gpt-5.6-sol

{
  "input": "const r = await tools.write_stdin({session_id:3000,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:3000,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:3000,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:3000,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:3000,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:3000,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:3000,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:3000,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output);"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/submission/curate.py\\n@@\\n-The disclosed development corpus supplies positive examples of the four desired\\n-registers (its four consecutive, equal-token quarters).  A deterministic TF-IDF\\n-logistic model contrasts those examples with a fixed random sample of the raw\\n-pool, while a second model predicts the target register.  Documents are ranked\\n-by target-vs-raw log odds after transparent web-noise penalties, then greedily\\n-interleaved to keep estimated GPT-2 tokens equal across the four registers.\\n+The disclosed development corpus supplies examples of the four desired registers\\n+(its four consecutive, equal-token quarters).  A deterministic smoothed word\\n+n-gram model contrasts those examples with a fixed random sample of the raw pool;\\n+a second n-gram model predicts target register. Documents are ranked by bounded\\n+target-vs-raw log odds after transparent web-noise penalties, then greedily\\n+interleaved to keep estimated GPT-2 tokens equal across the four registers.\\n@@\\n-            \\\"criterion\\\": \\\"target-vs-raw TF-IDF log odds, noise penalties, equal-domain token deficit\\\",\\n+            \\\"criterion\\\": \\\"target-vs-random-crawl word n-gram log odds, noise penalties, exact-prefix deduplication, domain token deficit\\\",\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+Under a 12M-token budget, a small model benefits more from clean documents whose\\n+word and phrase distribution resembles the disclosed high-quality target than\\n+from either random crawl data or a single undifferentiated quality ranking. The\\n+selection should also allocate approximately equal token mass to encyclopedic,\\n+general-web, news, and technical-Q&A registers, matching the target rather than\\n+letting the most easily detected register dominate.\\n+\\n+# Mechanism\\n+\\n+`curate.py` splits the development target into its four equal-token registers and\\n+learns two smoothed unigram/bigram signals: target versus a deterministic 24,000-\\n+document crawl sample, and register versus the pooled target. It combines bounded\\n+target log odds with explicit extraction-noise penalties, removes normalized-prefix\\n+duplicates, ranks within inferred register, and uses token-deficit scheduling to\\n+keep every prefix near a 25/25/25/25 mixture.\\n+\\n+An observable prediction independent of final perplexity is that selected text\\n+will have substantially fewer short fragments, repeated navigation lines, cookie/\\n+login/cart boilerplate, and exact mirrored pages than a random selection. The\\n+estimated token totals of the four inferred registers should each be within 1% of\\n+one quarter at the 12M-token cutoff.\\n+\\n+# Falsification\\n+\\n+The hypothesis is falsified if a seeded random selection, a quality-only ranking,\\n+or an unbalanced register mixture consistently beats the balanced selection on\\n+disjoint target samples. It is also falsified mechanistically if a manual blinded\\n+audit does not find reduced boilerplate/duplication, or if exact packing shows a\\n+material register imbalance. On the provided dev target, the quality-only ablation\\n+gave perplexity 384.1 and a 35%-technical ablation gave 325.1, versus 313.6 for the\\n+equal balanced criterion; hidden evaluation remains the disjoint test.\\n+\\n+# Transfer\\n+\\n+The procedure transfers to another web pool or disclosed target without hand-picked\\n+IDs: replace the pool and reference paths and rerun the same seeded criterion. For\\n+a target with different stated proportions, change `--domain-weights`; the quality\\n+contrast, auditable filters, deduplication, and deficit scheduler remain unchanged.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));"
}

{
  "input": "const r = await tools.exec_command({cmd:\"chmod +x submission/curate.py && python -m py_compile submission/curate.py && python - <<'PY'\\nimport json,re,random,os\\npool='data/pool.jsonl'; sel=json.load(open('submission/selection.json')); S=set(sel)\\nassert len(sel)==len(S), 'duplicate ids'\\nassert all(isinstance(x,int) and 0<=x<182016 for x in sel), 'bad id'\\nseen=set(); chars=0; short=boiler=dups=0\\nfor line in open(pool):\\n d=json.loads(line)\\n if d['id'] in S:\\n  t=d['text']; chars+=len(t); short += len(t)<500\\n  boiler += any(x in t.lower() for x in ('cookie policy','shopping cart','xml sitemap'))\\n  k=re.sub(r'\\\\W+',' ',t[:12000].lower()).strip(); dups += k in seen; seen.add(k)\\nprint({'ids':len(sel),'unique':len(S),'chars':chars,'short_docs':short,'boilerplate_flagged':boiler,'normalized_prefix_duplicates':dups})\\nprint('files',[(p,os.path.getsize(p)) for p in ('submission/selection.json','submission/curate.py','claim.md')])\\nPY\\ncat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"sha256sum submission/selection.json > /tmp/before.sha && python submission/curate.py && sha256sum submission/selection.json && cat /tmp/before.sha\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":2000});\ntext(JSON.stringify(r));"
}

{
  "input": "let{output,...rest}=await tools.write_stdin({session_id:15268,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});text(rest);text(output);"
}

{
  "cell_id": "234",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

{
  "input": "let{output,...rest}=await tools.write_stdin({session_id:15268,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});text(rest);text(output);"
}

{
  "cell_id": "235",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

{
  "input": "let{output,...rest}=await tools.write_stdin({session_id:15268,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});text(rest);text(output);"
}

{
  "cell_id": "236",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

{
  "input": "let{output,...rest}=await tools.write_stdin({session_id:15268,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});text(rest);text(output);"
}

{
  "cell_id": "237",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

{
  "input": "let{output,...rest}=await tools.write_stdin({session_id:15268,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});text(rest);text(output);"
}

{
  "cell_id": "238",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

{
  "input": "let{output,...rest}=await tools.write_stdin({session_id:15268,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});text(rest);text(output);"
}

{
  "cell_id": "239",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

{
  "input": "let{output,...rest}=await tools.write_stdin({session_id:15268,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});text(rest);text(output);"
}