gpt-5.6-sol

{
  "cell_id": "157",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:89641,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n"
}

{
  "cell_id": "158",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.exec_command({cmd:\"ps -o pid,etime,pcpu,rss -p 4702; stat -c '%y %s' submission/selection.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:89641,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n"
}

{
  "cell_id": "160",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.exec_command({cmd:\"ps -o pid,etime,pcpu,rss -p 4702; stat -c '%y %s' submission/selection.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:89641,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python3 - <<'PY'\\nimport json\\nx=json.load(open('submission/selection.json'));y=json.load(open('/tmp/wiki40clean.json'))\\nprint(len(x),len(y),x==y, next(((i,a,b) for i,(a,b) in enumerate(zip(x,y)) if a!=b),None),x[:20])\\nPY\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+For a 12M-token, 30M-parameter training run, a small amount of fluent,\\n+information-dense, target-register-matched English is more valuable than a\\n+random sample of the much larger crawl. The best mixture should slightly\\n+overweight encyclopedic prose because that quarter is the hardest register for\\n+the fixed model, while retaining broad web, news, and technical Q&A coverage.\\n+\\n+# Mechanism\\n+\\n+`curate.py` combines structural prose quality with clipped unigram log-odds\\n+against each of the four disclosed dev quarters. It first ranks sufficiently\\n+long English documents while penalizing navigation, commerce/SEO phrases,\\n+URLs, repetition, fragments, and abnormal sentence statistics. It then makes\\n+a token-weighted register mixture (40% encyclopedic, 20% edited web, 20% news,\\n+20% technical Q&A), suppresses normalized exact duplicates, and defers text\\n+with more than 8% non-ASCII characters.\\n+\\n+An observable prediction independent of final perplexity is that the consumed\\n+prefix will contain substantially fewer navigation/privacy/cart phrases,\\n+non-English bytes, and normalized duplicates than a random 12M-token prefix.\\n+Its estimated register token shares should be close to 40/20/20/20. A further\\n+prediction is that a diagnostic split by target quarter will show the largest\\n+relative gain on the encyclopedic quarter when moving from 25% to 40%\\n+encyclopedic data.\\n+\\n+# Falsification\\n+\\n+The hypothesis is falsified if a seed-matched frozen training run on this prefix\\n+does not beat random selection, if the predicted contamination reductions are\\n+absent, or if replacing the 40/20/20/20 merge with an equal-register merge\\n+consistently improves held-out loss across multiple disjoint targets. It is also\\n+weakened if duplicate suppression or the English gate hurts on a hidden sample,\\n+which would indicate that multilingual/templated material was useful rather\\n+than wasted budget.\\n+\\n+# Transfer\\n+\\n+The quality filters and duplicate suppression transfer directly to other raw\\n+web pools. The register log-odds and mixture weights should be re-estimated when\\n+the evaluation distribution, tokenizer, model capacity, or token budget\\n+changes. In particular, a multilingual target should relax the ASCII gate, and\\n+a code-heavy target should increase the technical queue rather than reuse these\\n+English broad-domain weights.\\n*** End Patch\"; text(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python3 -m py_compile submission/curate.py && python3 - <<'PY'\\nimport json,os,numpy as np\\np='submission/selection.json';x=json.load(open(p))\\nassert isinstance(x,list) and len(x)==len(set(x)) and all(type(i) is int and 0<=i<182016 for i in x)\\na=np.load('/tmp/dev_train.npy')\\nassert len(a)==12_000_000\\nprint({'ids':len(x),'unique':len(set(x)),'min':min(x),'max':max(x),'packed_tokens':len(a)})\\nfor p in ['submission/selection.json','submission/curate.py','claim.md']:\\n print(p,os.path.getsize(p))\\nPY\\ncat /tmp/dev.json 2>/dev/null || true\\ncat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n"
}

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

{
  "input": "const r = await tools.write_stdin({session_id:10336,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n"
}

{
  "cell_id": "167",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:10336,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n"
}

{
  "cell_id": "168",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:10336,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"cat /workspace/.timer/remaining_secs && python3 - <<'PY'\\nimport json, os\\npaths=['/workspace/submission/selection.json','/workspace/submission/curate.py','/workspace/claim.md']\\nfor p in paths:\\n assert os.path.isfile(p), p\\n print(p, os.path.getsize(p))\\nx=json.load(open(paths[0])); assert len(x)==len(set(x)); assert all(type(i) is int and 0<=i<182016 for i in x)\\nprint('valid ids',len(x),'unique',len(set(x)))\\nPY\\ncat /tmp/dev.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);\n"
}