gpt-5.6-sol

{
  "input": "const r=await tools.write_stdin({session_id:14062,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}

{
  "cell_id": "64",
  "yield_time_ms": 60000,
  "max_tokens": 1000
}

{
  "input": "const r=await tools.exec_command({cmd:\"python train_nano.py --train_npy /tmp/s1.npy --heldout_npy data/multi_dev.npy --out_json /tmp/s1r.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:28596,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}

{
  "cell_id": "66",
  "yield_time_ms": 60000,
  "max_tokens": 1000
}

{
  "input": "const r=await tools.exec_command({cmd:\"python pack_selection.py /tmp/s2.json /tmp/s2.npy\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(JSON.stringify(r))\n"
}

{
  "input": "const r=await tools.write_stdin({session_id:75923,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}

{
  "cell_id": "68",
  "yield_time_ms": 60000,
  "max_tokens": 1000
}

{
  "input": "const r=await tools.exec_command({cmd:\"python train_nano.py --train_npy /tmp/s2.npy --heldout_npy data/multi_dev.npy --out_json /tmp/s2r.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:80336,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}

{
  "cell_id": "70",
  "yield_time_ms": 60000,
  "max_tokens": 1000
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Delete File: /workspace/submission/curate.py\\n*** Add File: /workspace/submission/curate.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Conservative English quality filtering with distribution-preserving ordering.\\n+\\n+This intentionally rejects only strong raw-crawl failure signals.  Eligible\\n+documents are ordered by a stable content hash, preserving the pool's broad\\n+topic/register distribution instead of concentrating on one quality-score tail.\\n+\\\"\\\"\\\"\\n+import hashlib, json, re\\n+from pathlib import Path\\n+\\n+POOL = Path('/workspace/data/pool.jsonl')\\n+OUT = Path('/workspace/submission/selection.json')\\n+WORDS = re.compile(r\\\"[A-Za-z]+(?:'[A-Za-z]+)?\\\")\\n+STOP = set(('the of and to in a is was for that on as with by are from at it an '\\n+            'be this which or were has have not but can their its they he she his '\\n+            'her also into after between than').split())\\n+CHROME = re.compile(r'cookie|privacy policy|sign in|log in|shopping cart|all rights reserved', re.I)\\n+\\n+def eligible(text):\\n+    n = len(text)\\n+    if n < 700 or n > 60_000:\\n+        return False\\n+    # Sampling the first 16k characters makes the decision cheap and prevents\\n+    # very long documents from having disproportionate influence on the gate.\\n+    sample = text[:16_000]\\n+    words = WORDS.findall(sample)\\n+    if len(words) < 130:\\n+        return False\\n+    letters = sum(c.isalpha() for c in sample)\\n+    ascii_letters = sum('a' <= c.lower() <= 'z' for c in sample)\\n+    if letters / len(sample) < .54 or ascii_letters / max(1, letters) < .91:\\n+        return False\\n+    lower = [w.lower() for w in words]\\n+    diversity = len(set(lower)) / len(lower)\\n+    function_rate = sum(w in STOP for w in lower) / len(lower)\\n+    if diversity < .23 or not (.24 <= function_rate <= .58):\\n+        return False\\n+    if len(CHROME.findall(sample)) > 6 or sample.count('|') / len(sample) > .006:\\n+        return False\\n+    return True\\n+\\n+def main():\\n+    ranked = []\\n+    with POOL.open() as f:\\n+        for line in f:\\n+            doc = json.loads(line)\\n+            text = doc['text']\\n+            if eligible(text):\\n+                key = int.from_bytes(hashlib.blake2b(text[:4096].encode(), digest_size=8).digest(), 'big')\\n+                ranked.append((key, doc['id']))\\n+    ranked.sort()\\n+    OUT.parent.mkdir(parents=True, exist_ok=True)\\n+    OUT.write_text(json.dumps([doc_id for _, doc_id in ranked]))\\n+    print(f'wrote {len(ranked)} ids to {OUT}')\\n+\\n+if __name__ == '__main__':\\n+    main()\\n*** End Patch\"; text(await tools.apply_patch(patch));\n"
}

{
  "input": "const r=await tools.exec_command({cmd:\"python -u submission/curate.py\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(JSON.stringify(r))\n"
}

{
  "input": "const r=await tools.write_stdin({session_id:69527,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}

{
  "cell_id": "73",
  "yield_time_ms": 60000,
  "max_tokens": 1000
}

{
  "input": "const r=await tools.write_stdin({session_id:69527,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}

{
  "cell_id": "74",
  "yield_time_ms": 60000,
  "max_tokens": 1000
}

{
  "input": "const r=await tools.write_stdin({session_id:69527,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"cmp submission/selection.json /tmp/strict.json; echo cmp_status=$?; python - <<'PY'\\nimport json\\nx=json.load(open('submission/selection.json'))\\nprint(len(x),len(set(x)),min(x),max(x))\\nPY\\ncat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output)\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+Under a 12M-token budget and a 30M-parameter model, conservative removal of clear crawl failures will outperform both random data and aggressive “best-document” ranking. The useful signal is broad edited English, not maximal lexical rarity. A stable content-hash ordering preserves the pool's topic and register diversity while avoiding any dependence on opaque document IDs.\\n+\\n+# Mechanism\\n+\\n+The filter removes documents with strong indicators of non-English text, templates/lists, boilerplate, low linguistic content, or degenerate repetition. It requires normal alphabetic density, mostly ASCII English letters, a plausible English function-word rate, minimum lexical diversity, and limited navigation chrome. It deliberately does not rank the survivors by a scalar quality score, because doing so concentrates the 12M-token prefix on stylistic extremes.\\n+\\n+An observable prediction independent of final held-out perplexity is that the selected prefix will have higher alphabetic and English function-word density and fewer pipe-delimited/navigation-heavy pages than a random prefix, while retaining documents from news, expository prose, informal web prose, and technical discussion. The script retains 96,229 of 182,016 documents, leaving ample exact-token coverage.\\n+\\n+# Falsification\\n+\\n+The hypothesis is falsified if conservative filtering fails to improve dev cross-entropy over random, or if an aggressive quality tail performs better without losing domain coverage. In fixed-recipe dev experiments, random selection gave perplexity 476.714; a lexical-richness/domain-ranked candidate was worse at 512.885; a revised aggressive rank was 495.955; a loose conservative filter reached 449.894; and the submitted conservative English filter reached 437.128. Prefix deduplication also failed its test (451.628), so it was not included.\\n+\\n+# Transfer\\n+\\n+The gates use generic surface statistics rather than source names, topic keywords, labels, or hand-picked IDs. They should transfer to other heterogeneous English crawls and small-model regimes where token scarcity makes obvious boilerplate costly. Thresholds may need recalibration for multilingual corpora, code-heavy targets, or much larger models/token budgets; in those settings, non-ASCII text, markup, rare vocabulary, and duplicates may carry more value.\\n*** End Patch\"; text(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python -m py_compile submission/curate.py && test -s claim.md && test -s submission/selection.json && sha256sum submission/selection.json submission/curate.py claim.md && git diff --no-index /dev/null submission/curate.py | head -20; cat /tmp/rs.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output)\n"
}