gpt-5.6-sol

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/claim.md\\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+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 reproducible final selection to 324.7.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));"
}

{
  "input": "const 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);"
}