{"schema_version":"ATIF-v1.8","session_id":"b42110f3-06d6-4ced-9be4-47e222017852","agent":{"name":"computer-1","version":"1.1.0","model_name":"openai/gpt-6-astra"},"steps":[{"step_id":1,"timestamp":"2026-09-24T23:03:15.144439+00:00","source":"user","message":"# Apartment Dining Table Set — Lighting Brief\n\n## Objective\n\nLight the textured scene in `/home/ubuntu/task/input/base.blend` so that it renders like the supplied `lit-reference-*` images.\n\n## References\n\n`/home/ubuntu/task/input/cameras.json` publishes every scored camera: views `v01`, `v02`, `v03`, `v04`, `v05`, `v06` at 384×216 pixels. Renders use Cycles on the CPU with 24 samples, seed 7, no denoising and no adaptive sampling, lit by the scene's own lights. Scored renders are produced inside the grader from your file and from the hidden reference using the published cameras.\n\n## Lighting review\n\n`appearance_similarity` compares luminance, luminance edges, and the luminance histogram of the grader's renders of your file with renders of the hidden reference from the same cameras. Light count, light type, and energy values earn no credit by themselves. A submission with no positive-energy light or lit world, or whose rendered subject has mean luminance below both 0.05 and a quarter of the reference's subject luminance, is capped as `unlit_submission`. Keep `/home/ubuntu/task/output/submission/result.blend` at or below 3 GiB (and every submitted file at or below 512 MiB, the whole submission archive at or below 2 GiB).\n\n## Scoring\n\nThe deterministic score is 0.75 of the final result (75%); a three-pass visual review of paired candidate and reference renders is the remaining 0.25 (25%). The visual review scores these criteria with these weights: `light_direction_and_shadows` 0.4, `contrast_and_exposure` 0.3, `mood_and_colour` 0.3. Deterministic components and weights:\n\n- `appearance_similarity`: weight 1\n\nTolerances:\n\n- `minimum_subject_luminance`: 0.05\n\nQuality gates:\n\n- `appearance_similarity` below 0.25 caps the final score at 0.3\n\nScore caps:\n\n- `missing_submission`: final score capped at 0\n- `corrupt_submission`: final score capped at 0\n- `prohibited_change`: final score capped at 0.2\n- `unlit_submission`: final score capped at 0.2\n\n## Preservation and deliverable\n\nChange only lights, world. Preserve exactly: geometry, object structure, transforms, materials, material assignments, image textures, camera, animation, render settings, collections and view layers, the compositing and sequencer pipeline, and colour management. Any protected change caps the score at 0.2. Save `/home/ubuntu/task/output/submission/result.blend`.\n"},{"step_id":2,"timestamp":"2026-09-24T23:03:19.105662+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 -lah /home/ubuntu/task/input && which blender","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_0_1","content":"{\"exit_code\":0,\"stdout\":\"lrwxrwxrwx 1 ubuntu ubuntu 4 Sep 24 23:02 /home/ubuntu/task/input -> 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lit-reference-01.png\\n-rw-rw-r-- 1 ubuntu ubuntu 74K Jan 1 1970 lit-reference-02.png\\n-rw-rw-r-- 1 ubuntu ubuntu 75K Jan 1 1970 lit-reference-03.png\\n-rw-rw-r-- 1 ubuntu ubuntu 77K Jan 1 1970 lit-reference-04.png\\n-rw-rw-r-- 1 ubuntu ubuntu 73K Jan 1 1970 lit-reference-05.png\\n-rw-rw-r-- 1 ubuntu ubuntu 74K Jan 1 1970 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/app/cameras.json; cat > /tmp/inspect.py <<'PY'\nimport bpy\nfrom mathutils import Vector\nbpy.ops.wm.open_mainfile(filepath='/app/base.blend')\ns=bpy.context.scene\nprint('WORLD',s.world,[(n.name,[(i.name,str(i.default_value)) for i in n.inputs]) for n in s.world.node_tree.nodes] if s.world and s.world.use_nodes else '')\nprint('SETTINGS',s.render.engine,s.view_settings.view_transform,s.view_settings.look,s.view_settings.exposure)\nfor o in s.objects:\n if o.type in ['LIGHT','CAMERA']: print(o.name,o.type,list(o.location),list(o.rotation_euler),str(o.data))\n if o.type=='MESH':\n pts=[o.matrix_world@Vector(c) for c in o.bound_box]; print('MESH',o.name,'bounds',[[round(f(v[i] for v in pts),3) for i in range(3)] for f in [min,max]])\nPY\n/usr/local/bin/blender -b -t 4 --python /tmp/inspect.py > /tmp/inspect.log 2>&1; cat /tmp/inspect.log","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_3_1","content":"{\"exit_code\":0,\"stdout\":\"{\\n \\\"render\\\": {\\n \\\"adaptive\\\": false,\\n \\\"budget\\\": {\\n \\\"max_seconds_per_frame\\\": 8.0\\n },\\n \\\"color_management\\\": {\\n \\\"exposure\\\": 0.0,\\n \\\"gamma\\\": 1.0,\\n \\\"look\\\": \\\"AgX - Base Contrast\\\",\\n \\\"view_transform\\\": \\\"AgX\\\"\\n },\\n \\\"denoise\\\": false,\\n \\\"engine\\\": \\\"CYCLES\\\",\\n \\\"lighting\\\": \\\"scene\\\",\\n \\\"samples\\\": 24,\\n \\\"seed\\\": 7\\n },\\n \\\"schema_version\\\": 1,\\n \\\"views\\\": [\\n {\\n \\\"clip\\\": [\\n 0.01,\\n 1000.0\\n ],\\n \\\"film_transparent\\\": false,\\n \\\"frame\\\": 1,\\n \\\"id\\\": \\\"v01\\\",\\n \\\"lens_mm\\\": 55.0,\\n \\\"location\\\": [\\n 3.84321809,\\n 0.0,\\n 1.3138752\\n ],\\n \\\"ortho_scale\\\": null,\\n \\\"projection\\\": \\\"PERSP\\\",\\n \\\"resolution\\\": [\\n 384,\\n 216\\n ],\\n \\\"rotation_quaternion\\\": [\\n 0.56098551,\\n 0.43045929,\\n 0.43045932,\\n 0.56098551\\n ],\\n \\\"sensor_width\\\": 36.0\\n },\\n {\\n \\\"clip\\\": [\\n 0.01,\\n 1000.0\\n ],\\n \\\"film_transparent\\\": false,\\n \\\"frame\\\": 1,\\n \\\"id\\\": \\\"v02\\\",\\n \\\"lens_mm\\\": 55.0,\\n \\\"location\\\": [\\n 2.36709476,\\n 4.09992838,\\n 2.00719213\\n ],\\n \\\"ortho_scale\\\": null,\\n \\\"projection\\\": \\\"PERSP\\\",\\n \\\"resolution\\\": [\\n 384,\\n 216\\n ],\\n \\\"rotation_quaternion\\\": [\\n 0.21201214,\\n 0.14845249,\\n 0.55403233,\\n 0.7912401\\n ],\\n \\\"sensor_width\\\": 36.0\\n },\\n {\\n \\\"clip\\\": [\\n 0.01,\\n 1000.0\\n ],\\n \\\"film_transparent\\\": false,\\n \\\"frame\\\": 1,\\n \\\"id\\\": \\\"v03\\\",\\n \\\"lens_mm\\\": 55.0,\\n \\\"location\\\": [\\n -2.19704461,\\n 3.80539322,\\n 1.46148074\\n ],\\n \\\"ortho_scale\\\": null,\\n \\\"projection\\\": \\\"PERSP\\\",\\n \\\"resolution\\\": [\\n 384,\\n 216\\n ],\\n \\\"rotation_quaternion\\\": [\\n 0.20533496,\\n 0.15755899,\\n -0.58801836,\\n -0.76632053\\n ],\\n \\\"sensor_width\\\": 36.0\\n },\\n {\\n \\\"clip\\\": [\\n 0.01,\\n 1000.0\\n ],\\n \\\"film_transparent\\\": false,\\n \\\"frame\\\": 1,\\n \\\"id\\\": \\\"v04\\\",\\n \\\"lens_mm\\\": 55.0,\\n \\\"location\\\": [\\n -3.84321809,\\n 0.0,\\n 1.3138752\\n ],\\n \\\"ortho_scale\\\": null,\\n \\\"projection\\\": \\\"PERSP\\\",\\n \\\"resolution\\\": [\\n 384,\\n 216\\n ],\\n \\\"rotation_quaternion\\\": [\\n 0.56098557,\\n 0.43045929,\\n -0.43045935,\\n -0.56098551\\n ],\\n \\\"sensor_width\\\": 36.0\\n },\\n {\\n \\\"clip\\\": [\\n 0.01,\\n 1000.0\\n ],\\n \\\"film_transparent\\\": false,\\n \\\"frame\\\": 1,\\n \\\"id\\\": \\\"v05\\\",\\n \\\"lens_mm\\\": 55.0,\\n \\\"location\\\": [\\n -2.36709476,\\n -4.09992838,\\n 2.00719213\\n ],\\n \\\"ortho_scale\\\": null,\\n \\\"projection\\\": \\\"PERSP\\\",\\n \\\"resolution\\\": [\\n 384,\\n 216\\n ],\\n \\\"rotation_quaternion\\\": [\\n 0.7912401,\\n 0.55403227,\\n -0.14845252,\\n -0.21201216\\n ],\\n \\\"sensor_width\\\": 36.0\\n },\\n {\\n \\\"clip\\\": [\\n 0.01,\\n 1000.0\\n ],\\n \\\"film_transparent\\\": false,\\n \\\"frame\\\": 1,\\n \\\"id\\\": \\\"v06\\\",\\n \\\"lens_mm\\\": 55.0,\\n \\\"location\\\": [\\n 2.19704461,\\n -3.80539322,\\n 1.46148074\\n ],\\n \\\"ortho_scale\\\": null,\\n \\\"projection\\\": \\\"PERSP\\\",\\n \\\"resolution\\\": [\\n 384,\\n 216\\n ],\\n \\\"rotation_quaternion\\\": [\\n 0.76632053,\\n 0.58801842,\\n 0.15755904,\\n 0.20533496\\n ],\\n \\\"sensor_width\\\": 36.0\\n }\\n ]\\n}\\nBlender 4.5.12 LTS (hash 84afd5f785f7 built 2026-07-21 01:31:22)\\nRead blend: \\\"/app/base.blend\\\"\\nTraceback (most recent call last):\\n File \\\"/tmp/inspect.py\\\", line 5, in \\n print('WORLD',s.world,[(n.name,[(i.name,str(i.default_value)) for i in n.inputs]) for n in s.world.node_tree.nodes] if s.world and s.world.use_nodes else '')\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/tmp/inspect.py\\\", line 5, in \\n print('WORLD',s.world,[(n.name,[(i.name,str(i.default_value)) for i in n.inputs]) for n in s.world.node_tree.nodes] if s.world and s.world.use_nodes else '')\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/tmp/inspect.py\\\", line 5, in \\n print('WORLD',s.world,[(n.name,[(i.name,str(i.default_value)) for i in n.inputs]) for n in s.world.node_tree.nodes] if s.world and s.world.use_nodes else '')\\n ^^^^^^^^^^^^^^^\\nAttributeError: 'NodeSocketShader' object has no attribute 'default_value'\\n\\nBlender 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range(6):out.paste(Image.open('/app/lit-reference-%02d.png'%(i+1)),((i%3)*384,(i//3)*216))\nout.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\":\"WORLD [('World Output', [('Surface', ''), ('Volume', '')]), ('Background', [('Color', ''), ('Strength', '1.0'), ('Weight', '0.0')])]\\nSETTINGS CYCLES AgX AgX - Base Contrast 0.0\\nMESH asset_001 bounds [[-0.681, -0.949, 0.0], [0.681, 0.949, 0.568]]\\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.rCeGKA/command.sh: line 4: python: command not found\\n/tmp/computer-1-bash.rCeGKA/command.sh: line 10: xdg-open: command not 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> /tmp/render.py <<'PY'\nimport bpy,json,os,sys\nfrom mathutils import Vector\nbpy.ops.wm.open_mainfile(filepath='/app/base.blend');s=bpy.context.scene\np=json.load(open('/tmp/params.json'))\nw=s.world;w.use_nodes=True\nbg=w.node_tree.nodes.get('Background');bg.inputs['Color'].default_value=(*p.get('worldcolor',[1,1,1]),1);bg.inputs['Strength'].default_value=p['world']\nfor idx,l in enumerate(p.get('lights',[])):\n d=bpy.data.lights.new('Light'+str(idx),l.get('type','AREA'));o=bpy.data.objects.new(d.name,d);s.collection.objects.link(o);o.location=l['loc'];o.rotation_euler=(Vector(l.get('target',[0,0,.25]))-o.location).to_track_quat('-Z','Y').to_euler();d.energy=l['power'];d.color=l.get('color',[1,1,1]);\n if d.type=='AREA':d.shape='DISK';d.size=l.get('size',3)\n if d.type=='SUN':d.angle=l.get('size',.1)\nif p.get('save'):\n bpy.ops.wm.save_as_mainfile(filepath=p['save'])\ns.render.engine='CYCLES';s.cycles.device='CPU';s.cycles.samples=24;s.cycles.use_denoising=False;s.cycles.use_adaptive_sampling=False;s.cycles.seed=7\ns.render.resolution_x=384;s.render.resolution_y=216;s.render.resolution_percentage=100\ncamd=bpy.data.cameras.new('Preview');cam=bpy.data.objects.new('Preview',camd);s.collection.objects.link(cam);s.camera=cam\nfor v in json.load(open('/app/cameras.json'))['views']:\n if v['id'] not in p.get('views',['v01','v02','v03','v04','v05','v06']):continue\n cam.location=v['location'];cam.rotation_mode='QUATERNION';cam.rotation_quaternion=v['rotation_quaternion'];camd.lens=v['lens_mm'];camd.sensor_width=v['sensor_width'];camd.clip_start=.01;camd.clip_end=1000\n s.render.filepath='/tmp/'+p.get('name','test')+'-'+v['id']+'.png';bpy.ops.render.render(write_still=True)\nPY\n echo '{\"world\":0.05,\"lights\":[],\"name\":\"ambient\"}' > /tmp/params.json\n/usr/local/bin/blender -b -t 8 --python 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out.paste(Image.open('/tmp/'+name+'-v%02d.png'%(i+1)),((i%3)*384,(i//3)*432+216))\nout.save('/tmp/compare.png')\nPY\npython3 /tmp/montage.py ambient\nDISPLAY=:1 display /tmp/compare.png >/dev/null 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_13_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":12397,"completion_tokens":183,"cached_tokens":11581,"extra":{"provider_usage":{"input_tokens":12397,"output_tokens":183,"total_tokens":12580,"input_tokens_details":{"cached_tokens":11581,"cache_write_tokens":813},"output_tokens_details":{"reasoning_tokens":0}}}}},{"step_id":16,"timestamp":"2026-09-24T23:05:05.878147+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_ep14.webp"}}]}]},"metrics":{"prompt_tokens":12620,"completion_tokens":18,"cached_tokens":12394,"extra":{"provider_usage":{"input_tokens":12620,"output_tokens":18,"total_tokens":12638,"input_tokens_details":{"cached_tokens":12394,"cache_write_tokens":223},"output_tokens_details":{"reasoning_tokens":0}}}}},{"step_id":17,"timestamp":"2026-09-24T23:05:25.784889+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_15_1","function_name":"bash_command","arguments":{"command":"echo '{\"world\":0.05,\"lights\":[{\"loc\":[0,0,3],\"power\":400,\"size\":5}],\"name\":\"top\"}' > /tmp/params.json\n/usr/local/bin/blender -b -t 8 --python /tmp/render.py > /tmp/render.log 2>&1\npython3 /tmp/montage.py top\npkill display; DISPLAY=:1 display /tmp/compare.png >/dev/null 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_15_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":14214,"completion_tokens":417,"cached_tokens":12617,"extra":{"provider_usage":{"input_tokens":14214,"output_tokens":417,"total_tokens":14631,"input_tokens_details":{"cached_tokens":12617,"cache_write_tokens":1594},"output_tokens_details":{"reasoning_tokens":290}}}}},{"step_id":18,"timestamp":"2026-09-24T23:05:30.561069+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_ep16.webp"}}]}]},"metrics":{"prompt_tokens":14671,"completion_tokens":18,"cached_tokens":14211,"extra":{"provider_usage":{"input_tokens":14671,"output_tokens":18,"total_tokens":14689,"input_tokens_details":{"cached_tokens":14211,"cache_write_tokens":457},"output_tokens_details":{"reasoning_tokens":0}}}}},{"step_id":19,"timestamp":"2026-09-24T23:05:39.065730+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_17_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nimport importlib.util\nfor m in ['numpy','scipy','cv2','OpenEXR']:print(m,importlib.util.find_spec(m))\nPY\n tail /tmp/render.log","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_17_1","content":"{\"exit_code\":0,\"stdout\":\"numpy ModuleSpec(name='numpy', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7f27bf3c0560>, origin='/usr/lib/python3/dist-packages/numpy/__init__.py', submodule_search_locations=['/usr/lib/python3/dist-packages/numpy'])\\nscipy None\\ncv2 None\\nOpenEXR None\\nFra:1 Mem:267.29M (Peak 267.29M) | Time:00:00.57 | Mem:146.39M, Peak:147.53M | Scene, ViewLayer | Updating Device | Writing constant memory\\nFra:1 Mem:267.29M (Peak 267.29M) | Time:00:00.57 | Mem:146.39M, Peak:147.53M | Scene, ViewLayer | Sample 0/24\\nFra:1 Mem:268.63M (Peak 268.63M) | Time:00:00.58 | Remaining:00:00.18 | Mem:147.67M, Peak:147.67M | Scene, ViewLayer | Sample 1/24\\nFra:1 Mem:269.90M (Peak 272.43M) | Time:00:00.89 | Mem:147.67M, Peak:147.67M | Scene, ViewLayer | Sample 24/24\\nFra:1 Mem:269.90M (Peak 272.43M) | Time:00:00.89 | Mem:147.67M, Peak:147.67M | Scene, ViewLayer | Finished\\nSaved: '/tmp/top-v06.png'\\nTime: 00:00.90 (Saving: 00:00.00)\\n\\n\\nBlender quit\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":16265,"completion_tokens":233,"cached_tokens":14668,"extra":{"provider_usage":{"input_tokens":16265,"output_tokens":233,"total_tokens":16498,"input_tokens_details":{"cached_tokens":14668,"cache_write_tokens":1594},"output_tokens_details":{"reasoning_tokens":160}}}}},{"step_id":20,"timestamp":"2026-09-24T23:05:58.675529+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_18_1","function_name":"bash_command","arguments":{"command":"cat > /tmp/basis.py <<'PY'\nimport bpy,json,math,numpy as np\nfrom mathutils import Vector\nbpy.ops.wm.open_mainfile(filepath='/app/base.blend');s=bpy.context.scene\ns.render.engine='CYCLES';s.cycles.device='CPU';s.cycles.samples=24;s.cycles.use_denoising=False;s.cycles.use_adaptive_sampling=False;s.cycles.seed=7\ns.render.resolution_x=384;s.render.resolution_y=216;s.render.resolution_percentage=100\nbg=s.world.node_tree.nodes.get('Background');bg.inputs['Color'].default_value=(1,1,1,1)\ncamd=bpy.data.cameras.new('Preview');cam=bpy.data.objects.new('Preview',camd);s.collection.objects.link(cam);s.camera=cam\nim=bpy.data.images.new('LUT',width=4096,height=1,float_buffer=True);a=np.ones((1,4096,4),np.float32);a[0,:,:3]=np.geomspace(.00001,10,4096)[:,None];im.pixels.foreach_set(a.ravel());im.save_render('/tmp/lut.png',scene=s);np.save('/tmp/lutx.npy',a[0,:,0])\nd=bpy.data.lights.new('basis','AREA');o=bpy.data.objects.new('basis',d);s.collection.objects.link(o);d.shape='DISK';d.size=1.5\nlights=[]\nfor z in [1.5,3.5]:\n for i in range(12):\n angle=i*math.pi/6;lights.append([3*math.cos(angle),3*math.sin(angle),z])\nlights.append([0,0,3.5])\njson.dump(lights,open('/tmp/basisloc.json','w'))\nfor idx,loc in enumerate([[0,0,3]]+lights):\n bg.inputs['Strength'].default_value=.05 if idx==0 else 0;d.energy=0 if idx==0 else 100\n o.location=loc;o.rotation_euler=(Vector([0,0,.25])-o.location).to_track_quat('-Z','Y').to_euler()\n for vi,v in enumerate(json.load(open('/app/cameras.json'))['views']):\n cam.location=v['location'];cam.rotation_mode='QUATERNION';cam.rotation_quaternion=v['rotation_quaternion'];camd.lens=55;camd.sensor_width=36;camd.clip_start=.01;camd.clip_end=1000\n s.render.image_settings.file_format='OPEN_EXR';s.render.filepath='/tmp/raw.exr';bpy.ops.render.render(write_still=True)\n im=bpy.data.images.load('/tmp/raw.exr',check_existing=False);arr=np.empty(384*216*4,dtype=np.float32);im.pixels.foreach_get(arr);np.save('/tmp/basis-%02d-%d.npy'%(idx,vi),arr.reshape(216,384,4)[::-1,:,:3]);bpy.data.images.remove(im)\nPY\n/usr/local/bin/blender -b -t 8 --python /tmp/basis.py > /tmp/basis.log 2>&1 &\npython3 -m pip install scipy --break-system-packages >/tmp/pip.log 2>&1","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_18_1","content":"{\"exit_code\":1,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":16913,"completion_tokens":1039,"cached_tokens":16262,"extra":{"provider_usage":{"input_tokens":16913,"output_tokens":1039,"total_tokens":17952,"input_tokens_details":{"cached_tokens":16262,"cache_write_tokens":648},"output_tokens_details":{"reasoning_tokens":296}}}}},{"step_id":21,"timestamp":"2026-09-24T23:06:05.886615+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_19_1","function_name":"bash_command","arguments":{"command":"ls /tmp/basis*.npy | tail; cat /tmp/pip.log; tail -3 /tmp/basis.log; pgrep -a blender","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_19_1","content":"{\"exit_code\":0,\"stdout\":\"/tmp/basis-00-0.npy\\n/tmp/basis-00-1.npy\\n/tmp/basis-00-2.npy\\n/tmp/basis-00-3.npy\\n/tmp/basis-00-4.npy\\n/tmp/basis-00-5.npy\\n/usr/bin/python3: No module named pip\\nFra:1 Mem:174.63M (Peak 206.78M) | Time:00:01.43 | Mem:48.00M, Peak:48.00M | Scene, ViewLayer | Updating Particle Systems\\nFra:1 Mem:174.63M (Peak 206.78M) | Time:00:01.43 | Mem:48.00M, Peak:48.00M | Scene, ViewLayer | Updating Particle Systems | Copying Particles to device\\nFra:1 Mem:174.63M (Peak 206.78M) | Time:00:01.43 | Mem:48.00M, Peak:48.00M | Scene, ViewLayer | Updating Meshes\\n472 /usr/local/bin/blender\\n3250 /usr/local/bin/blender -b -t 8 --python /tmp/basis.py\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":17992,"completion_tokens":166,"cached_tokens":16910,"extra":{"provider_usage":{"input_tokens":17992,"output_tokens":166,"total_tokens":18158,"input_tokens_details":{"cached_tokens":16910,"cache_write_tokens":1079},"output_tokens_details":{"reasoning_tokens":109}}}}},{"step_id":22,"timestamp":"2026-09-24T23:06:17.888373+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_20_1","function_name":"bash_command","arguments":{"command":"cat > /tmp/fit.py <<'PY'\nimport numpy as np,json\nfrom PIL import Image\nlut=np.asarray(Image.open('/tmp/lut.png'))[0,:,:3].mean(1)/255\nx=np.load('/tmp/lutx.npy');lut,inds=np.unique(lut,return_index=True);x=x[inds]\ndef inv(a):return np.interp(a,lut,x)\ndef reduce(a):return a.reshape(72,3,128,3,3).mean((1,3))\nrefs=[];ambs=[];masks=[]\nfor v in range(6):\n ref=np.asarray(Image.open('/app/lit-reference-%02d.png'%(v+1)))[:,:,:3]/255\n ref=inv(ref);amb=np.load('/tmp/basis-00-%d.npy'%v)\n mask=reduce(amb).mean(2)<.049\n refs.append(reduce(ref)[mask].flatten());ambs.append(reduce(amb)[mask].flatten());masks.append(mask)\nb=np.concatenate(refs);amb=np.concatenate(ambs)\n# preserve background but allow ambient subject strength\nA=[]\nfor idx in range(1,27):\n ar=[]\n for v in range(6):ar.append(reduce(np.load('/tmp/basis-%02d-%d.npy'%(idx,v)))[masks[v]].flatten())\n A.append(np.concatenate(ar))\nA=np.stack(A,axis=1)\n# current ambient exact background\nb=b-amb\n# brightness compression weighting emphasize dark as well\nw=1/np.maximum(np.concatenate(refs),.012)**.65\nAw=A*w[:,None];bw=b*w\nata=Aw.T@Aw;atb=Aw.T@bw\ncoef=np.zeros(26)\nfor k in range(3000):\n for j in range(26):coef[j]=max(0,coef[j]+(atb[j]-ata[j]@coef)/ata[j,j])\nprint('fit',np.sqrt(np.mean((Aw@coef-bw)**2)))\nloc=json.load(open('/tmp/basisloc.json'))\nlights=[]\nfor i,c in enumerate(coef):\n if c>.005:\n print(i,loc[i],c*100);lights.append({'loc':loc[i],'power':float(c*100),'size':1.5})\njson.dump({'world':.05,'lights':lights,'name':'fit'},open('/tmp/params.json','w'))\nnp.save('/tmp/coef.npy',coef)\nPY\nls /tmp/basis*.npy | wc -l; tail -3 /tmp/basis.log","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_20_1","content":"{\"exit_code\":0,\"stdout\":\"11\\n\\nFra:1 Mem:89.08M (Peak 89.08M) | Time:00:00.00 | Mem:0.00M, Peak:0.00M | Scene, ViewLayer | Synchronizing object | asset_001\\nFra:1 Mem:89.08M (Peak 89.08M) | Time:00:00.00 | Mem:0.00M, Peak:0.00M | Scene, ViewLayer | Synchronizing object | basis\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":18448,"completion_tokens":697,"cached_tokens":17989,"extra":{"provider_usage":{"input_tokens":18448,"output_tokens":697,"total_tokens":19145,"input_tokens_details":{"cached_tokens":17989,"cache_write_tokens":456},"output_tokens_details":{"reasoning_tokens":98}}}}},{"step_id":23,"timestamp":"2026-09-24T23:06:22.861601+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_21_1","function_name":"bash_command","arguments":{"command":"grep -E 'Time: [0-9]|Saved' /tmp/basis.log | tail -24; ps -p 3250 -o etime,pcpu,rss; ls -lh /tmp/lut.png","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_21_1","content":"{\"exit_code\":0,\"stdout\":\"Saved: '/tmp/raw.exr'\\nTime: 00:00.73 (Saving: 00:00.00)\\nSaved: '/tmp/raw.exr'\\nTime: 00:00.71 (Saving: 00:00.00)\\nSaved: '/tmp/raw.exr'\\nTime: 00:00.78 (Saving: 00:00.00)\\nSaved: '/tmp/raw.exr'\\nTime: 00:00.72 (Saving: 00:00.00)\\nSaved: '/tmp/raw.exr'\\nTime: 00:00.98 (Saving: 00:00.00)\\nSaved: '/tmp/raw.exr'\\nTime: 00:03.36 (Saving: 00:00.00)\\nSaved: '/tmp/raw.exr'\\nTime: 00:04.11 (Saving: 00:00.50)\\nSaved: '/tmp/raw.exr'\\nTime: 00:02.27 (Saving: 00:00.00)\\nSaved: '/tmp/raw.exr'\\nTime: 00:02.11 (Saving: 00:00.00)\\nSaved: '/tmp/raw.exr'\\nTime: 00:01.79 (Saving: 00:00.00)\\nSaved: '/tmp/raw.exr'\\nTime: 00:01.75 (Saving: 00:00.00)\\nSaved: '/tmp/raw.exr'\\nTime: 00:01.90 (Saving: 00:00.00)\\n ELAPSED %CPU RSS\\n 00:24 220 746624\\n-rw-rw-r-- 1 ubuntu ubuntu 616 Sep 24 23:05 /tmp/lut.png\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":19293,"completion_tokens":106,"cached_tokens":18445,"extra":{"provider_usage":{"input_tokens":19293,"output_tokens":106,"total_tokens":19399,"input_tokens_details":{"cached_tokens":18445,"cache_write_tokens":845},"output_tokens_details":{"reasoning_tokens":34}}}}},{"step_id":24,"timestamp":"2026-09-24T23:08:09.292043+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_22_1","function_name":"bash_command","arguments":{"command":"sleep 100; ls /tmp/basis*.npy | wc -l; tail -3 /tmp/basis.log","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_22_1","content":"{\"exit_code\":0,\"stdout\":\"113\\nFra:1 Mem:267.37M (Peak 267.37M) | Time:00:00.67 | Mem:146.39M, Peak:147.53M | Scene, ViewLayer | Updating Device | Writing constant memory\\nFra:1 Mem:267.37M (Peak 267.37M) | Time:00:00.67 | Mem:146.39M, Peak:147.53M | Scene, ViewLayer | Sample 0/24\\nFra:1 Mem:268.72M (Peak 268.72M) | Time:00:00.68 | Remaining:00:00.22 | Mem:147.67M, Peak:147.67M | Scene, ViewLayer | Sample 1/24\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":19822,"completion_tokens":118,"cached_tokens":19290,"extra":{"provider_usage":{"input_tokens":19822,"output_tokens":118,"total_tokens":19940,"input_tokens_details":{"cached_tokens":19290,"cache_write_tokens":529},"output_tokens_details":{"reasoning_tokens":68}}}}},{"step_id":25,"timestamp":"2026-09-24T23:09:15.609613+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_23_1","function_name":"bash_command","arguments":{"command":"sleep 55; python3 /tmp/fit.py; /usr/local/bin/blender -b -t 8 --python /tmp/render.py > /tmp/fitrender.log 2>&1; python3 /tmp/montage.py fit; pkill display; DISPLAY=:1 display /tmp/compare.png >/dev/null 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_23_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"Traceback (most recent call last):\\n File \\\"/tmp/fit.py\\\", line 1, in \\n import numpy as np,json\\n File \\\"/usr/lib/python3/dist-packages/numpy/__init__.py\\\", line 159, in \\n from . import ma\\n File \\\"/usr/lib/python3/dist-packages/numpy/ma/__init__.py\\\", line 42, in \\n from . import core\\n File \\\"/usr/lib/python3/dist-packages/numpy/ma/core.py\\\", line 24, in \\n import inspect\\n File \\\"/tmp/inspect.py\\\", line 1, in \\n import bpy\\nModuleNotFoundError: No module named 'bpy'\\nTraceback (most recent call last):\\n File \\\"/tmp/montage.py\\\", line 6, in \\n out.paste(Image.open('/tmp/'+name+'-v%02d.png'%(i+1)),((i%3)*384,(i//3)*432+216))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/usr/lib/python3/dist-packages/PIL/Image.py\\\", line 3247, in open\\n fp = builtins.open(filename, \\\"rb\\\")\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nFileNotFoundError: [Errno 2] No such file or directory: '/tmp/fit-v01.png'\\n\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":20145,"completion_tokens":96,"cached_tokens":19819,"extra":{"provider_usage":{"input_tokens":20145,"output_tokens":96,"total_tokens":20241,"input_tokens_details":{"cached_tokens":19819,"cache_write_tokens":323},"output_tokens_details":{"reasoning_tokens":0}}}}},{"step_id":26,"timestamp":"2026-09-24T23:09:26.538072+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_24_1","function_name":"bash_command","arguments":{"command":"mv /tmp/inspect.py /tmp/sceneinfo.py; OPENBLAS_NUM_THREADS=1 python3 /tmp/fit.py; /usr/local/bin/blender -b -t 8 --python /tmp/render.py > /tmp/fitrender.log 2>&1; python3 /tmp/montage.py fit; pkill display; DISPLAY=:1 display /tmp/compare.png >/dev/null 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_24_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"Traceback (most recent call last):\\n File \\\"/tmp/fit.py\\\", line 18, in \\n for v in range(6):ar.append(reduce(np.load('/tmp/basis-%02d-%d.npy'%(idx,v)))[masks[v]].flatten())\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/usr/lib/python3/dist-packages/numpy/lib/npyio.py\\\", line 427, in load\\n fid = stack.enter_context(open(os_fspath(file), \\\"rb\\\"))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nFileNotFoundError: [Errno 2] No such file or directory: '/tmp/basis-26-0.npy'\\nTraceback (most recent call last):\\n File \\\"/tmp/montage.py\\\", line 6, in \\n out.paste(Image.open('/tmp/'+name+'-v%02d.png'%(i+1)),((i%3)*384,(i//3)*432+216))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/usr/lib/python3/dist-packages/PIL/Image.py\\\", line 3247, in open\\n fp = builtins.open(filename, \\\"rb\\\")\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nFileNotFoundError: [Errno 2] No such file or directory: '/tmp/fit-v01.png'\\n\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":20600,"completion_tokens":112,"cached_tokens":20142,"extra":{"provider_usage":{"input_tokens":20600,"output_tokens":112,"total_tokens":20712,"input_tokens_details":{"cached_tokens":20142,"cache_write_tokens":455},"output_tokens_details":{"reasoning_tokens":0}}}}},{"step_id":27,"timestamp":"2026-09-24T23:09:39.455103+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_25_1","function_name":"bash_command","arguments":{"command":"sed -i 's/range(1,27)/range(1,26)/;s/zeros(26)/zeros(25)/;s/range(26)/range(25)/' /tmp/fit.py\nOPENBLAS_NUM_THREADS=1 python3 /tmp/fit.py; /usr/local/bin/blender -b -t 8 --python /tmp/render.py > /tmp/fitrender.log 2>&1; python3 /tmp/montage.py fit; pkill display; DISPLAY=:1 display /tmp/compare.png >/dev/null 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_25_1","content":"{\"exit_code\":0,\"stdout\":\"fit 0.07351017664052106\\n2 [1.5000000000000004, 2.598076211353316, 1.5] 8.222949379045598\\n3 [1.8369701987210297e-16, 3.0, 1.5] 1.984668135635203\\n6 [-3.0, 3.6739403974420594e-16, 1.5] 0.5350864073867282\\n7 [-2.5980762113533165, -1.4999999999999991, 1.5] 29.274041158842973\\n8 [-1.5000000000000013, -2.598076211353315, 1.5] 0.9375079482570146\\n10 [1.5000000000000004, -2.598076211353316, 1.5] 12.28540507201612\\n11 [2.598076211353315, -1.5000000000000013, 1.5] 5.85045689653416\\n14 [1.5000000000000004, 2.598076211353316, 3.5] 41.44517739210782\\n15 [1.8369701987210297e-16, 3.0, 3.5] 16.582713760089455\\n19 [-2.5980762113533165, -1.4999999999999991, 3.5] 20.392208097548878\\n22 [1.5000000000000004, -2.598076211353316, 3.5] 59.83208488698549\\n23 [2.598076211353315, -1.5000000000000013, 3.5] 1.3955209618865752\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":21056,"completion_tokens":156,"cached_tokens":20597,"extra":{"provider_usage":{"input_tokens":21056,"output_tokens":156,"total_tokens":21212,"input_tokens_details":{"cached_tokens":20597,"cache_write_tokens":456},"output_tokens_details":{"reasoning_tokens":8}}}}},{"step_id":28,"timestamp":"2026-09-24T23:09:43.968387+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_ep26.webp"}}]}]},"metrics":{"prompt_tokens":21674,"completion_tokens":18,"cached_tokens":21053,"extra":{"provider_usage":{"input_tokens":21674,"output_tokens":18,"total_tokens":21692,"input_tokens_details":{"cached_tokens":21053,"cache_write_tokens":618},"output_tokens_details":{"reasoning_tokens":0}}}}},{"step_id":29,"timestamp":"2026-09-24T23:09:57.406014+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_27_1","function_name":"bash_command","arguments":{"command":"ls /opt; find / -path '*/site-packages/scipy' -type d 2>/dev/null | head; cp /tmp/params.json /tmp/fitparams.json","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_27_1","content":"{\"exit_code\":0,\"stdout\":\"blender-4.5.12-linux-x64\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":23268,"completion_tokens":296,"cached_tokens":21671,"extra":{"provider_usage":{"input_tokens":23268,"output_tokens":296,"total_tokens":23564,"input_tokens_details":{"cached_tokens":21671,"cache_write_tokens":1594},"output_tokens_details":{"reasoning_tokens":232}}}}},{"step_id":30,"timestamp":"2026-09-24T23:10:16.683102+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_28_1","function_name":"bash_command","arguments":{"command":"echo '{\"world\":0.05,\"lights\":[{\"loc\":[-2.6,-1.5,2.2],\"power\":47,\"size\":1.8},{\"loc\":[1.65,-2.55,3],\"power\":77,\"size\":1.8},{\"loc\":[1.1,2.8,3],\"power\":65,\"size\":1.8}],\"name\":\"three\"}' > /tmp/params.json\n/usr/local/bin/blender -b -t 8 --python /tmp/render.py > /tmp/threerender.log 2>&1\npython3 /tmp/montage.py three; pkill display; DISPLAY=:1 display /tmp/compare.png >/dev/null 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_28_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":23617,"completion_tokens":363,"cached_tokens":23265,"extra":{"provider_usage":{"input_tokens":23617,"output_tokens":363,"total_tokens":23980,"input_tokens_details":{"cached_tokens":23265,"cache_write_tokens":349},"output_tokens_details":{"reasoning_tokens":174}}}}},{"step_id":31,"timestamp":"2026-09-24T23:10:21.737863+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_ep29.webp"}}]}]},"metrics":{"prompt_tokens":24020,"completion_tokens":18,"cached_tokens":23614,"extra":{"provider_usage":{"input_tokens":24020,"output_tokens":18,"total_tokens":24038,"input_tokens_details":{"cached_tokens":23614,"cache_write_tokens":403},"output_tokens_details":{"reasoning_tokens":0}}}}},{"step_id":32,"timestamp":"2026-09-24T23:10:50.698209+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_30_1","function_name":"bash_command","arguments":{"command":"cp /tmp/params.json /tmp/threeparams.json\ncat > /tmp/opt.py <<'PY'\nimport bpy,numpy as np,json,math,os\nfrom mathutils import Vector\nbpy.ops.wm.open_mainfile(filepath='/app/base.blend');s=bpy.context.scene\ns.render.engine='CYCLES';s.cycles.device='CPU';s.cycles.samples=24;s.cycles.use_denoising=False;s.cycles.use_adaptive_sampling=False;s.cycles.seed=7\ns.render.resolution_x=384;s.render.resolution_y=216;s.render.resolution_percentage=100\nbg=s.world.node_tree.nodes.get('Background');bg.inputs['Color'].default_value=(1,1,1,1);bg.inputs['Strength'].default_value=.05\ncamd=bpy.data.cameras.new('Preview');cam=bpy.data.objects.new('Preview',camd);s.collection.objects.link(cam);s.camera=cam\nvs=json.load(open('/app/cameras.json'))['views']\nobjs=[]\nfor i in range(3):\n d=bpy.data.lights.new('L'+str(i),'AREA');o=bpy.data.objects.new(d.name,d);s.collection.objects.link(o);d.shape='DISK';objs.append(o)\ndef image(path):\n im=bpy.data.images.load(path,check_existing=False);im.colorspace_settings.name='Non-Color';a=np.empty(384*216*4,np.float32);im.pixels.foreach_get(a);bpy.data.images.remove(im);return a.reshape(216,384,4)[::-1,:,:3]\ndef red(a):return a.reshape(72,3,128,3,3).mean((1,3))\nrefs=[red(image('/app/lit-reference-%02d.png'%(i+1))) for i in range(6)]\nmasks=[np.abs(a.mean(2)-.2353)>.02 for a in refs]\n# use mask from ambient geometry instead\nmasks=[red(np.load('/tmp/basis-00-%d.npy'%i)).mean(2)<.049 for i in range(6)]\ny=np.concatenate([a[m].ravel() for a,m in zip(refs,masks)])\nx=[]\nfor l in json.load(open('/tmp/threeparams.json'))['lights']:\n loc=l['loc'];x.extend([math.atan2(loc[1],loc[0]),loc[2],math.log(l['power']),math.log(l['size'])])\nx=np.array(x)\ncounter=0\nbest=100\n\ndef eval(x):\n global counter,best\n lights=[]\n for i,o in enumerate(objs):\n az,z,lp,ls=x[4*i:4*i+4];loc=[3*math.cos(az),3*math.sin(az),z];o.location=loc;o.rotation_euler=(Vector([0,0,.25])-o.location).to_track_quat('-Z','Y').to_euler();o.data.energy=math.exp(lp);o.data.size=math.exp(ls);lights.append({'loc':loc,'power':math.exp(lp),'size':math.exp(ls)})\n out=[]\n for i,v in enumerate(vs):\n cam.location=v['location'];cam.rotation_mode='QUATERNION';cam.rotation_quaternion=v['rotation_quaternion'];camd.lens=55;camd.sensor_width=36;camd.clip_start=.01;camd.clip_end=1000\n s.render.image_settings.file_format='PNG';s.render.filepath='/tmp/opt-v%02d.png'%(i+1);bpy.ops.render.render(write_still=True);out.append(red(image(s.render.filepath))[masks[i]].ravel())\n r=np.concatenate(out)-y;loss=np.mean(r*r);counter+=1\n print('OPT',counter,loss,x.tolist(),flush=True)\n if loss /tmp/opt.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":25614,"completion_tokens":1627,"cached_tokens":24017,"extra":{"provider_usage":{"input_tokens":25614,"output_tokens":1627,"total_tokens":27241,"input_tokens_details":{"cached_tokens":24017,"cache_write_tokens":1594},"output_tokens_details":{"reasoning_tokens":369}}}}},{"step_id":33,"timestamp":"2026-09-24T23:12:47.085822+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_31_1","function_name":"bash_command","arguments":{"command":"sleep 110; grep -E '^OPT|^ITER|Error' /tmp/opt.log | tail -10; cat /tmp/bestparams.json","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_31_1","content":"{\"exit_code\":0,\"stdout\":\"OPT 11 0.0010102645 [-2.618314331457818, 2.2, 3.8501476017100584, 0.5877866649021191, -0.9964914966201949, 3.0, 4.343805421853684, 0.5877866649021191, 1.1964627107873127, 3.16, 4.174387269895637, 0.5877866649021191]\\nOPT 12 0.00087621354 [-2.618314331457818, 2.2, 3.8501476017100584, 0.5877866649021191, -0.9964914966201949, 3.0, 4.343805421853684, 0.5877866649021191, 1.1964627107873127, 3.0, 4.254387269895637, 0.5877866649021191]\\nOPT 13 0.0009261955 [-2.618314331457818, 2.2, 3.8501476017100584, 0.5877866649021191, -0.9964914966201949, 3.0, 4.343805421853684, 0.5877866649021191, 1.1964627107873127, 3.0, 4.174387269895637, 0.687786664902119]\\nOPT 14 0.00029908834 [-2.59440249583205, 2.119719867408276, 3.9291492146713507, 0.5474526748944801, -0.8353074688291716, 2.636480063199997, 4.427018147671841, 0.7205855005373043, 1.2177969938411877, 2.8545778691768646, 4.172920603547358, 0.6225553207983059]\\nITER 0 LOSS 0.00029908834\\nOPT 15 0.0004225249 [-2.5244024958320503, 2.119719867408276, 3.9291492146713507, 0.5474526748944801, -0.8353074688291716, 2.636480063199997, 4.427018147671841, 0.7205855005373043, 1.2177969938411877, 2.8545778691768646, 4.172920603547358, 0.6225553207983059]\\nOPT 16 0.00033289648 [-2.59440249583205, 2.279719867408276, 3.9291492146713507, 0.5474526748944801, -0.8353074688291716, 2.636480063199997, 4.427018147671841, 0.7205855005373043, 1.2177969938411877, 2.8545778691768646, 4.172920603547358, 0.6225553207983059]\\nOPT 17 0.00030008607 [-2.59440249583205, 2.119719867408276, 4.009149214671351, 0.5474526748944801, -0.8353074688291716, 2.636480063199997, 4.427018147671841, 0.7205855005373043, 1.2177969938411877, 2.8545778691768646, 4.172920603547358, 0.6225553207983059]\\nOPT 18 0.00030906583 [-2.59440249583205, 2.119719867408276, 3.9291492146713507, 0.6474526748944801, -0.8353074688291716, 2.636480063199997, 4.427018147671841, 0.7205855005373043, 1.2177969938411877, 2.8545778691768646, 4.172920603547358, 0.6225553207983059]\\nOPT 19 0.0004342985 [-2.59440249583205, 2.119719867408276, 3.9291492146713507, 0.5474526748944801, -0.7653074688291717, 2.636480063199997, 4.427018147671841, 0.7205855005373043, 1.2177969938411877, 2.8545778691768646, 4.172920603547358, 0.6225553207983059]\\n{\\\"world\\\": 0.05, \\\"lights\\\": [{\\\"loc\\\": [-2.5619694700588695, -1.5608691279176083, 2.119719867408276], \\\"power\\\": 50.86368517839399, \\\"size\\\": 1.728843477591497}, {\\\"loc\\\": [2.0128491761019434, -2.2245085287015036, 2.636480063199997], \\\"power\\\": 83.68151858446463, \\\"size\\\": 2.0556364346023392}, {\\\"loc\\\": [1.0371412572350573, 2.8150200732038986, 2.8545778691768646], \\\"power\\\": 64.90473656427642, \\\"size\\\": 1.8636842735951311}], \\\"name\\\": \\\"best\\\"}\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":27281,"completion_tokens":79,"cached_tokens":25611,"extra":{"provider_usage":{"input_tokens":27281,"output_tokens":79,"total_tokens":27360,"input_tokens_details":{"cached_tokens":25611,"cache_write_tokens":1667},"output_tokens_details":{"reasoning_tokens":22}}}}},{"step_id":34,"timestamp":"2026-09-24T23:12:52.243440+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_32_1","function_name":"bash_command","arguments":{"command":"python3 /tmp/montage.py best; pkill display; DISPLAY=:1 display /tmp/compare.png >/dev/null 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_32_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":28699,"completion_tokens":54,"cached_tokens":27278,"extra":{"provider_usage":{"input_tokens":28699,"output_tokens":54,"total_tokens":28753,"input_tokens_details":{"cached_tokens":27278,"cache_write_tokens":1418},"output_tokens_details":{"reasoning_tokens":0}}}}},{"step_id":35,"timestamp":"2026-09-24T23:12:57.560134+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_ep33.webp"}}]}]},"metrics":{"prompt_tokens":28793,"completion_tokens":18,"cached_tokens":28696,"extra":{"provider_usage":{"input_tokens":28793,"output_tokens":18,"total_tokens":28811,"input_tokens_details":{"cached_tokens":28696,"cache_write_tokens":94},"output_tokens_details":{"reasoning_tokens":0}}}}},{"step_id":36,"timestamp":"2026-09-24T23:14:55.193890+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_34_1","function_name":"bash_command","arguments":{"command":"sleep 110; grep -E '^OPT|^ITER|Error' /tmp/opt.log | tail -4","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_34_1","content":"{\"exit_code\":0,\"stdout\":\"OPT 38 0.00027822945 [-2.5937930492240544, 2.1060432706028225, 3.959314266712762, 0.548435835000014, -0.85228670498492, 2.508763998746872, 4.40303786356213, 0.7309162641827744, 1.2227841186604962, 2.8220262974500656, 4.231623973865175, 0.6663384527851623]\\nOPT 39 0.00025978274 [-2.5937930492240544, 2.1060432706028225, 3.959314266712762, 0.548435835000014, -0.85228670498492, 2.508763998746872, 4.40303786356213, 0.7309162641827744, 1.2227841186604962, 2.8220262974500656, 4.151623973865175, 0.7663384527851623]\\nOPT 40 0.0002458975 [-2.593595515633219, 2.113349752314389, 3.9751989157212746, 0.534764365125334, -0.8484989876686203, 2.435149669647217, 4.3705625150001275, 0.7308035267047264, 1.2232613243022967, 2.7936851661652327, 4.13456914411869, 0.6879779768128675]\\nITER 2 LOSS 0.0002458975\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":30387,"completion_tokens":88,"cached_tokens":28790,"extra":{"provider_usage":{"input_tokens":30387,"output_tokens":88,"total_tokens":30475,"input_tokens_details":{"cached_tokens":28790,"cache_write_tokens":1594},"output_tokens_details":{"reasoning_tokens":39}}}}},{"step_id":37,"timestamp":"2026-09-24T23:15:05.187757+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'\nimport numpy as np\nfrom PIL import Image\nfor i in range(6):\n a=np.array(Image.open('/app/lit-reference-%02d.png'%(i+1)))[:,:,:3]/255\n b=np.array(Image.open('/tmp/best-v%02d.png'%(i+1)))[:,:,:3]/255\n m=np.abs(a.mean(2)-a[0,0].mean())>.01\n print(i,a[m].mean(),b[m].mean(),np.mean(abs(a[m]-b[m])),a[0,0],b[0,0])\nPY\npython3 /tmp/montage.py best; pkill display; DISPLAY=:1 display /tmp/compare.png >/dev/null 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_35_1","content":"{\"exit_code\":0,\"stdout\":\"0 0.21137264954687338 0.21053810569771783 0.01638309600521401 [0.23137255 0.23137255 0.23137255] [0.22745098 0.22745098 0.22745098]\\n1 0.19746958594560596 0.2015037284588547 0.0124592544257997 [0.23137255 0.23137255 0.23137255] [0.22745098 0.22745098 0.22745098]\\n2 0.2255567346676883 0.2208493690500148 0.017126957878198134 [0.23137255 0.23137255 0.23137255] [0.22745098 0.22745098 0.22745098]\\n3 0.17955915713367218 0.1777983556567376 0.01702976738460101 [0.23137255 0.23137255 0.23137255] [0.22745098 0.22745098 0.22745098]\\n4 0.25121516959880125 0.2447103257640342 0.012910795938928932 [0.23137255 0.23137255 0.23137255] [0.22745098 0.22745098 0.22745098]\\n5 0.18572051333309936 0.1841307047113862 0.009969291371826703 [0.23137255 0.23137255 0.23137255] [0.22745098 0.22745098 0.22745098]\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":30904,"completion_tokens":292,"cached_tokens":30384,"extra":{"provider_usage":{"input_tokens":30904,"output_tokens":292,"total_tokens":31196,"input_tokens_details":{"cached_tokens":30384,"cache_write_tokens":517},"output_tokens_details":{"reasoning_tokens":103}}}}},{"step_id":38,"timestamp":"2026-09-24T23:17:02.436492+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_36_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nimport numpy as np\nfrom PIL import Image\nx=np.load('/tmp/lutx.npy');y=np.array(Image.open('/tmp/lut.png'))[0,:,0]\nfor v in [58,59,60]:print(v,x[y==v].min(),x[y==v].max())\nPY\nsleep 110; grep -E '^OPT|^ITER|Error' /tmp/opt.log | tail -4","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_36_1","content":"{\"exit_code\":0,\"stdout\":\"58 0.049405225 0.05075683\\n59 0.05092836 0.052145414\\n60 0.052321635 0.053571984\\nOPT 58 0.0003256771 [-2.593750017158904, 2.123144296742976, 3.9860406448406946, 0.5276608404888568, -0.7762164145256001, 2.394561141729355, 4.346819647366427, 0.7132175525180675, 1.2235039433790404, 2.775025874376297, 4.1249933301099775, 0.6982256115366501]\\nOPT 59 0.00027821827 [-2.593750017158904, 2.123144296742976, 3.9860406448406946, 0.5276608404888568, -0.8462164145256, 2.554561141729355, 4.346819647366427, 0.7132175525180675, 1.2235039433790404, 2.775025874376297, 4.1249933301099775, 0.6982256115366501]\\nOPT 60 0.00026435152 [-2.593750017158904, 2.123144296742976, 3.9860406448406946, 0.5276608404888568, -0.8462164145256, 2.394561141729355, 4.426819647366427, 0.7132175525180675, 1.2235039433790404, 2.775025874376297, 4.1249933301099775, 0.6982256115366501]\\nOPT 61 0.00025344573 [-2.593750017158904, 2.123144296742976, 3.9860406448406946, 0.5276608404888568, -0.8462164145256, 2.394561141729355, 4.346819647366427, 0.8132175525180675, 1.2235039433790404, 2.775025874376297, 4.1249933301099775, 0.6982256115366501]\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":31639,"completion_tokens":177,"cached_tokens":30901,"extra":{"provider_usage":{"input_tokens":31639,"output_tokens":177,"total_tokens":31816,"input_tokens_details":{"cached_tokens":30901,"cache_write_tokens":735},"output_tokens_details":{"reasoning_tokens":53}}}}},{"step_id":39,"timestamp":"2026-09-24T23:17:12.345723+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'\nimport numpy as np\nfrom PIL import Image\nfor i in range(6):\n a=np.array(Image.open('/app/lit-reference-%02d.png'%(i+1)))[:,:,:3]/255;b=np.array(Image.open('/tmp/best-v%02d.png'%(i+1)))[:,:,:3]/255;m=(np.load('/tmp/basis-00-%d.npy'%i).mean(2)<.04)\n print(i,a[m].mean(0),b[m].mean(0))\nPY","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_37_1","content":"{\"exit_code\":0,\"stdout\":\"0 [0.25261571 0.20841525 0.17513737] [0.25112116 0.20781107 0.17482744]\\n1 [0.2423878 0.19371265 0.16056137] [0.24594836 0.19796438 0.16520948]\\n2 [0.25982988 0.22150389 0.19462811] [0.25504827 0.21702598 0.19052227]\\n3 [0.22834035 0.18133832 0.14628243] [0.22504731 0.18091601 0.14809703]\\n4 [0.29246826 0.24697782 0.2133273 ] [0.28470582 0.24045029 0.20785106]\\n5 [0.23989208 0.18711679 0.14738099] [0.23853342 0.18636988 0.14699498]\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":32400,"completion_tokens":248,"cached_tokens":31636,"extra":{"provider_usage":{"input_tokens":32400,"output_tokens":248,"total_tokens":32648,"input_tokens_details":{"cached_tokens":31636,"cache_write_tokens":761},"output_tokens_details":{"reasoning_tokens":105}}}}},{"step_id":40,"timestamp":"2026-09-24T23:19:06.386903+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_38_1","function_name":"bash_command","arguments":{"command":"sleep 105; grep -E '^OPT|^ITER|Error' /tmp/opt.log | tail -4; pgrep -a blender; cat /tmp/bestparams.json","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_38_1","content":"{\"exit_code\":0,\"stdout\":\"OPT 79 0.00023556108 [-2.5939776764447875, 2.1416866349056365, 3.9996486869914305, 0.5184388573466596, -0.8442809084315858, 2.3604765059426427, 4.324249002861105, 0.6811468598894455, 1.2237510717523434, 2.761346043087542, 4.119795252811277, 0.7355728537727875]\\nITER 5 LOSS 0.00023556108\\nOPT 80 0.00036542283 [-2.5239776764447877, 2.1416866349056365, 3.9996486869914305, 0.5184388573466596, -0.8442809084315858, 2.3604765059426427, 4.324249002861105, 0.6811468598894455, 1.2237510717523434, 2.761346043087542, 4.119795252811277, 0.7355728537727875]\\nOPT 81 0.00025554048 [-2.5939776764447875, 2.3016866349056366, 3.9996486869914305, 0.5184388573466596, -0.8442809084315858, 2.3604765059426427, 4.324249002861105, 0.6811468598894455, 1.2237510717523434, 2.761346043087542, 4.119795252811277, 0.7355728537727875]\\n472 /usr/local/bin/blender\\n6662 /usr/local/bin/blender -b -t 8 --python /tmp/opt.py\\n{\\\"world\\\": 0.05, \\\"lights\\\": [{\\\"loc\\\": [-2.5613061514310465, -1.5619573613391244, 2.1416866349056365], \\\"power\\\": 54.57897236167374, \\\"size\\\": 1.6794038132152624}, {\\\"loc\\\": [1.9928069116662974, -2.2424809057860524, 2.3604765059426427], \\\"power\\\": 75.50878467978646, \\\"size\\\": 1.9761427920330625}, {\\\"loc\\\": [1.0203621236134126, 2.821145359015577, 2.761346043087542], \\\"power\\\": 61.54663947287203, \\\"size\\\": 2.086677010985257}], \\\"name\\\": \\\"best\\\"}\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":32930,"completion_tokens":282,"cached_tokens":32397,"extra":{"provider_usage":{"input_tokens":32930,"output_tokens":282,"total_tokens":33212,"input_tokens_details":{"cached_tokens":32397,"cache_write_tokens":530},"output_tokens_details":{"reasoning_tokens":219}}}}},{"step_id":41,"timestamp":"2026-09-24T23:20:33.636765+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_39_1","function_name":"bash_command","arguments":{"command":"sed -i \"s/d.shape='DISK'/d.shape=l.get('shape','DISK')/\" /tmp/render.py\ncat > /tmp/loss.py <<'PY'\nimport numpy as np,sys\nfrom PIL import Image\nrs=[]\ndef red(a):return a.reshape(72,3,128,3,3).mean((1,3))\nfor i in range(6):\n a=red(np.array(Image.open('/app/lit-reference-%02d.png'%(i+1)))[:,:,:3]/255);b=red(np.array(Image.open('/tmp/'+sys.argv[1]+'-v%02d.png'%(i+1)))[:,:,:3]/255);m=red(np.load('/tmp/basis-00-%d.npy'%i)).mean(2)<.049\n rs.append((a-b)[m].flatten())\nprint(sys.argv[1],np.mean(np.concatenate(rs)**2))\nPY\nsleep 70\ncp /tmp/bestparams.json /tmp/bestdisk.json\npython3 - <<'PY'\nimport json,math\np=json.load(open('/tmp/bestdisk.json'));p['name']='square';p['world']=.0515\nfor l in p['lights']:l['shape']='SQUARE';l['size']*=math.sqrt(math.pi/4)\njson.dump(p,open('/tmp/params.json','w'))\nPY\n/usr/local/bin/blender -b -t 8 --python /tmp/render.py >/tmp/square.log 2>&1\npython3 /tmp/loss.py square; python3 /tmp/loss.py best","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_39_1","content":"{\"exit_code\":0,\"stdout\":\"square 0.00023688273121458568\\nbest 0.00023428212023780554\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":33871,"completion_tokens":434,"cached_tokens":32927,"extra":{"provider_usage":{"input_tokens":33871,"output_tokens":434,"total_tokens":34305,"input_tokens_details":{"cached_tokens":32927,"cache_write_tokens":941},"output_tokens_details":{"reasoning_tokens":61}}}}},{"step_id":42,"timestamp":"2026-09-24T23:20:48.406204+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_40_1","function_name":"bash_command","arguments":{"command":"cp /tmp/bestparams.json /tmp/bestangular.json\npython3 - <<'PY'\np='/tmp/opt.py';s=open(p).read();a=s.index('x=[]\\n');b=s.index('counter=0',a)\ns=s[:a]+\"base=json.load(open('/tmp/bestangular.json'))['lights']\\nx=np.zeros(6)\\n\"+s[b:]\na=s.index(' az,z,lp,ls=');b=s.index('\\n out=[]',a)\ns=s[:a]+''' l=base[i];sc=math.exp(x[i*2]);power=math.exp(x[i*2+1]);loc=np.array(l['loc']);target=np.array([0,0,.25]);loc=(target+(loc-target)*sc).tolist();o.location=loc;o.rotation_euler=(Vector([0,0,.25])-o.location).to_track_quat('-Z','Y').to_euler();o.data.energy=l['power']*sc*sc*power;o.data.size=l['size']*sc;lights.append({'loc':loc,'power':o.data.energy,'size':o.data.size})'''+s[b:]\ns=s.replace(\"default_value=.05\",\"default_value=.0515\").replace(\"'world':.05\",\"'world':.0515\")\ns=s.replace(\"steps=np.tile([.07,.16,.08,.10],3)\",\"steps=np.tile([.12,.06],3)\").replace('range(7)','range(4)').replace(\"np.tile([.18,.6,.35,.4],3)\",\"np.tile([.4,.15],3)\")\ns=s.replace(\" for i in range(3):xx[i*4+1]=np.clip(xx[i*4+1],.6,6);xx[i*4+3]=np.clip(xx[i*4+3],math.log(.3),math.log(5))\",\" for i in range(3):xx[i*2]=np.clip(xx[i*2],-1,1.5)\")\ns=s.replace('/tmp/bestparams.json','/tmp/radialparams.json').replace('/tmp/best-v','/tmp/radial-v')\nopen('/tmp/radial.py','w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /usr/local/bin/blender -b -t 8 --python /tmp/radial.py >/tmp/radial.log 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_40_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":34370,"completion_tokens":772,"cached_tokens":33868,"extra":{"provider_usage":{"input_tokens":34370,"output_tokens":772,"total_tokens":35142,"input_tokens_details":{"cached_tokens":33868,"cache_write_tokens":499},"output_tokens_details":{"reasoning_tokens":213}}}}},{"step_id":43,"timestamp":"2026-09-24T23:22:43.744117+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_41_1","function_name":"bash_command","arguments":{"command":"sleep 110; grep -E '^OPT|^ITER|Error' /tmp/radial.log | tail -6; cat /tmp/radialparams.json","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_41_1","content":"{\"exit_code\":0,\"stdout\":\"OPT 14 0.00013519773 [0.18248112499713898, 0.027132149785757065, 0.2648879587650299, -0.02118554338812828, 0.17115643620491028, 0.04584934882819652]\\nOPT 15 0.000107366184 [0.28760170191526413, 0.0560174286365509, 0.3917897492647171, -0.024028240935876966, 0.27143295109272003, -0.015312058851122856]\\nITER 1 LOSS 0.000107366184\\nOPT 16 0.0001080686 [0.4076017019152641, 0.0560174286365509, 0.3917897492647171, -0.024028240935876966, 0.27143295109272003, -0.015312058851122856]\\nOPT 17 0.00011213282 [0.28760170191526413, 0.1160174286365509, 0.3917897492647171, -0.024028240935876966, 0.27143295109272003, -0.015312058851122856]\\nOPT 18 0.0001215392 [0.28760170191526413, 0.0560174286365509, 0.5117897492647171, -0.024028240935876966, 0.27143295109272003, -0.015312058851122856]\\n{\\\"world\\\": 0.0515, \\\"lights\\\": [{\\\"loc\\\": [-3.414613883246525, -2.0827482752032256, 2.7846624050211384], \\\"power\\\": 103.31895446777344, \\\"size\\\": 2.237168550491333}, {\\\"loc\\\": [2.949500116065797, -3.3172432190704186, 3.355687173886156], \\\"power\\\": 159.9608917236328, \\\"size\\\": 2.8876843452453613}, {\\\"loc\\\": [1.3390776184561408, 3.7007104110545557, 3.5399121414220462], \\\"power\\\": 104.2258071899414, \\\"size\\\": 2.764958381652832}], \\\"name\\\": \\\"best\\\"}\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":35182,"completion_tokens":86,"cached_tokens":34367,"extra":{"provider_usage":{"input_tokens":35182,"output_tokens":86,"total_tokens":35268,"input_tokens_details":{"cached_tokens":34367,"cache_write_tokens":812},"output_tokens_details":{"reasoning_tokens":27}}}}},{"step_id":44,"timestamp":"2026-09-24T23:24:29.593470+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_42_1","function_name":"bash_command","arguments":{"command":"sleep 100; grep -E '^OPT|^ITER|Error' /tmp/radial.log | tail -5; python3 /tmp/montage.py radial; pkill display; DISPLAY=:1 display /tmp/compare.png >/dev/null 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_42_1","content":"{\"exit_code\":0,\"stdout\":\"OPT 26 0.00011385744 [0.34599539265036583, 0.07552967593073845, 0.41829220950603485, 0.03474120196420699, 0.3263748586177826, -0.01150440308265388]\\nOPT 27 0.00010453419 [0.34599539265036583, 0.07552967593073845, 0.41829220950603485, -0.025258798035793006, 0.4463748586177826, -0.01150440308265388]\\nOPT 28 0.00011255629 [0.34599539265036583, 0.07552967593073845, 0.41829220950603485, -0.025258798035793006, 0.3263748586177826, 0.04849559691734612]\\nOPT 29 0.000101055484 [0.395007211714983, 0.08969030436128378, 0.49569171667099, -0.02789910265710205, 0.34424511156976223, -0.015388003550469875]\\nITER 3 LOSS 0.000101055484\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":35867,"completion_tokens":107,"cached_tokens":35179,"extra":{"provider_usage":{"input_tokens":35867,"output_tokens":107,"total_tokens":35974,"input_tokens_details":{"cached_tokens":35179,"cache_write_tokens":685},"output_tokens_details":{"reasoning_tokens":25}}}}},{"step_id":45,"timestamp":"2026-09-24T23:24:35.327632+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_ep43.webp"}}]}]},"metrics":{"prompt_tokens":36309,"completion_tokens":18,"cached_tokens":35864,"extra":{"provider_usage":{"input_tokens":36309,"output_tokens":18,"total_tokens":36327,"input_tokens_details":{"cached_tokens":35864,"cache_write_tokens":442},"output_tokens_details":{"reasoning_tokens":0}}}}},{"step_id":46,"timestamp":"2026-09-24T23:24:45.377185+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_44_1","function_name":"bash_command","arguments":{"command":"python3 - <<'PY'\nfrom PIL import Image\nim=Image.new('RGB',(1152,648));im.paste(Image.open('/app/lit-reference-03.png').resize((576,324)),(0,0));im.paste(Image.open('/tmp/radial-v03.png').resize((576,324)),(576,0));im.paste(Image.open('/app/lit-reference-04.png').resize((576,324)),(0,324));im.paste(Image.open('/tmp/radial-v04.png').resize((576,324)),(576,324));im.save('/tmp/detail.png')\nPY\npkill display; DISPLAY=:1 display /tmp/detail.png >/dev/null 2>&1 &","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_44_1","content":"{\"exit_code\":0,\"stdout\":\"\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":37903,"completion_tokens":247,"cached_tokens":36306,"extra":{"provider_usage":{"input_tokens":37903,"output_tokens":247,"total_tokens":38150,"input_tokens_details":{"cached_tokens":36306,"cache_write_tokens":1594},"output_tokens_details":{"reasoning_tokens":66}}}}},{"step_id":47,"timestamp":"2026-09-24T23:24:52.178755+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_ep45.webp"}}]}]},"metrics":{"prompt_tokens":38190,"completion_tokens":18,"cached_tokens":37900,"extra":{"provider_usage":{"input_tokens":38190,"output_tokens":18,"total_tokens":38208,"input_tokens_details":{"cached_tokens":37900,"cache_write_tokens":287},"output_tokens_details":{"reasoning_tokens":0}}}}},{"step_id":48,"timestamp":"2026-09-24T23:25:05.598888+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_46_1","function_name":"bash_command","arguments":{"command":"mkdir -p /home/ubuntu/task/output/submission\npython3 - <<'PY'\nimport json\np=json.load(open('/tmp/radialparams.json'));p['save']='/home/ubuntu/task/output/submission/result.blend';p['views']=[]\njson.dump(p,open('/tmp/params.json','w'))\nprint(json.dumps(p,indent=2))\nPY\n/usr/local/bin/blender -b -t 8 --python /tmp/render.py > /tmp/finalsave.log 2>&1\ncat /tmp/finalsave.log; ls -lah /home/ubuntu/task/output/submission","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_46_1","content":"{\"exit_code\":0,\"stdout\":\"{\\n \\\"world\\\": 0.0515,\\n \\\"lights\\\": [\\n {\\n \\\"loc\\\": [\\n -3.8017821038483812,\\n -2.3189020458032967,\\n 3.0720567537623853\\n ],\\n \\\"power\\\": 132.4631805419922,\\n \\\"size\\\": 2.4908313751220703\\n },\\n {\\n \\\"loc\\\": [\\n 3.2724458484805607,\\n -3.68045376283183,\\n 3.695734090161452\\n ],\\n \\\"power\\\": 196.14662170410156,\\n \\\"size\\\": 3.203861713409424\\n },\\n {\\n \\\"loc\\\": [\\n 1.44021613055171,\\n 3.9802194846974728,\\n 3.7883942415800598\\n ],\\n \\\"power\\\": 120.55524444580078,\\n \\\"size\\\": 2.9737915992736816\\n }\\n ],\\n \\\"name\\\": \\\"best\\\",\\n \\\"save\\\": \\\"/home/ubuntu/task/output/submission/result.blend\\\",\\n \\\"views\\\": []\\n}\\nInfo: Saved as \\\"result.blend\\\"\\nBlender 4.5.12 LTS (hash 84afd5f785f7 built 2026-07-21 01:31:22)\\nRead blend: \\\"/app/base.blend\\\"\\n\\nBlender quit\\nlrwxrwxrwx 1 ubuntu ubuntu 4 Sep 24 23:02 /home/ubuntu/task/output/submission -> /app\\n\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":39784,"completion_tokens":360,"cached_tokens":38187,"extra":{"provider_usage":{"input_tokens":39784,"output_tokens":360,"total_tokens":40144,"input_tokens_details":{"cached_tokens":38187,"cache_write_tokens":1594},"output_tokens_details":{"reasoning_tokens":201}}}}},{"step_id":49,"timestamp":"2026-09-24T23:25:19.214174+00:00","source":"agent","model_name":"gpt-6-astra","message":"","tool_calls":[{"tool_call_id":"call_47_1","function_name":"bash_command","arguments":{"command":"ls -lh /home/ubuntu/task/output/submission/result.blend\ncat >/tmp/verify.py <<'PY'\nimport bpy,hashlib,json\n\ndef snapshot(p):\n bpy.ops.wm.open_mainfile(filepath=p)\n s=bpy.context.scene\n o=bpy.data.objects.get('asset_001')\n h=hashlib.sha256()\n for v in o.data.vertices:h.update(str(tuple(v.co)).encode())\n return {'objects':[(o.name,o.type,tuple(o.matrix_world[i][j] for i in range(4) for j in range(4))) for o in s.objects if o.type!='LIGHT'],'mesh':h.hexdigest(),'materials':[(m.name,len(m.node_tree.nodes)) for m in bpy.data.materials],'render':(s.render.engine,s.render.resolution_x,s.render.resolution_y,s.render.resolution_percentage,s.cycles.samples,s.cycles.seed,s.cycles.use_denoising,s.cycles.use_adaptive_sampling),'color':(s.view_settings.view_transform,s.view_settings.look,s.view_settings.exposure),'images':[(im.name,tuple(im.size),bool(im.packed_file)) for im in bpy.data.images]}\na=snapshot('/app/base.blend');b=snapshot('/app/result.blend');print('PRESERVATION CHECK',a==b);print('DIFFERENCES',[(k,a[k],b[k]) for k in a if a[k]!=b[k]])\nPY\n/usr/local/bin/blender -b -t 4 --python /tmp/verify.py > /tmp/verify.log 2>&1; cat /tmp/verify.log","timeout_sec":120}}],"observation":{"results":[{"source_call_id":"call_47_1","content":"{\"exit_code\":0,\"stdout\":\"-rw-rw-r-- 1 ubuntu ubuntu 41M Sep 24 23:25 /home/ubuntu/task/output/submission/result.blend\\nPRESERVATION CHECK True\\nDIFFERENCES []\\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\",\"stderr\":\"\",\"timed_out\":false,\"stdout_truncated\":false,\"stderr_truncated\":false}"}]},"metrics":{"prompt_tokens":40575,"completion_tokens":418,"cached_tokens":39781,"extra":{"provider_usage":{"input_tokens":40575,"output_tokens":418,"total_tokens":40993,"input_tokens_details":{"cached_tokens":39781,"cache_write_tokens":791},"output_tokens_details":{"reasoning_tokens":50}}}}},{"step_id":50,"timestamp":"2026-09-24T23:25:23.819067+00:00","source":"agent","model_name":"gpt-6-astra","message":"Analysis: Saved the lit scene to `/home/ubuntu/task/output/submission/result.blend`.\n\nOnly lights and world lighting were changed; protected scene elements were preserved.","tool_calls":[{"tool_call_id":"call_48_task_complete","function_name":"mark_task_complete","arguments":{"result":null}}],"observation":{"results":[{"content":"Saved the lit scene to `/home/ubuntu/task/output/submission/result.blend`.\n\nOnly lights and world lighting were changed; protected scene elements were preserved."}]},"metrics":{"prompt_tokens":41142,"completion_tokens":35,"cached_tokens":40572,"extra":{"provider_usage":{"input_tokens":41142,"output_tokens":35,"total_tokens":41177,"input_tokens_details":{"cached_tokens":40572,"cache_write_tokens":567},"output_tokens_details":{"reasoning_tokens":0}}}}}],"final_metrics":{"total_prompt_tokens":1041855,"total_completion_tokens":11109,"total_cached_tokens":1000569},"extra":{"early_termination_reason":"task_complete"}}