['rotation_quaternion'];s.render.filepath='/tmp/final'+str(i+1)+'.png';bpy.ops.render.render(write_still=True)\nPY\nblender -b -t 4 --python /tmp/verify.py >/tmp/verify.log 2>&1\ncat /tmp/verify.log","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_44_1","content":"{\"exit_code\":0,\"stdout\":\"EGL Error (0x3009): EGL_BAD_MATCH: Arguments are inconsistent (for example, a valid context requires buffers not supplied by a valid surface).\\nEGL Error (0x3009): EGL_BAD_MATCH: Arguments are inconsistent (for example, a valid context requires buffers not supplied by a valid surface).\\nEGL Error (0x3009): EGL_BAD_MATCH: Arguments are inconsistent (for example, a valid context requires buffers not supplied by a valid surface).\\nBlender 4.5.12 LTS (hash 84afd5f785f7 built 2026-07-21 01:31:22)\\nRead blend: \\\"/home/ubuntu/task/output/submission/result.blend\\\"\\nSaved: '/tmp/final1.png'\\nTime: 00:00.66 (Saving: 00:00.05)\\n\\nSaved: '/tmp/final2.png'\\nTime: 00:00.23 (Saving: 00:00.01)\\n\\nSaved: '/tmp/final3.png'\\nTime: 00:00.25 (Saving: 00:00.01)\\n\\nSaved: '/tmp/final4.png'\\nTime: 00:00.25 (Saving: 00:00.01)\\n\\nSaved: '/tmp/final5.png'\\nTime: 00:00.24 (Saving: 00:00.01)\\n\\nSaved: '/tmp/final6.png'\\nTime: 00:00.22 (Saving: 00:00.01)\\n\\nSaved: '/tmp/final7.png'\\nTime: 00:00.21 (Saving: 00:00.01)\\n\\nSaved: '/tmp/final8.png'\\nTime: 00:00.23 (Saving: 00:00.01)\\n\\nRIG [0.0, 0.0, 0.0] 49\\nlabels 39 set() 1\\nWEIGHT CharacterMesh00 0.9999999599531293 1.0000000367872417\\nSTRAIN CharacterMesh00 1 [ 0.39620795  1.00000135  1.53278304  2.34341925 35.13365061]\\nSTRAIN CharacterMesh00 25 [ 0.3873315   1.00000121  1.52371102  2.31237084 35.13346718]\\nSTRAIN CharacterMesh00 48 [ 0.39899332  1.00000147  1.5394358   2.37169367 35.13366532]\\nSTRAIN CharacterMesh00 72 [ 0.38736787  1.00000113  1.52193233  2.30822096 35.13335925]\\nSTRAIN CharacterMesh00 95 [ 0.39802857  1.00000111  1.53579047  2.35712321 35.1333731 ]\\nSTRAIN CharacterMesh00 119 [ 0.39628475  1.00000097  1.5330733   2.34369995 35.13367972]\\nSTRAIN CharacterMesh00 142 [ 0.38711752  1.00000132  1.52426323  2.31635235 35.13388781]\\nSTRAIN CharacterMesh00 166 [ 0.39919483  1.00000136  1.53948295  2.37177888 35.13350234]\\nWEIGHT CharacterMesh01 0.9999999610463419 1.0000000400468707\\nSTRAIN CharacterMesh01 1 [ 0.38300737  0.99999948  1.44292699  2.30701394 36.68532675]\\nSTRAIN CharacterMesh01 25 [ 0.37985629  0.99999972  1.43245903  2.27213341 36.68544125]\\nSTRAIN CharacterMesh01 48 [ 0.38002539  0.99999908  1.43278988  2.27339954 36.68567409]\\nSTRAIN CharacterMesh01 72 [ 0.38298323  0.99999948  1.44285064  2.30637304 36.68580791]\\nSTRAIN CharacterMesh01 95 [ 0.37775792  0.99999919  1.42734095  2.24773165 36.6852721 ]\\nSTRAIN CharacterMesh01 119 [ 0.38306739  0.99999963  1.44281861  2.30623745 36.68559682]\\nSTRAIN CharacterMesh01 142 [ 0.37995145  0.99999944  1.43176283  2.26714676 36.68549026]\\nSTRAIN CharacterMesh01 166 [ 0.37984225  0.99999958  1.4324269   2.27220818 36.68530597]\\nWEIGHT CharacterMesh02 0.9999999571591616 1.0000000447034836\\nSTRAIN CharacterMesh02 1 [0.82548359 1.00000098 1.11608858 1.30155123 2.39264862]\\nSTRAIN CharacterMesh02 25 [0.69699534 1.00000329 1.26498278 1.6100592  3.96541479]\\nSTRAIN CharacterMesh02 48 [0.80278658 1.00000197 1.15853922 1.43077098 3.24460485]\\nSTRAIN CharacterMesh02 72 [0.7603995  1.00000138 1.17002117 1.40173123 2.61310811]\\nSTRAIN CharacterMesh02 95 [0.67167416 1.00000177 1.2393741  1.65572067 4.22285864]\\nSTRAIN CharacterMesh02 119 [0.8009493  1.00000115 1.14238466 1.36057716 2.62093378]\\nSTRAIN CharacterMesh02 142 [0.7940775  1.00000095 1.13841287 1.36560648 2.65501168]\\nSTRAIN CharacterMesh02 166 [0.80051762 1.00000114 1.13424311 1.34771825 2.66455386]\\n\\nBlender quit\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":55678,"completion_tokens":803,"cached_tokens":54347}},{"step_id":47,"timestamp":"2026-09-26T02:31:49.320662+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_45_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\np='/tmp/animate.py';s=open(p).read();s=s.replace('# frame, yaw', '''# Smooth the hand envelopes across the actual mesh graph (also welded UV seams).\nfor obj in ['CharacterMesh00','CharacterMesh01']:\n ob=bpy.data.objects[obj];verts=ob.data.vertices;N=len(verts);G=len(ob.vertex_groups)\n xyz=np.array([list(v.co) for v in verts]);_,inv=np.unique(np.round(xyz,4),axis=0,return_inverse=True);M=int(inv.max())+1\n weights=np.zeros((N,G))\n for v in verts:\n  for gr in v.groups:weights[v.index,gr.group]=gr.weight\n W=np.zeros((M,G));cnt=np.bincount(inv,minlength=M);np.add.at(W,inv,weights);W/=cnt[:,None]\n E=np.array([list(e.vertices) for e in ob.data.edges]);E=inv[E];E=E[E[:,0]!=E[:,1]];E=np.unique(np.sort(E,axis=1),axis=0)\n deg=np.bincount(E.ravel(),minlength=M).clip(1)\n for it in range(32):\n  sums=np.zeros_like(W);np.add.at(sums,E[:,0],W[E[:,1]]);np.add.at(sums,E[:,1],W[E[:,0]])\n  W=.48*W+.52*sums/deg[:,None]\n W/=W.sum(axis=1)[:,None]\n for v in verts:\n  for j,w in enumerate(W[inv[v.index]]):\n   ob.vertex_groups[j].add([v.index],float(w),'REPLACE')\n# frame, yaw''');open(p,'w').write(s)\nPY\nblender -b -t 4 --python /tmp/animate.py > /tmp/animate.log 2>&1\nblender -b -t 4 --python /tmp/verify.py >/tmp/verify2.log 2>&1\ngrep STRAIN /tmp/verify2.log | head -16","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_45_1","content":"{\"exit_code\":0,\"stdout\":\"STRAIN CharacterMesh00 1 [0.39433354 1.00000097 1.50050563 2.0480752  4.56081351]\\nSTRAIN CharacterMesh00 25 [0.40153423 1.00000163 1.49087746 2.02669519 4.56101874]\\nSTRAIN CharacterMesh00 48 [0.38916163 1.00000419 1.50821024 2.06595991 4.56051101]\\nSTRAIN CharacterMesh00 72 [0.40295135 1.0000016  1.48959667 2.02289046 4.56097073]\\nSTRAIN CharacterMesh00 95 [0.39214035 1.00000225 1.50352644 2.05253838 4.56070188]\\nSTRAIN CharacterMesh00 119 [0.3943929  1.00000078 1.50054188 2.04885448 4.56081118]\\nSTRAIN CharacterMesh00 142 [0.40033517 1.00000062 1.4920541  2.02904119 4.56098863]\\nSTRAIN CharacterMesh00 166 [0.38925593 1.00000229 1.50836746 2.06615841 4.56059962]\\nSTRAIN CharacterMesh01 1 [0.36138813 0.99996552 1.42999623 1.97615917 4.00913278]\\nSTRAIN CharacterMesh01 25 [0.36466932 0.99996215 1.41989369 1.95395402 4.00887318]\\nSTRAIN CharacterMesh01 48 [0.3643557  0.99995843 1.42012808 1.95410755 4.00896694]\\nSTRAIN CharacterMesh01 72 [0.36136265 0.99996252 1.42982755 1.97563816 4.00900702]\\nSTRAIN CharacterMesh01 95 [0.36794221 0.99996433 1.41282046 1.94449232 4.00880933]\\nSTRAIN CharacterMesh01 119 [0.36133559 0.99996457 1.42969002 1.97560775 4.00898086]\\nSTRAIN CharacterMesh01 142 [0.36504841 0.99996568 1.41866011 1.95358976 4.00898306]\\nSTRAIN CharacterMesh01 166 [0.36467102 0.99996377 1.41981704 1.95399169 4.00896106]\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":58026,"completion_tokens":1116,"cached_tokens":55675}},{"step_id":48,"timestamp":"2026-09-26T02:32:21.021510+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_46_1","function_name":"bash_command","arguments":{"command":"cat >/tmp/hands_auto.py <<'PY'\nimport bpy,bmesh\nfrom mathutils.kdtree import KDTree\nbpy.ops.wm.open_mainfile(filepath='/tmp/weighted.blend');rig=bpy.data.objects['Guardian_Rig']\nfor name,side in [('CharacterMesh00','left'),('CharacterMesh01','right')]:\n o=bpy.data.objects[name];pr=o.copy();pr.data=o.data.copy();bpy.context.collection.objects.link(pr);pr.vertex_groups.clear()\n bm=bmesh.new();bm.from_mesh(pr.data);bmesh.ops.remove_doubles(bm,verts=list(bm.verts),dist=.01);bm.to_mesh(pr.data);bm.free()\n for b in rig.data.bones:b.use_deform=b.name.startswith(side) and any(d in b.name for d in ['forearm','hand','thumb','index','middle','ring','pinky'])\n bpy.ops.object.select_all(action='DESELECT');pr.select_set(True);rig.select_set(True);bpy.context.view_layer.objects.active=rig;bpy.ops.object.parent_set(type='ARMATURE_AUTO')\n print('proxy',name,len(pr.data.vertices),sum(not v.groups for v in pr.data.vertices))\n kd=KDTree(len(pr.data.vertices))\n for v in pr.data.vertices:kd.insert(v.co,v.index)\n kd.balance();o.vertex_groups.clear()\n for g in pr.vertex_groups:o.vertex_groups.new(name=g.name)\n for v in o.data.vertices:\n  _,i,_=kd.find(v.co);gs=pr.data.vertices[i].groups;total=sum(g.weight for g in gs)\n  if total:\n   for g in gs:o.vertex_groups[g.group].add([v.index],g.weight/total,'REPLACE')\n bpy.data.objects.remove(pr,do_unlink=True)\nfor b in rig.data.bones:b.use_deform=True\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/weighted_auto.blend')\nPY\nblender -b -t 4 --python /tmp/hands_auto.py >/tmp/ha.log 2>&1\ncat /tmp/ha.log\ncp /app/result.blend /tmp/good.blend\npython3 - <<'PY'\ns=open('/tmp/animate.py').read();a=s.index(\"for obj,side,sgn in\");b=s.index('# frame, yaw');s=s[:a]+s[b:];s=s.replace('/tmp/weighted.blend','/tmp/weighted_auto.blend');open('/tmp/animate_auto.py','w').write(s)\nPY\nblender -b -t 4 --python /tmp/animate_auto.py >/tmp/animateauto.log 2>&1\nblender -b -t 4 --python /tmp/verify.py >/tmp/verifyauto.log 2>&1\ngrep STRAIN /tmp/verifyauto.log | head -16","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_46_1","content":"{\"exit_code\":0,\"stdout\":\"proxy CharacterMesh00 16610 0\\nproxy CharacterMesh01 16610 0\\nInfo: Saved as \\\"weighted_auto.blend\\\"\\nBlender 4.5.12 LTS (hash 84afd5f785f7 built 2026-07-21 01:31:22)\\nRead blend: \\\"/tmp/weighted.blend\\\"\\n\\nBlender quit\\nSTRAIN CharacterMesh00 1 [0.44896529 1.04126823 1.55891796 1.98073581 3.59326502]\\nSTRAIN CharacterMesh00 25 [0.44448429 1.0393593  1.54818459 1.96372989 3.55770305]\\nSTRAIN CharacterMesh00 48 [0.45107039 1.0427432  1.56628228 1.99728365 3.62441117]\\nSTRAIN CharacterMesh00 72 [0.44410902 1.0392457  1.54700063 1.96163792 3.55342286]\\nSTRAIN CharacterMesh00 95 [0.44796251 1.0418462  1.56210839 1.98654007 3.60587616]\\nSTRAIN CharacterMesh00 119 [0.44870971 1.04132759 1.55960647 1.98117508 3.59443936]\\nSTRAIN CharacterMesh00 142 [0.44488544 1.03958185 1.54924492 1.96564144 3.56131595]\\nSTRAIN CharacterMesh00 166 [0.45115602 1.04275525 1.56633688 1.9977109  3.62467005]\\nSTRAIN CharacterMesh01 1 [0.42804207 0.99999852 1.38840852 1.71242192 3.55309591]\\nSTRAIN CharacterMesh01 25 [0.44076225 0.99999733 1.37796915 1.69342859 3.50722146]\\nSTRAIN CharacterMesh01 48 [0.44034499 0.99999682 1.37820018 1.69393766 3.50838214]\\nSTRAIN CharacterMesh01 72 [0.4278141  0.99999833 1.38830978 1.71219711 3.55232845]\\nSTRAIN CharacterMesh01 95 [0.44947743 0.99999619 1.36963662 1.68158142 3.47786696]\\nSTRAIN CharacterMesh01 119 [0.42781346 0.99999823 1.38825869 1.7121032  3.55233995]\\nSTRAIN CharacterMesh01 142 [0.44199835 0.99999727 1.37619365 1.69141496 3.50219615]\\nSTRAIN CharacterMesh01 166 [0.44072887 0.99999687 1.37787646 1.69345685 3.50717679]\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":59812,"completion_tokens":1051,"cached_tokens":58023}},{"step_id":49,"timestamp":"2026-09-26T02:32:29.377596+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_47_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nfrom PIL import Image\nim=Image.new('RGB',(1440,720),'white')\nfor j in range(8):im.paste(Image.open(f'/tmp/final{j+1}.png').resize((360,360)),((j%4)*360,(j//4)*360))\nim.save('/tmp/allfinal.png')\nPY\nDISPLAY=:1 display /tmp/allfinal.png >/tmp/d11.log 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_47_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":61619,"completion_tokens":224,"cached_tokens":59809}},{"step_id":50,"timestamp":"2026-09-26T02:32:34.837117+00:00","source":"agent","model_name":"gpt-6-astra","message":"","observation":{"results":[{"content":[{"type":"text","text":""},{"type":"image","source":{"media_type":"image/webp","path":"screenshot_ep48.webp"}}]}]},"metrics":{"prompt_tokens":61883,"completion_tokens":18,"cached_tokens":61616}},{"step_id":51,"timestamp":"2026-09-26T02:33:11.955229+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_49_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\ns=open('/tmp/animate.py').read().replace('/tmp/weighted.blend','/tmp/weighted_auto.blend').replace(\"[('CharacterMesh00','left',1),('CharacterMesh01','right',-1)]\",\"[('CharacterMesh00','left',1)]\").replace(\"for obj in ['CharacterMesh00','CharacterMesh01']:\",\"for obj in ['CharacterMesh00']:\")\ns=s.replace(\"# convenient opening view\", \"\"\"rig['Performance']='Boxing practice | 1–166 | 24 fps'\nrig['Rig notes']='FK deformation rig. Pelvis contains world travel; articulated three-bone digits. Bone bb_landmarks properties provide semantic correspondence.'\ntxt=bpy.data.texts.new('READ ME • Guardian rig')\ntxt.write('STONE GUARDIAN — BOXING PRACTICE\\\\n\\\\nEditable 49-bone FK armature with articulated hands.\\\\nWorld travel is keyed on the pelvis; the armature object remains at the original world origin.\\\\nEvery mesh vertex has normalized deformation weights.\\\\nThe original mesh data, materials and UVs have not been altered.\\\\n\\\\nTimeline: frames 1–166 at 24 fps. Named timeline markers identify guard, cover, duck, rising hook and recovery accents.\\\\nSelect Guardian_Rig and enter Pose Mode to edit the performance.\\\\nThe semantic correspondence is stored in each annotated Bone.bb_landmarks JSON property.\\\\n')\n# convenient opening view\"\"\")\nopen('/tmp/animate_final.py','w').write(s)\nPY\nblender -b -t 4 --python /tmp/animate_final.py > /tmp/animatefinal.log 2>&1\ncat > /tmp/protected.py <<'PY'\nimport bpy,hashlib,numpy as np\nnames=['CharacterMesh00','CharacterMesh01','CharacterMesh02']\ndef get():\n r={}\n for n in names:\n  o=bpy.data.objects[n];m=o.data\n  vs=np.empty(len(m.vertices)*3,dtype=np.float32);m.vertices.foreach_get('co',vs)\n  ed=np.empty(len(m.edges)*2,dtype=np.int32);m.edges.foreach_get('vertices',ed)\n  uv=[]\n  for layer in m.uv_layers:\n   a=np.empty(len(layer.data)*2,dtype=np.float32);layer.data.foreach_get('uv',a);uv.append(a.tobytes())\n  r[n]=(hashlib.sha256(vs.tobytes()+ed.tobytes()+b''.join(uv)).hexdigest(),[a.name for a in m.materials])\n return r\nbpy.ops.wm.open_mainfile(filepath='/app/base.blend');a=get()\nbpy.ops.wm.open_mainfile(filepath='/app/result.blend');b=get();print('PROTECTED DATA IDENTICAL',a==b)\nPY\nblender -b -t 2 --python /tmp/protected.py >/tmp/protected.log 2>&1\ncat /tmp/protected.log\nls -lh /app/result.blend*","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_49_1","content":"{\"exit_code\":0,\"stdout\":\"PROTECTED DATA IDENTICAL True\\nBlender 4.5.12 LTS (hash 84afd5f785f7 built 2026-07-21 01:31:22)\\nRead blend: \\\"/app/base.blend\\\"\\nRead blend: \\\"/app/result.blend\\\"\\n\\nBlender quit\\n-rw-rw-r-- 1 ubuntu ubuntu 26M Sep 26 02:33 /app/result.blend\\n-rw-rw-r-- 1 ubuntu ubuntu 24M Sep 26 02:32 /app/result.blend1\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":63477,"completion_tokens":1210,"cached_tokens":61880}},{"step_id":52,"timestamp":"2026-09-26T02:33:18.698409+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_50_1","function_name":"bash_command","arguments":{"command":"rm -f /app/result.blend1; which wmctrl; which xdotool; pgrep -a display","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_50_1","content":"{\"exit_code\":0,\"stdout\":\"/usr/bin/wmctrl\\n/usr/bin/xdotool\\n1767 display /tmp/refs.png\\n2266 display /tmp/front.png\\n2500 display /tmp/detail.png\\n2719 display /tmp/video2.png\\n3235 display /tmp/handplot.png\\n4821 display /tmp/compare.png\\n5453 display /tmp/compare2.png\\n6006 display /tmp/compare3.png\\n6007 display /tmp/textured.png\\n6240 display -resize 750x750 /tmp/textured.png\\n8027 display /tmp/allfinal.png\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":64851,"completion_tokens":149,"cached_tokens":63474}},{"step_id":53,"timestamp":"2026-09-26T02:33:24.507025+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_51_1","function_name":"bash_command","arguments":{"command":"kill 1767 2266 2500 2719 3235 4821 5453 6006 6007 6240 8027; 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