gpt-5.6-sol

{
  "input": "const r = await tools.write_stdin({session_id:46306,chars:\"\",yield_time_ms\":30000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:46306,chars:\"\",yield_time_ms\":30000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:46306,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "cell_id": "169",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.write_stdin({session_id:46306,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "cell_id": "170",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.write_stdin({session_id:46306,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "cell_id": "171",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.write_stdin({session_id:46306,chars:\"\",yield_time_ms\":30000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:46306,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python /workspace/pack_selection.py /workspace/submission/selection.json /tmp/final.npy && sha256sum /tmp/win.npy /tmp/final.npy && python -m py_compile /workspace/submission/curate.py && python - <<'PY'\\nimport json, numpy as np\\np='/workspace/submission/selection.json'; x=json.load(open(p))\\nprint('ids',len(x),'unique',len(set(x)),'min',min(x),'max',max(x),'packed',len(np.load('/tmp/final.npy')))\\nassert len(x)==len(set(x))==182016 and min(x)==0 and max(x)==182015\\nassert len(np.load('/tmp/final.npy'))==12_000_000\\nPY\\ncat /workspace/.timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);"
}

{
  "cell_id": "174",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.exec_command({cmd:\"ls -lh /tmp/final.npy && sha256sum /tmp/win.npy /tmp/final.npy && python - <<'PY'\\nimport json,numpy as np\\nx=json.load(open('/workspace/submission/selection.json'))\\nprint(len(x),len(set(x)),min(x),max(x),len(np.load('/tmp/final.npy')))\\nPY\\ncat /workspace/.timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport numpy as np\\nx=np.load('/tmp/win.npy');y=np.load('/tmp/final.npy')\\nprint(np.array_equal(x,y),np.sum(x!=y))\\nz=np.flatnonzero(x!=y);print(z[:20]);\\nif len(z):\\n i=z[0];print(i,x[i-10:i+20],y[i-10:i+20])\\nPY\\npython - <<'PY'\\nimport json\\nx=json.load(open('/workspace/experiments/tech4.json'));y=json.load(open('/workspace/submission/selection.json'))\\nfor i,(a,b) in enumerate(zip(x,y)):\\n if a!=b: print('first id diff',i,a,b);break\\nprint('prefix equal',x[:20]==y[:20],x[:20],y[:20])\\nPY\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+For a 12M-token run, matching the target's mixture of registers is more valuable than selecting merely clean-looking prose. Documents should therefore be ranked by their word/unigram and bigram density advantage over raw web for each disclosed target quarter, after removing obvious crawl noise and duplicate content. A token-aware mixture should retain all four registers; the validated allocation is 2.5M encyclopedic, 2.5M high-quality web, 3M news, and 4M technical Q&A tokens.\\n+\\n+# Mechanism\\n+\\n+The density ratio selects text whose local vocabulary and constructions are supported by a high-quality target register but uncommon in an unfiltered pool sample. Hygiene gates prevent this statistic from being won by navigation, URLs, repeated templates, line noise, or extreme document shapes. Independent rankings prevent plentiful news-like prose from crowding out encyclopedia and technical material, while normalized-content hashing prevents duplicate pages from consuming the small budget.\\n+\\n+Before training, this predicts observables other than final perplexity: the consumed prefix will contain approximately the four token totals above; every selected content hash in that prefix will be unique; navigation terms, URLs, and repeated word trigrams will be markedly lower than in a random prefix; and each quarter's mean target-vs-raw density advantage will be positive and higher than random. In development, the domain-balanced density selection reached 328.7 PPL versus 476.7 for random and 476.9 for a prose-hygiene-only control. Reallocating tokens in response to quarter-wise losses improved the disclosed dev result to 324.1.\\n+\\n+# Falsification\\n+\\n+The hypothesis is falsified if a seed-matched random or hygiene-only prefix has equal-or-better register density and crawl-noise statistics, or if shuffling the density ranks within each accepted register does not worsen repeated-seed dev loss. It is also falsified mechanistically if removing deduplication or replacing the token quotas with an unconstrained global rank does not measurably increase duplicate/template incidence or collapse one of the four register shares. A hidden-target reversal despite stable proxy statistics would show that the disclosed density model overfit source/topic particulars rather than transferable register structure.\\n+\\n+# Transfer\\n+\\n+The method transfers to another disclosed target by replacing the four reference partitions and re-estimating density ratios and token quotas; the pool needs no labels. With no target sample, the same framework can use small trusted reference corpora for desired registers, retain the hygiene and deduplication stages, and set equal initial quotas. For larger budgets, density thresholds can be relaxed while keeping per-register coverage and duplicate controls; for smaller budgets, stricter support and shorter high-density documents should become more valuable.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));"
}

{
  "input": "const r = await tools.exec_command({cmd:\"bash /workspace/dev_eval.sh /workspace/submission/selection.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));"
}

{
  "input": "const r = await tools.write_stdin({session_id:88285,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});\ntext(r.output);"
}

{
  "cell_id": "179",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "const r = await tools.write_stdin({session_id:88285,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});\ntext(r.output);"
}

{
  "cell_id": "180",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "const r = await tools.write_stdin({session_id:88285,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});\ntext(r.output);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json\\nx=json.load(open('/workspace/experiments/tech4.json'));y=json.load(open('/workspace/submission/selection.json'))\\nn=0\\nfor i,(u,v) in enumerate(zip(x,y)):\\n if u!=v:\\n  print(i,u,v);n+=1\\n  if n>=100:break\\nprint('compared',len(x),len(y))\\nPY\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport random\\nrandom.seed(20260803);a=random.sample(range(182016),12000)\\nb=random.Random(20260803).sample(range(182016),12000)\\nprint(a==b,a[:5],b[:5])\\nPY\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/claim.md\\n@@\\n-In development, the domain-balanced density selection reached 328.7 PPL versus 476.7 for random and 476.9 for a prose-hygiene-only control. Reallocating tokens in response to quarter-wise losses improved the disclosed dev result to 324.1.\\n+In development, the domain-balanced density selection reached 328.7 PPL versus 476.7 for random and 476.9 for a prose-hygiene-only control. Reallocating tokens in response to quarter-wise losses improved the reproducible final selection to 324.7.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst p = await tools.update_plan({explanation:\"Final curator was reproduced from scratch, packed to exactly 12M tokens, and evaluated successfully.\",plan:[\n  {step:\"Inspect corpus, training pipeline, dev data, and time budget\",status:\"completed\"},\n  {step:\"Develop reproducible document quality/domain scoring\",status:\"completed\"},\n  {step:\"Evaluate candidate selections with frozen training runs\",status:\"completed\"},\n  {step:\"Generate and validate final deliverables\",status:\"completed\"}\n]});\nconst r = await tools.exec_command({cmd:\"ls -lh /workspace/submission/selection.json /workspace/submission/curate.py /workspace/claim.md && python -m py_compile /workspace/submission/curate.py && python - <<'PY'\\nimport json\\nx=json.load(open('/workspace/submission/selection.json'))\\nassert len(x)==182016 and len(set(x))==182016 and min(x)==0 and max(x)==182015\\nprint('selection validation: OK')\\nPY\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"rg -n \\\"development|324\\\" /workspace/claim.md && sed -n '1,120p' /workspace/claim.md\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r.output);"
}