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          "arguments": "{\"command\": \"python - <<'PY'\\nimport json\\nfrom pathlib import Path\\np=Path('/workdir/data/observed.json')\\nd=json.loads(p.read_text())\\nd.append([{'v':-1},[-0.5144470970590475,-0.7430712042898234]])\\np.write_text(json.dumps(d))\\nPY\\npython /workdir/data/fit.py\\npython - <<'PY'\\nimport json,numpy as np\\nfrom pathlib import Path\\nd=np.load('/workdir/data/posterior.npz')\\nt,w=d['targets'],d['weights']\\npred=w@t\\nmean=pred.copy()\\nfor i in range(100):\\n    sqdist=np.sum((t-pred)**2,axis=1)\\n    wt=w/(.0196+sqdist)**2\\n    new=wt@t/wt.sum()\\n    if np.max(np.abs(new-pred))<1e-12:\\n        pred=new\\n        break\\n    pred=new\\nout=pred.reshape(8,2)\\nout[6,0]=.6\\nout[7,0]=-.6\\np=Path('/workdir/data/predictions.json')\\np.write_text(json.dumps({'predictions':out.tolist()},allow_nan=False))\\ncheck=json.loads(p.read_text())\\nassert set(check)=={'predictions'}\\nassert np.array(check['predictions']).shape==(8,2)\\nassert np.isfinite(check['predictions']).all()\\nprint('Final predictions:',p.read_text())\\nprint('IRLS iterations',i+1,'max shift from posterior mean',np.max(np.abs(pred-mean)))\\nprint('Posterior expected score of submitted predictions',np.sum(w*.0196/(.0196+np.sum((t-pred)**2,axis=1))))\\nprint('Posterior Monte Carlo standard error bound', np.sqrt(1/(w*w).sum())**-1)\\nprint('Submission shape and finiteness verified.')\\nPY\"}"
        },
        "type": "function"
      }
    },
    {
      "timestamp": "2026-10-17T08:59:20.510512Z",
      "type": "tool_call_completed",
      "tool_call_id": "call_7ed425a35797",
      "result": {
        "meta": null,
        "content": [
          {
            "type": "text",
            "text": "{\"stdout\":\"n 270357 ess 228783.105104099 map [0.13640636988895613, -0.16918963910904491, -0.5027063297565888, 0.7236758058659147, 0.7870975514813119, 0.12682411909659938, 0.35465662816173504] posterior_mean [0.13566320650452127, -0.16702947451432099, -0.5045064221605191, 0.7254472840750454, 0.7879590956418154, 0.12748967905658448, 0.35560368873430914] chisq 7.141217741289778\\nprediction [[-0.041008122457319274, -0.5262728751373951], [0.806932309305203, 0.5720153994641008], [-0.6897356199317131, -0.71233191510456], [0.30223130870895065, 0.42765342779619164], [0.688202492439861, 0.5174002275850489], [-0.36656994776369267, -0.45084038755643274], [0.6000000000000002, 0.22516419172979035], [-0.6000000000000002, -0.4014230641608706]] std [[0.03772224311137692, 0.028410675894041268], [0.014787674205631481, 0.0249321483520948], [0.01836850458052018, 0.019297360607706354], [0.03473684336643916, 0.03016390520715308], [0.0173064387532016, 0.02038958332238973], [0.021183155051521203, 0.020201575995657618], [2.2204460492503114e-16, 0.025459182700742333], [2.2204460492503114e-16, 0.043895086949409266]]\\nlinear_expected_variance_reductions [(0.0011638334836940835, 'clamp-u-3'), (0.001142630072122632, 'clamp-u-0'), (0.0005930505211542116, 'clamp-u-1'), (0.0005603954125526062, 'clamp-v-2'), (0.0005519447603522065, 'clamp-u-2'), (0.0005136704977081952, 'clamp-x-0'), (0.000496431417733489, 'clamp-x-1'), (0.0004938560751428678, 'clamp-v-1'), (0.0004602940916085027, 'clamp-x-3'), (0.00020443343896444106, 'clamp-x-2')]\\nFinal predictions: {\\\"predictions\\\": [[-0.041000203121065094, -0.5265991468967948], [0.8069515552811751, 0.5717933270596295], [-0.6895393976334234, -0.7125520154883291], [0.3026041560450008, 0.42748709903580806], [0.6882463135727455, 0.5171167552306318], [-0.366276248942072, -0.451080346940951], [0.6, 0.2248808112522991], [-0.6, -0.40235329985113005]]}\\nIRLS iterations 16 max shift from posterior mean 0.0009302356902594622\\nPosterior expected score of submitted predictions 0.6842141573984033\\nPosterior Monte Carlo standard error bound 0.0020906822158185913\\nSubmission shape and finiteness verified.\\n\",\"exit_code\":0}",
            "annotations": null,
            "meta": null
          }
        ],
        "structuredContent": {
          "stdout": "n 270357 ess 228783.105104099 map [0.13640636988895613, -0.16918963910904491, -0.5027063297565888, 0.7236758058659147, 0.7870975514813119, 0.12682411909659938, 0.35465662816173504] posterior_mean [0.13566320650452127, -0.16702947451432099, -0.5045064221605191, 0.7254472840750454, 0.7879590956418154, 0.12748967905658448, 0.35560368873430914] chisq 7.141217741289778\nprediction [[-0.041008122457319274, -0.5262728751373951], [0.806932309305203, 0.5720153994641008], [-0.6897356199317131, -0.71233191510456], [0.30223130870895065, 0.42765342779619164], [0.688202492439861, 0.5174002275850489], [-0.36656994776369267, -0.45084038755643274], [0.6000000000000002, 0.22516419172979035], [-0.6000000000000002, -0.4014230641608706]] std [[0.03772224311137692, 0.028410675894041268], [0.014787674205631481, 0.0249321483520948], [0.01836850458052018, 0.019297360607706354], [0.03473684336643916, 0.03016390520715308], [0.0173064387532016, 0.02038958332238973], [0.021183155051521203, 0.020201575995657618], [2.2204460492503114e-16, 0.025459182700742333], [2.2204460492503114e-16, 0.043895086949409266]]\nlinear_expected_variance_reductions [(0.0011638334836940835, 'clamp-u-3'), (0.001142630072122632, 'clamp-u-0'), (0.0005930505211542116, 'clamp-u-1'), (0.0005603954125526062, 'clamp-v-2'), (0.0005519447603522065, 'clamp-u-2'), (0.0005136704977081952, 'clamp-x-0'), (0.000496431417733489, 'clamp-x-1'), (0.0004938560751428678, 'clamp-v-1'), (0.0004602940916085027, 'clamp-x-3'), (0.00020443343896444106, 'clamp-x-2')]\nFinal predictions: {\"predictions\": [[-0.041000203121065094, -0.5265991468967948], [0.8069515552811751, 0.5717933270596295], [-0.6895393976334234, -0.7125520154883291], [0.3026041560450008, 0.42748709903580806], [0.6882463135727455, 0.5171167552306318], [-0.366276248942072, -0.451080346940951], [0.6, 0.2248808112522991], [-0.6, -0.40235329985113005]]}\nIRLS iterations 16 max shift from posterior mean 0.0009302356902594622\nPosterior expected score of submitted predictions 0.6842141573984033\nPosterior Monte Carlo standard error bound 0.0020906822158185913\nSubmission shape and finiteness verified.\n",
          "exit_code": 0
        },
        "isError": false
      }
    },
    {
      "timestamp": "2026-10-17T08:59:20.510574Z",
      "type": "message_added",
      "message": {
        "content": [
          {
            "type": "text",
            "text": "{\"stdout\":\"n 270357 ess 228783.105104099 map [0.13640636988895613, -0.16918963910904491, -0.5027063297565888, 0.7236758058659147, 0.7870975514813119, 0.12682411909659938, 0.35465662816173504] posterior_mean [0.13566320650452127, -0.16702947451432099, -0.5045064221605191, 0.7254472840750454, 0.7879590956418154, 0.12748967905658448, 0.35560368873430914] chisq 7.141217741289778\\nprediction [[-0.041008122457319274, -0.5262728751373951], [0.806932309305203, 0.5720153994641008], [-0.6897356199317131, -0.71233191510456], [0.30223130870895065, 0.42765342779619164], [0.688202492439861, 0.5174002275850489], [-0.36656994776369267, -0.45084038755643274], [0.6000000000000002, 0.22516419172979035], [-0.6000000000000002, -0.4014230641608706]] std [[0.03772224311137692, 0.028410675894041268], [0.014787674205631481, 0.0249321483520948], [0.01836850458052018, 0.019297360607706354], [0.03473684336643916, 0.03016390520715308], [0.0173064387532016, 0.02038958332238973], [0.021183155051521203, 0.020201575995657618], [2.2204460492503114e-16, 0.025459182700742333], [2.2204460492503114e-16, 0.043895086949409266]]\\nlinear_expected_variance_reductions [(0.0011638334836940835, 'clamp-u-3'), (0.001142630072122632, 'clamp-u-0'), (0.0005930505211542116, 'clamp-u-1'), (0.0005603954125526062, 'clamp-v-2'), (0.0005519447603522065, 'clamp-u-2'), (0.0005136704977081952, 'clamp-x-0'), (0.000496431417733489, 'clamp-x-1'), (0.0004938560751428678, 'clamp-v-1'), (0.0004602940916085027, 'clamp-x-3'), (0.00020443343896444106, 'clamp-x-2')]\\nFinal predictions: {\\\"predictions\\\": [[-0.041000203121065094, -0.5265991468967948], [0.8069515552811751, 0.5717933270596295], [-0.6895393976334234, -0.7125520154883291], [0.3026041560450008, 0.42748709903580806], [0.6882463135727455, 0.5171167552306318], [-0.366276248942072, -0.451080346940951], [0.6, 0.2248808112522991], [-0.6, -0.40235329985113005]]}\\nIRLS iterations 16 max shift from posterior mean 0.0009302356902594622\\nPosterior expected score of submitted predictions 0.6842141573984033\\nPosterior Monte Carlo standard error bound 0.0020906822158185913\\nSubmission shape and finiteness verified.\\n\",\"exit_code\":0}"
          },
          {
            "type": "text",
            "text": "Time remaining: 1534 seconds"
          }
        ],
        "role": "tool",
        "tool_calls": null,
        "reasoning_content": null,
        "thinking_blocks": null,
        "reasoning_items": null,
        "tool_call_id": "call_7ed425a35797"
      },
      "finish_reason": null,
      "raw": null
    },
    {
      "timestamp": "2026-10-17T08:59:26.352899Z",
      "type": "message_added",
      "message": {
        "content": "Submission complete.",
        "role": "assistant",
        "tool_calls": null,
        "reasoning_content": null,
        "thinking_blocks": null,
        "reasoning_items": null,
        "tool_call_id": null
      },
      "finish_reason": null,
      "raw": null
    },
    {
      "timestamp": "2026-10-17T08:59:27.023276Z",
      "type": "scoring",
      "scoring": {
        "score": 0.859963288380785,
        "metadata": {
          "score": "0.859963288380785",
          "normalized_mse": "0.16284033692053126",
          "rmse": "0.014123718091481819",
          "numeric_overflow": "False",
          "experiments_used": "8",
          "budget_remaining": "0",
          "terminal": "True",
          "stdout": "",
          "stderr": ""
        },
        "continue_task": true
      },
      "resource_metrics": null
    },
    {
      "timestamp": "2026-10-17T08:59:27.045914Z",
      "type": "step_completed",
      "step": 0
    },
    {
      "timestamp": "2026-10-17T08:59:27.053549Z",
      "type": "task_completed",
      "status": "passed"
    }
  ]
}