gpt-5.6-sol

{
  "input": "const r = await tools.exec_command({\"cmd\":\"bash dev_eval.sh submission/selection.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":5000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n"
}

{
  "input": "let{output,...rest}=await tools.write_stdin({\"session_id\":2771,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output)\n"
}

{
  "cell_id": "75",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

{
  "input": "let{output,...rest}=await tools.write_stdin({\"session_id\":2771,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output)\n"
}

{
  "cell_id": "76",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

{
  "input": "let{output,...rest}=await tools.write_stdin({\"session_id\":2771,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output)\n"
}

{
  "cell_id": "77",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nimport random,json\\nx=list(range(182016));random.Random(1337).shuffle(x)\\njson.dump(x[:30000],open('/tmp/random.json','w'))\\nPY\\nbash dev_eval.sh /tmp/random.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":3000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n"
}

{
  "input": "let{output,...rest}=await tools.write_stdin({\"session_id\":80792,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":3000});text(rest);text(output)\n"
}

{
  "cell_id": "79",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "let{output,...rest}=await tools.write_stdin({\"session_id\":80792,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":3000});text(rest);text(output)\n"
}

{
  "cell_id": "80",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "let{output,...rest}=await tools.write_stdin({\"session_id\":80792,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":3000});text(rest);text(output)\n"
}

{
  "cell_id": "81",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/submission/curate.py\\n@@\\n     tok=GPT2TokenizerFast.from_pretrained('gpt2',local_files_only=True)\\n+    tok.model_max_length=10**9  # documents are encoded, never passed to GPT-2\\n@@\\n-            # GPT-2 English token count is close enough for balancing here; the\\n-            # published list deliberately has a 35% coverage margin.\\n+            # Exact GPT-2 count is used for prefix balancing; the published list\\n+            # deliberately has a 35% coverage margin.\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+At a severe token bottleneck, a token-balanced mixture of clean, substantive English documents representing encyclopedia-like exposition, high-quality general prose, reporting, and technical explanation will train a better broad language model than random web data. Hard rejection of corruption, non-English text, tiny pages, giant threads, and templated boilerplate should matter more than maximizing raw document count.\\n+\\n+# Mechanism\\n+\\n+The filter raises the fraction of sentence-bearing English prose and lowers navigation, repeated-line, URL/policy, mojibake, and first-person-chatter rates. Positive lexical/structural evidence assigns eligible documents to four disclosed registers, and weighted round-robin ordering keeps each near 25% of the consumed token prefix. An observable prediction independent of final perplexity is that the selected 12M-token prefix has fewer repeated/short lines and corrupted characters than a random prefix, while no register differs from 25% token share by more than one boundary document. In the provided diagnostic, the resulting dev perplexity was 446.27 versus 476.71 for a seed-1337 random control under the same frozen training recipe.\\n+\\n+# Falsification\\n+\\n+Reject the hypothesis if (a) a random 12M-token sample does not have worse held-out loss across repeated training seeds, (b) ablating the cleanliness gates and balance constraint does not worsen loss, or (c) the predicted corpus statistics above fail on the actually consumed prefix. A particularly useful test is four-way evaluation: improvement confined to one target quarter while the others regress would falsify the broad-mixture mechanism even if aggregate noise produces a small win.\\n+\\n+# Transfer\\n+\\n+The criterion uses only document text, aggregate frequencies from the disclosed development distribution, and generic web-quality signals—never hand-picked ids—so it transfers to a newly crawled pool with the same target registers. For a different language or target mixture, replace the English function-word test and vocabulary reference, then set round-robin shares to the new evaluation mixture; the corruption, repetition, size-diversity, and prefix-balancing principles remain unchanged.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.exec_command({\"cmd\":\"chmod +x submission/curate.py && python -m py_compile submission/curate.py && python - <<'PY'\\nimport json\\np='submission/selection.json'; x=json.load(open(p))\\nassert isinstance(x,list) and x\\nassert len(x)==len(set(x))\\nassert all(type(i) is int and 0<=i<182016 for i in x)\\nprint('valid ids:',len(x),'unique:',len(set(x)),'range:',min(x),max(x))\\nPY\\nls -lh submission/selection.json submission/curate.py claim.md\\ncat /tmp/dev.json\\ncat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n"
}

{
  "input": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nimport json,collections\\nx=json.load(open('submission/selection.json'))\\nc=collections.Counter(min(9,i*10//182016) for i in x)\\nprint(c)\\nPY\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n"
}

{
  "input": "const r = await tools.exec_command({\"cmd\":\"cat .timer/remaining_secs && test -s submission/selection.json && test -s submission/curate.py && test -s claim.md && python - <<'PY'\\nimport json\\nx=json.load(open('/workspace/submission/selection.json'))\\nassert len(x)==len(set(x)) and all(type(i) is int and 0<=i<182016 for i in x)\\nprint(len(x), 'valid unique ids')\\nPY\\nls -lh /workspace/submission/selection.json /workspace/submission/curate.py /workspace/claim.md\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n"
}