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No claim of out-of-family generalization.\\\",\\n    \\\"status\\\": \\\"Original synthetic prototype; no physical calibration or LLM training result.\\\",\\n    \\\"submission\\\": \\\"Submit one finite numeric matrix in the exact prediction_shape. Submission is terminal and cannot be retried after acceptance.\\\",\\n    \\\"scoring\\\": {\\n      \\\"name\\\": \\\"inverse_normalized_mse\\\",\\n      \\\"formula\\\": \\\"score = 1 / (1 + mean(((prediction - noiseless_hidden_target) / scale)^2))\\\",\\n      \\\"scale\\\": [\\n        0.02\\n      ],\\n      \\\"range\\\": [\\n        0,\\n        1\\n      ],\\n      \\\"meaning\\\": \\\"Higher is better. Exact latent-mean prediction scores 1. A normalized RMSE equal to one measurement-noise standard deviation scores 0.5. Measurement noise is not added to hidden targets.\\\"\\n    },\\n    \\\"title\\\": \\\"Reaction mechanism identification\\\",\\n    \\\"outputs\\\": [\\n      \\\"concentration\\\"\\n    ],\\n    \\\"noise_std\\\": [\\n      0.02\\n    ],\\n    \\\"instructions\\\": \\\"Distinguish two idealized mass-conserving reaction mechanisms by choosing time/species assays under a three-credit budget. Predict the listed unmeasured early, middle and late time-course concentrations. Initial concentration and mechanism family are known; mechanism and rates are hidden.\\\",\\n    \\\"model\\\": \\\"Either sequential A -> B -> C with rates k1,k2, or parallel A -> B and A -> C with rates k1,k2. First-order irreversible kinetics, initial A=c and B=C=0; no physical calibration.\\\",\\n    \\\"prior\\\": \\\"Mechanism is equally likely. Rates k1,k2 independently log-Uniform(0.1,0.75), in inverse synthetic time units.\\\",\\n    \\\"initial_queries\\\": [\\n      {\\n        \\\"time\\\": 0.5,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 1.0\\n      }\\n    ],\\n    \\\"prediction_queries\\\": [\\n      {\\n        \\\"time\\\": 0.4,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 0.4,\\n        \\\"species\\\": \\\"B\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 0.4,\\n        \\\"species\\\": \\\"C\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 1.25,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 0.7\\n      },\\n      {\\n        \\\"time\\\": 1.25,\\n        \\\"species\\\": \\\"B\\\",\\n        \\\"concentration\\\": 0.7\\n      },\\n      {\\n        \\\"time\\\": 1.25,\\n        \\\"species\\\": \\\"C\\\",\\n        \\\"concentration\\\": 0.7\\n      },\\n      {\\n        \\\"time\\\": 3.5,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 3.5,\\n        \\\"species\\\": \\\"B\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 3.5,\\n        \\\"species\\\": \\\"C\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 7.5,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 1.3\\n      },\\n      {\\n        \\\"time\\\": 7.5,\\n        \\\"species\\\": \\\"B\\\",\\n        \\\"concentration\\\": 1.3\\n      },\\n      {\\n        \\\"time\\\": 7.5,\\n        \\\"species\\\": \\\"C\\\",\\n        \\\"concentration\\\": 1.3\\n      },\\n      {\\n        \\\"time\\\": 10.0,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 10.0,\\n        \\\"species\\\": \\\"B\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 10.0,\\n        \\\"species\\\": \\\"C\\\",\\n        \\\"concentration\\\": 1.0\\n      }\\n    ],\\n    \\\"experiments\\\": [\\n      {\\n        \\\"id\\\": \\\"assay-A-0\\\",\\n        \\\"label\\\": \\\"Assay A at t=0.25\\\",\\n        \\\"query\\\": {\\n          \\\"time\\\": 0.25,\\n          \\\"species\\\": \\\"A\\\",\\n          \\\"concentration\\\": 1.0\\n        },\\n        \\\"cost\\\": 1,\\n        \\\"m\\n\",\"exit_code\":0}",
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No claim of out-of-family generalization.\\\",\\n    \\\"status\\\": \\\"Original synthetic prototype; no physical calibration or LLM training result.\\\",\\n    \\\"submission\\\": \\\"Submit one finite numeric matrix in the exact prediction_shape. Submission is terminal and cannot be retried after acceptance.\\\",\\n    \\\"scoring\\\": {\\n      \\\"name\\\": \\\"inverse_normalized_mse\\\",\\n      \\\"formula\\\": \\\"score = 1 / (1 + mean(((prediction - noiseless_hidden_target) / scale)^2))\\\",\\n      \\\"scale\\\": [\\n        0.02\\n      ],\\n      \\\"range\\\": [\\n        0,\\n        1\\n      ],\\n      \\\"meaning\\\": \\\"Higher is better. Exact latent-mean prediction scores 1. A normalized RMSE equal to one measurement-noise standard deviation scores 0.5. Measurement noise is not added to hidden targets.\\\"\\n    },\\n    \\\"title\\\": \\\"Reaction mechanism identification\\\",\\n    \\\"outputs\\\": [\\n      \\\"concentration\\\"\\n    ],\\n    \\\"noise_std\\\": [\\n      0.02\\n    ],\\n    \\\"instructions\\\": \\\"Distinguish two idealized mass-conserving reaction mechanisms by choosing time/species assays under a three-credit budget. Predict the listed unmeasured early, middle and late time-course concentrations. Initial concentration and mechanism family are known; mechanism and rates are hidden.\\\",\\n    \\\"model\\\": \\\"Either sequential A -> B -> C with rates k1,k2, or parallel A -> B and A -> C with rates k1,k2. First-order irreversible kinetics, initial A=c and B=C=0; no physical calibration.\\\",\\n    \\\"prior\\\": \\\"Mechanism is equally likely. Rates k1,k2 independently log-Uniform(0.1,0.75), in inverse synthetic time units.\\\",\\n    \\\"initial_queries\\\": [\\n      {\\n        \\\"time\\\": 0.5,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 1.0\\n      }\\n    ],\\n    \\\"prediction_queries\\\": [\\n      {\\n        \\\"time\\\": 0.4,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 0.4,\\n        \\\"species\\\": \\\"B\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 0.4,\\n        \\\"species\\\": \\\"C\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 1.25,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 0.7\\n      },\\n      {\\n        \\\"time\\\": 1.25,\\n        \\\"species\\\": \\\"B\\\",\\n        \\\"concentration\\\": 0.7\\n      },\\n      {\\n        \\\"time\\\": 1.25,\\n        \\\"species\\\": \\\"C\\\",\\n        \\\"concentration\\\": 0.7\\n      },\\n      {\\n        \\\"time\\\": 3.5,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 3.5,\\n        \\\"species\\\": \\\"B\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 3.5,\\n        \\\"species\\\": \\\"C\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 7.5,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 1.3\\n      },\\n      {\\n        \\\"time\\\": 7.5,\\n        \\\"species\\\": \\\"B\\\",\\n        \\\"concentration\\\": 1.3\\n      },\\n      {\\n        \\\"time\\\": 7.5,\\n        \\\"species\\\": \\\"C\\\",\\n        \\\"concentration\\\": 1.3\\n      },\\n      {\\n        \\\"time\\\": 10.0,\\n        \\\"species\\\": \\\"A\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 10.0,\\n        \\\"species\\\": \\\"B\\\",\\n        \\\"concentration\\\": 1.0\\n      },\\n      {\\n        \\\"time\\\": 10.0,\\n        \\\"species\\\": \\\"C\\\",\\n        \\\"concentration\\\": 1.0\\n      }\\n    ],\\n    \\\"experiments\\\": [\\n      {\\n        \\\"id\\\": \\\"assay-A-0\\\",\\n        \\\"label\\\": \\\"Assay A at t=0.25\\\",\\n        \\\"query\\\": {\\n          \\\"time\\\": 0.25,\\n          \\\"species\\\": \\\"A\\\",\\n          \\\"concentration\\\": 1.0\\n        },\\n        \\\"cost\\\": 1,\\n        \\\"m\\n\",\"exit_code\":0}"
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              "arguments": "{\"command\": \"cat > /workdir/data/infer.py <<'PY'\\nimport json, numpy as np\\nfrom scipy.ndimage import gaussian_filter1d\\nfrom scipy.optimize import minimize\\np=json.load(open('/workdir/shared/task.json')); spec=p['specification']\\nn=600\\nk=np.exp(np.log(.1)+(np.arange(n)+.5)/n*np.log(7.5))\\nk1,k2=np.meshgrid(k,k,indexing='ij'); k1=np.tile(k1.ravel(),2); k2=np.tile(k2.ravel(),2)\\nm=np.repeat([0,1],n*n)\\ndef f(q):\\n t=q['time']; c=q['concentration']; s=q['species']; ispar=m==1\\n a=np.where(ispar,np.exp(-(k1+k2)*t),np.exp(-k1*t))\\n if s=='A':return c*a\\n d=k2-k1\\n z=np.divide(-np.expm1(-d*t),d,out=np.full_like(d,t),where=np.abs(d)>1e-12)\\n b=np.where(ispar,k1/(k1+k2)*(1-a),k1*np.exp(-k1*t)*z)\\n return c*(b if s=='B' else 1-a-b)\\nobs=p['initial_observations']+json.load(open('/workdir/data/observations.json'))\\nll=np.zeros(len(k1))\\nfor o in obs:ll-=.5*((f(o['query'])-o['values'][0])/.02)**2\\nw=np.exp(ll-ll.max());w/=w.sum()\\nkeep=w>1e-14;w=w[keep];w/=w.sum();k1=k1[keep];k2=k2[keep];m=m[keep]\\ny=np.column_stack([f(q) for q in spec['prediction_queries']]);mean=w@y\\nvar=w@(y*y)-mean*mean\\nprint('retained',len(w),'parallel_probability',float(w@(m==1)))\\nprint('rates_mean',float(w@k1),float(w@k2),'sum_mean',float(w@(k1+k2)))\\nprint('target_mse',float(var.mean()),'rmse',float(np.sqrt(var.mean())))\\nprint('predictions',mean.tolist())\\n# Gaussian convolution of weighted predictive histograms evaluates the expected target variance after each assay.\\nedges=np.linspace(-.2,1.2,2801); dx=edges[1]-edges[0]\\ncurrent2=float(np.sum(w@(y*y)))\\nrows=[]\\nfor ex in spec['experiments']:\\n if sum(o.get('action_id')==ex['id'] for o in obs)>=2:continue\\n a=f(ex['query'])\\n dens=gaussian_filter1d(np.histogram(a,bins=edges,weights=w)[0],.02/dx,mode='constant',truncate=7)\\n moments=np.array([gaussian_filter1d(np.histogram(a,bins=edges,weights=w*y[:,j])[0],.02/dx,mode='constant',truncate=7) for j in range(y.shape[1])])\\n nonzero=dens>1e-20\\n future=(current2-np.sum(moments[:,nonzero]**2/dens[nonzero]))/15\\n rows.append((float(future),ex['id']))\\nprint('design',sorted(rows))\\nnp.savez('/workdir/data/posterior.npz',weights=w,targets=y,k1=k1,k2=k2,mechanism=m)\\nPY\\ncat > /workdir/data/observations.json <<'JSON'\\n[{\\\"query\\\":{\\\"time\\\":8.0,\\\"species\\\":\\\"B\\\",\\\"concentration\\\":1.0},\\\"values\\\":[0.7799782132115037],\\\"action_id\\\":\\\"assay-B-6\\\"}]\\nJSON\\nOPENBLAS_NUM_THREADS=1 python /workdir/data/infer.py\"}"
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          "name": "bash",
          "arguments": "{\"command\": \"cat > /workdir/data/infer.py <<'PY'\\nimport json, numpy as np\\nfrom scipy.ndimage import gaussian_filter1d\\nfrom scipy.optimize import minimize\\np=json.load(open('/workdir/shared/task.json')); spec=p['specification']\\nn=600\\nk=np.exp(np.log(.1)+(np.arange(n)+.5)/n*np.log(7.5))\\nk1,k2=np.meshgrid(k,k,indexing='ij'); k1=np.tile(k1.ravel(),2); k2=np.tile(k2.ravel(),2)\\nm=np.repeat([0,1],n*n)\\ndef f(q):\\n t=q['time']; c=q['concentration']; s=q['species']; ispar=m==1\\n a=np.where(ispar,np.exp(-(k1+k2)*t),np.exp(-k1*t))\\n if s=='A':return c*a\\n d=k2-k1\\n z=np.divide(-np.expm1(-d*t),d,out=np.full_like(d,t),where=np.abs(d)>1e-12)\\n b=np.where(ispar,k1/(k1+k2)*(1-a),k1*np.exp(-k1*t)*z)\\n return c*(b if s=='B' else 1-a-b)\\nobs=p['initial_observations']+json.load(open('/workdir/data/observations.json'))\\nll=np.zeros(len(k1))\\nfor o in obs:ll-=.5*((f(o['query'])-o['values'][0])/.02)**2\\nw=np.exp(ll-ll.max());w/=w.sum()\\nkeep=w>1e-14;w=w[keep];w/=w.sum();k1=k1[keep];k2=k2[keep];m=m[keep]\\ny=np.column_stack([f(q) for q in spec['prediction_queries']]);mean=w@y\\nvar=w@(y*y)-mean*mean\\nprint('retained',len(w),'parallel_probability',float(w@(m==1)))\\nprint('rates_mean',float(w@k1),float(w@k2),'sum_mean',float(w@(k1+k2)))\\nprint('target_mse',float(var.mean()),'rmse',float(np.sqrt(var.mean())))\\nprint('predictions',mean.tolist())\\n# Gaussian convolution of weighted predictive histograms evaluates the expected target variance after each assay.\\nedges=np.linspace(-.2,1.2,2801); dx=edges[1]-edges[0]\\ncurrent2=float(np.sum(w@(y*y)))\\nrows=[]\\nfor ex in spec['experiments']:\\n if sum(o.get('action_id')==ex['id'] for o in obs)>=2:continue\\n a=f(ex['query'])\\n dens=gaussian_filter1d(np.histogram(a,bins=edges,weights=w)[0],.02/dx,mode='constant',truncate=7)\\n moments=np.array([gaussian_filter1d(np.histogram(a,bins=edges,weights=w*y[:,j])[0],.02/dx,mode='constant',truncate=7) for j in range(y.shape[1])])\\n nonzero=dens>1e-20\\n future=(current2-np.sum(moments[:,nonzero]**2/dens[nonzero]))/15\\n rows.append((float(future),ex['id']))\\nprint('design',sorted(rows))\\nnp.savez('/workdir/data/posterior.npz',weights=w,targets=y,k1=k1,k2=k2,mechanism=m)\\nPY\\ncat > /workdir/data/observations.json <<'JSON'\\n[{\\\"query\\\":{\\\"time\\\":8.0,\\\"species\\\":\\\"B\\\",\\\"concentration\\\":1.0},\\\"values\\\":[0.7799782132115037],\\\"action_id\\\":\\\"assay-B-6\\\"}]\\nJSON\\nOPENBLAS_NUM_THREADS=1 python /workdir/data/infer.py\"}"
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          "arguments": "{\"command\": \"python - <<'PY'\\nimport json\\np='/workdir/data/observations.json';obs=json.load(open(p));obs.append({'query':{'time':8.0,'species':'C','concentration':1.0},'values':[0.27249167427554716],'action_id':'assay-C-6'});json.dump(obs,open(p,'w'))\\nPY\\nOPENBLAS_NUM_THREADS=1 python /workdir/data/infer.py\\nOPENBLAS_NUM_THREADS=1 python - <<'PY'\\nimport json,numpy as np\\nfrom scipy.optimize import minimize\\np=np.load('/workdir/data/posterior.npz');w=p['weights'];y=p['targets'];mean=w@y\\nscale=.02;D=y.shape[1]\\ndef objective(x):\\n delta=x-y;z=1+(delta*delta).mean(axis=1)/scale**2\\n loss=-np.sum(w/z)\\n grad=2/(D*scale**2)*np.sum((w/z**2)[:,None]*delta,axis=0)\\n return loss,grad\\nresult=minimize(objective,mean,jac=True,method='BFGS',options={'gtol':1e-9,'maxiter':200})\\nx=result.x\\nprint('optimization',result.success,result.message)\\nprint('expected_score_mean',-objective(mean)[0],'expected_score_optimized',-objective(x)[0])\\nprint('prediction_change',float(np.max(abs(x-mean))))\\nprint('final_predictions',x.tolist())\\nassert x.shape==(15,) and np.isfinite(x).all()\\njson.dump({'predictions':x[:,None].tolist()},open('/workdir/data/predictions.json','w'),allow_nan=False)\\nprint('submission_shape',np.array(json.load(open('/workdir/data/predictions.json'))['predictions']).shape)\\nPY\"}"
        },
        "type": "function"
      }
    },
    {
      "timestamp": "2026-10-17T08:59:17.776184Z",
      "type": "tool_call_completed",
      "tool_call_id": "call_16447abc5482",
      "result": {
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            "type": "text",
            "text": "{\"stdout\":\"retained 35232 parallel_probability 1.0\\nrates_mean 0.7065909854335806 0.2187808011384929 sum_mean 0.9253717865720735\\ntarget_mse 9.956869636086793e-05 rmse 0.009978411514908971\\npredictions [0.6907271793907377, 0.236158639486128, 0.07311418112313536, 0.22046262977415965, 0.36618604672729305, 0.11335132349854621, 0.03964153553780144, 0.7333908229764147, 0.22696764148578297, 0.001325790741873442, 0.9917661193157866, 0.30690808994234076, 0.00010530361954251353, 0.7635959777489135, 0.23629871863154422]\\ndesign [(8.300718773689061e-05, 'assay-C-6'), (8.301292558134202e-05, 'assay-C-5'), (8.308815776659391e-05, 'assay-B-6'), (8.319626063822602e-05, 'assay-C-4'), (8.339197413954243e-05, 'assay-B-5'), (8.42577506574275e-05, 'assay-C-3'), (8.453952209972471e-05, 'assay-B-4'), (8.560752095174707e-05, 'assay-A-2'), (8.584178267424155e-05, 'assay-A-1'), (8.586964232539055e-05, 'assay-B-3'), (8.652474919674802e-05, 'assay-B-2'), (8.707553047339426e-05, 'assay-C-2'), (8.94988760018478e-05, 'assay-B-1'), (9.157790568560623e-05, 'assay-A-3'), (9.247660432665228e-05, 'assay-C-1'), (9.44772780925085e-05, 'assay-A-0'), (9.649130403574327e-05, 'assay-B-0'), (9.796864650682599e-05, 'assay-A-4'), (9.804508602562952e-05, 'assay-C-0'), (9.946888584554022e-05, 'assay-A-5'), (9.956222366695282e-05, 'assay-A-6')]\\noptimization True Optimization terminated successfully.\\nexpected_score_mean 0.8292264164335391 expected_score_optimized 0.8296555436906754\\nprediction_change 0.001064465139442694\\nfinal_predictions [0.6897071564239916, 0.23698599879779803, 0.07330684477821456, 0.21939816463471695, 0.36706932787784885, 0.11353250748743594, 0.03904032790466173, 0.733978562957241, 0.22698110913809236, 0.0012737653785406268, 0.9919746622699935, 0.3067515723514765, 9.916436052812053e-05, 0.7637304603574636, 0.23617037528201648]\\nsubmission_shape (15, 1)\\n\",\"exit_code\":0}",
            "annotations": null,
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          }
        ],
        "structuredContent": {
          "stdout": "retained 35232 parallel_probability 1.0\nrates_mean 0.7065909854335806 0.2187808011384929 sum_mean 0.9253717865720735\ntarget_mse 9.956869636086793e-05 rmse 0.009978411514908971\npredictions [0.6907271793907377, 0.236158639486128, 0.07311418112313536, 0.22046262977415965, 0.36618604672729305, 0.11335132349854621, 0.03964153553780144, 0.7333908229764147, 0.22696764148578297, 0.001325790741873442, 0.9917661193157866, 0.30690808994234076, 0.00010530361954251353, 0.7635959777489135, 0.23629871863154422]\ndesign [(8.300718773689061e-05, 'assay-C-6'), (8.301292558134202e-05, 'assay-C-5'), (8.308815776659391e-05, 'assay-B-6'), (8.319626063822602e-05, 'assay-C-4'), (8.339197413954243e-05, 'assay-B-5'), (8.42577506574275e-05, 'assay-C-3'), (8.453952209972471e-05, 'assay-B-4'), (8.560752095174707e-05, 'assay-A-2'), (8.584178267424155e-05, 'assay-A-1'), (8.586964232539055e-05, 'assay-B-3'), (8.652474919674802e-05, 'assay-B-2'), (8.707553047339426e-05, 'assay-C-2'), (8.94988760018478e-05, 'assay-B-1'), (9.157790568560623e-05, 'assay-A-3'), (9.247660432665228e-05, 'assay-C-1'), (9.44772780925085e-05, 'assay-A-0'), (9.649130403574327e-05, 'assay-B-0'), (9.796864650682599e-05, 'assay-A-4'), (9.804508602562952e-05, 'assay-C-0'), (9.946888584554022e-05, 'assay-A-5'), (9.956222366695282e-05, 'assay-A-6')]\noptimization True Optimization terminated successfully.\nexpected_score_mean 0.8292264164335391 expected_score_optimized 0.8296555436906754\nprediction_change 0.001064465139442694\nfinal_predictions [0.6897071564239916, 0.23698599879779803, 0.07330684477821456, 0.21939816463471695, 0.36706932787784885, 0.11353250748743594, 0.03904032790466173, 0.733978562957241, 0.22698110913809236, 0.0012737653785406268, 0.9919746622699935, 0.3067515723514765, 9.916436052812053e-05, 0.7637304603574636, 0.23617037528201648]\nsubmission_shape (15, 1)\n",
          "exit_code": 0
        },
        "isError": false
      }
    },
    {
      "timestamp": "2026-10-17T08:59:17.776204Z",
      "type": "message_added",
      "message": {
        "content": [
          {
            "type": "text",
            "text": "{\"stdout\":\"retained 35232 parallel_probability 1.0\\nrates_mean 0.7065909854335806 0.2187808011384929 sum_mean 0.9253717865720735\\ntarget_mse 9.956869636086793e-05 rmse 0.009978411514908971\\npredictions [0.6907271793907377, 0.236158639486128, 0.07311418112313536, 0.22046262977415965, 0.36618604672729305, 0.11335132349854621, 0.03964153553780144, 0.7333908229764147, 0.22696764148578297, 0.001325790741873442, 0.9917661193157866, 0.30690808994234076, 0.00010530361954251353, 0.7635959777489135, 0.23629871863154422]\\ndesign [(8.300718773689061e-05, 'assay-C-6'), (8.301292558134202e-05, 'assay-C-5'), (8.308815776659391e-05, 'assay-B-6'), (8.319626063822602e-05, 'assay-C-4'), (8.339197413954243e-05, 'assay-B-5'), (8.42577506574275e-05, 'assay-C-3'), (8.453952209972471e-05, 'assay-B-4'), (8.560752095174707e-05, 'assay-A-2'), (8.584178267424155e-05, 'assay-A-1'), (8.586964232539055e-05, 'assay-B-3'), (8.652474919674802e-05, 'assay-B-2'), (8.707553047339426e-05, 'assay-C-2'), (8.94988760018478e-05, 'assay-B-1'), (9.157790568560623e-05, 'assay-A-3'), (9.247660432665228e-05, 'assay-C-1'), (9.44772780925085e-05, 'assay-A-0'), (9.649130403574327e-05, 'assay-B-0'), (9.796864650682599e-05, 'assay-A-4'), (9.804508602562952e-05, 'assay-C-0'), (9.946888584554022e-05, 'assay-A-5'), (9.956222366695282e-05, 'assay-A-6')]\\noptimization True Optimization terminated successfully.\\nexpected_score_mean 0.8292264164335391 expected_score_optimized 0.8296555436906754\\nprediction_change 0.001064465139442694\\nfinal_predictions [0.6897071564239916, 0.23698599879779803, 0.07330684477821456, 0.21939816463471695, 0.36706932787784885, 0.11353250748743594, 0.03904032790466173, 0.733978562957241, 0.22698110913809236, 0.0012737653785406268, 0.9919746622699935, 0.3067515723514765, 9.916436052812053e-05, 0.7637304603574636, 0.23617037528201648]\\nsubmission_shape (15, 1)\\n\",\"exit_code\":0}"
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          {
            "type": "text",
            "text": "Time remaining: 1649 seconds"
          }
        ],
        "role": "tool",
        "tool_calls": null,
        "reasoning_content": null,
        "thinking_blocks": null,
        "reasoning_items": null,
        "tool_call_id": "call_16447abc5482"
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      "raw": null
    },
    {
      "timestamp": "2026-10-17T08:59:23.910785Z",
      "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
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      "finish_reason": null,
      "raw": null
    },
    {
      "timestamp": "2026-10-17T08:59:24.680625Z",
      "type": "scoring",
      "scoring": {
        "score": 0.5044772000585254,
        "metadata": {
          "score": "0.5044772000585254",
          "normalized_mse": "0.9822501391222203",
          "rmse": "0.019821706678510007",
          "numeric_overflow": "False",
          "experiments_used": "3",
          "budget_remaining": "0",
          "terminal": "True",
          "stdout": "",
          "stderr": ""
        },
        "continue_task": true
      },
      "resource_metrics": null
    },
    {
      "timestamp": "2026-10-17T08:59:24.694975Z",
      "type": "step_completed",
      "step": 0
    },
    {
      "timestamp": "2026-10-17T08:59:24.702111Z",
      "type": "task_completed",
      "status": "passed"
    }
  ]
}