gpt-5.6-sol

{
  "cell_id": "52",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.exec_command({cmd:\"ps -p $(pgrep -n -f '^python submission/curate.py$') -o etime,%cpu 2>/dev/null || true\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"pid=$(pgrep -n -f '^python submission/curate.py$'); while kill -0 \\\"$pid\\\" 2>/dev/null; do sleep 15; done; stat -c '%y %s' submission/selection.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n"
}

{
  "cell_id": "54",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:34550,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n"
}

{
  "cell_id": "55",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.exec_command({cmd:\"python pack_selection.py submission/selection.json train2.npy\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:45752,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n"
}

{
  "cell_id": "57",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.exec_command({cmd:\"pid=$(pgrep -n -f '^python pack_selection.py'); while kill -0 $pid 2>/dev/null; do sleep 5; done; python train_nano.py --train_npy train2.npy --heldout_npy data/multi_dev.npy --out_json result2.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:45347,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n"
}

{
  "cell_id": "59",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.exec_command({cmd:\"pgrep -af 'train_nano.py --train_npy train2' || cat result2.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"while kill -0 2332 2>/dev/null; do sleep 10; done; cat result2.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+At a 12M-token budget, a small model benefits more from clean documents whose\\n+local n-gram distribution resembles the four evaluation registers than from a\\n+random sample of the much larger crawl. Equal-mass domain balancing should\\n+avoid improving news or code at the expense of encyclopedic and general prose.\\n+\\n+# Mechanism\\n+\\n+The curator learns four smoothed unigram/bigram log-odds profiles from equal\\n+quarters of the disclosed dev corpus versus random raw-web negatives and the\\n+other three quarters. It assigns each document to its strongest profile, adds\\n+language/structure hygiene rewards, penalizes boilerplate and corruption,\\n+removes duplicate crawl copies, and fair-merges ranked domain queues by text\\n+mass. Independently of final perplexity, the selected prefix should therefore\\n+have (a) close to 25% character mass per assigned domain, (b) fewer navigation,\\n+commerce, replacement-character, and duplicate spans than a random prefix, and\\n+(c) higher held-out target-vs-raw n-gram log odds.\\n+\\n+# Falsification\\n+\\n+Reject the hypothesis if a length-matched random sample has comparable hygiene,\\n+duplicate rate, domain balance, and target log-odds, or if ablations that shuffle\\n+rank within each domain do not worsen dev cross-entropy across multiple seeds.\\n+A per-quarter evaluation would further falsify the balancing mechanism if one\\n+quarter improves only by causing a large regression in another.\\n+\\n+# Transfer\\n+\\n+The approach transfers to another corpus or target by replacing the reference\\n+quarters and retaining the same reproducible background sampling, log-odds,\\n+hygiene, deduplication, and mass-balanced merge. With no target sample, reference\\n+profiles can instead come from small trusted corpora for the desired registers;\\n+the scoring and selection procedure is otherwise unchanged.\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json,numpy as np,os\\ns=json.load(open('submission/selection.json'))\\nassert isinstance(s,list) and len(s)==len(set(s)) and all(type(i) is int and 0<=i<182016 for i in s)\\na=np.load('train2.npy',mmap_mode='r'); assert len(a)==12_000_000\\nprint({'selection_ids':len(s),'unique':len(set(s)),'packed_tokens':len(a),'dev_ppl':json.load(open('result2.json'))['heldout_ppl']})\\nPY\\npython -m py_compile submission/curate.py\\nls -lh submission/selection.json submission/curate.py claim.md\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"cat /workspace/.timer/remaining_secs; python - <<'PY'\\nimport json\\nfrom pathlib import Path\\npaths=[Path('/workspace/submission/selection.json'),Path('/workspace/submission/curate.py'),Path('/workspace/claim.md')]\\nfor p in paths: print(p, p.exists(), p.stat().st_size if p.exists() else None)\\ns=json.load(open(paths[0])); print('ids',len(s),'unique',len(set(s)),'range',min(s),max(s))\\nPY\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n"
}