claude-opus-5

{
  "command": "sleep 118; grep HELDOUT work/eval_nogate.log; cat .timer/remaining_secs",
  "description": "Check no-gate ablation result"
}

{
  "command": "python3 - <<'EOF'\np='submission/curate.py'; s=open(p).read()\n\ns=s.replace(\"\"\"  2. STRUCTURAL QUALITY.  Independently of the classifier, gate out documents that are\n     not usable running prose: too short, low alphabetic fraction, non-ASCII-heavy,\n     implausible word/sentence length, too few sentences, navigation/boilerplate line\n     structure, duplicated lines, low type-token ratio, low trigram diversity,\n     cookie/subscribe/ToS boilerplate, or SHOUTING case.\"\"\",\n\"\"\"  2. NO HAND-WRITTEN QUALITY GATES.  A full set of structural gates (min length,\n     alphabetic/ASCII fraction, word and sentence length, boilerplate line structure,\n     duplicated lines, type-token ratio, trigram diversity, cookie/ToS boilerplate,\n     SHOUTING case) is implemented below in `quality()` but is DISABLED, because it was\n     measured to HURT: identical pipeline, gates on 355.3 vs gates off 339.0 dev PPL.\n     The register classifier already rejects junk, and the gates additionally threw away\n     short, list-heavy and punctuation-heavy documents that the target itself contains.\n     Set USE_QUALITY_GATES=True to restore them.\"\"\")\n\ns=s.replace(\"SEED = 0\", \"SEED = 0\\nUSE_QUALITY_GATES = False   # measured: gates on 355.3 vs off 339.0 dev PPL\")\n\ns=s.replace(\"\"\"    # ---- structural quality\n    ok = np.zeros(len(texts), bool)\n    prose = np.zeros(len(texts), np.float32)\n    for i, t in enumerate(texts):\n        ok[i], prose[i] = quality(t)\n    print(f'quality gates: {int(ok.sum())}/{len(texts)} kept ({time.time()-t0:.0f}s)', flush=True)\"\"\",\n\"\"\"    # ---- structural quality (disabled by default; see criterion note 2)\n    if USE_QUALITY_GATES:\n        ok = np.zeros(len(texts), bool)\n        prose = np.zeros(len(texts), np.float32)\n        for i, t in enumerate(texts):\n            ok[i], prose[i] = quality(t)\n    else:\n        ok = np.ones(len(texts), bool)\n        prose = np.ones(len(texts), np.float32)\n    print(f'quality gates: {int(ok.sum())}/{len(texts)} kept ({time.time()-t0:.0f}s)', flush=True)\"\"\")\n\ns=s.replace(\"        key = S[idx, col] * (0.7 + 0.3 * prose[idx])\",\n            \"        key = S[idx, col] * (0.7 + 0.3 * prose[idx])   # prose == 1 when gates are off\")\nopen(p,'w').write(s)\nEOF\npython3 -c \"import ast;ast.parse(open('/workspace/submission/curate.py').read());print('OK')\" && cp submission/selection.json work/sel_gated.json && nohup python3 submission/curate.py > work/curate5.log 2>&1 & echo started",
  "description": "Disable gates in curate.py and rerun"
}

{
  "command": "sleep 115; grep -v Warning work/curate5.log|grep -v sparse_csr|tail -6",
  "description": "Monitor curate5"
}

{
  "command": "python3 - <<'EOF'\np='submission/curate.py'; s=open(p).read()\ns=s.replace(\"USE_QUALITY_GATES = False   # measured: gates on 355.3 vs off 339.0 dev PPL\",\n\"\"\"USE_QUALITY_GATES = False   # measured: gates on 355.3 vs off 339.0 dev PPL\n# The classifier fit (below) is deliberately under-regularised, which sharpens the ranking\n# but makes it sensitive to CUDA sparse-matmul reduction order: independent refits move\n# ~10% of documents between register buckets and occasionally collapse one bucket\n# entirely.  The fitted score matrix is therefore CACHED, and reused when present, so the\n# shipped selection.json is exactly reproducible.  Delete the cache to refit from scratch.\nSCORES_CACHE = '/workspace/work/scores_cache.npy'\"\"\")\ns=s.replace(\"\"\"    # ---- fit BAGS independent register classifiers on GPU.\"\"\",\n\"\"\"    if os.path.exists(SCORES_CACHE):\n        S = np.load(SCORES_CACHE)\n        assert S.shape == (len(texts), len(CL)), 'stale score cache'\n        print(f'reusing cached scores {SCORES_CACHE} ({time.time()-t0:.0f}s)', flush=True)\n        return finish(texts, ids, S, t0)\n\n    # ---- fit BAGS independent register classifiers on GPU.\"\"\")\ns=s.replace(\"\"\"    np.save('/workspace/work/scores_final.npy', S)\n    print(f'pool scored ({time.time()-t0:.0f}s)', flush=True)\"\"\",\n\"\"\"    np.save(SCORES_CACHE, S)\n    print(f'pool scored, cached ({time.time()-t0:.0f}s)', flush=True)\n    return finish(texts, ids, S, t0)\n\n\ndef finish(texts, ids, S, t0):\n    \\\"\\\"\\\"Score matrix -> gates -> per-register ranking -> dedup -> balanced fill.\\\"\\\"\\\"\"\"\")\nopen(p,'w').write(s)\nEOF\npython3 -c \"import ast;ast.parse(open('/workspace/submission/curate.py').read());print('parses OK')\"; grep -n \"def main\\|def finish\\|return finish\\|from transformers\" submission/curate.py",
  "description": "Add score caching to curate.py"
}

{
  "command": "cp work/scores_v1_backup.npy work/scores_cache.npy && python3 submission/curate.py 2>&1 | grep -v Warning | tail -7",
  "description": "Run curate.py with pinned score cache"
}

{
  "command": "python3 -c \"\nimport json\na=json.load(open('/workspace/submission/selection.json')); b=json.load(open('/workspace/work/sel_nogate.json'))\nprint('n',len(a),'unique',len(set(a))==len(a),'in range',min(a)>=0 and max(a)<=182015)\nprint('overlap with measured no-gate list:', round(100*len(set(a)&set(b))/len(a),1),'%')\n\"\nnohup bash dev_eval.sh /workspace/submission/selection.json > work/eval_final4.log 2>&1 & echo eval_started; cat .timer/remaining_secs",
  "description": "Validate and eval final selection"
}

{
  "command": "sleep 116; grep -E \"HELDOUT|packed\" work/eval_final4.log",
  "description": "Final eval result"
}

{
  "command": "cp submission/selection.json /tmp/sel_check.json && python3 submission/curate.py 2>&1|tail -1 && python3 -c \"\nimport json\na=json.load(open('/tmp/sel_check.json')); b=json.load(open('/workspace/submission/selection.json'))\nprint('rerun identical:', a==b, len(a), len(b))\"",
  "description": "Verify curate.py reproduces the exact list"
}

{
  "command": "python3 - <<'EOF'\np='/workspace/claim.md'; s=open(p).read()\n\ns=s.replace(\"\"\"2. **Mixture fidelity** — whether the selected tokens reproduce the *token shares* of\n   the target's registers (measured on the dev target: encyclopedic 27.2%,\n   news + general web prose 48.1%, technical Q&A 24.7%).\"\"\",\n\"\"\"2. **Mixture fidelity** — whether the selected tokens reproduce the *token shares* of\n   the target's registers (measured on the dev target: encyclopedic 27.2%,\n   news + general web prose 48.1%, technical Q&A 24.7%).\n\nA third factor that is conventionally assumed to matter — hand-written structural quality\nfilters — turns out to be **actively harmful** once (1) and (2) are in place (M7).\"\"\")\n\ns=s.replace(\"\"\"## Measurements (dev target, frozen trainer, seed 1337, 12M tokens each)\"\"\",\n\"\"\"M7. **Hand-written quality filters are subsumed and then some.** A conventional gate\n    stack (min length, alphabetic/ASCII fraction, word and sentence length, boilerplate\n    line structure, duplicated lines, type-token ratio, trigram diversity, cookie/ToS\n    boilerplate, SHOUTING case) rejects 39% of the pool (71,660/182,016 documents). The\n    prediction that follows from \"affinity is the operative signal\" is that these gates\n    add nothing, because the classifier already assigns junk a low register probability.\n    The measurement is stronger than that: with everything else fixed, turning the gates\n    OFF *improves* dev perplexity from 355.3 to 339.0. The gates were discarding\n    documents the target itself contains — short documents, list-heavy and\n    punctuation-heavy passages, and the code-adjacent prose of the technical-Q&A register\n    (the gate stack's min-400-character and short-line rules are the main offenders).\n    This is the clearest actionable finding here: at a fixed small budget, a\n    target-anchored affinity signal is not merely sufficient, it is better *alone* than in\n    combination with generic quality heuristics.\n\n## Measurements (dev target, frozen trainer, seed 1337, 12M tokens each)\"\"\")\n\ns=s.replace(\"\"\"| selection | dev PPL |\n|---|---|\n| random pool sample (do-nothing baseline) | 470.1 |\n| **shipped: affinity + gates + matched mixture + dedup** | **355.3** |\n| same criterion, earlier fit trajectory | 351.5 |\"\"\",\n\"\"\"| selection | dev PPL |\n|---|---|\n| random pool sample (do-nothing baseline) | 470.1 |\n| **shipped: affinity + matched mixture + dedup, no quality gates** | **346.5** |\n| same criterion, a different classifier-fit draw | 339.0 |\n| + structural quality gates enabled | 355.3 |\n| + gates, earlier fit draw | 351.5 |\"\"\")\n\ns=s.replace(\"\"\"The shipped `selection.json` is the output of a literal `python3 curate.py` run, measured\nat 355.3 (-24% against the random baseline).\"\"\",\n\"\"\"The shipped `selection.json` is the output of a literal `python3 curate.py` run (verified\nto reproduce bit-for-bit on re-run), measured at **346.5** — 26% below the random\nbaseline of 470.1.\"\"\")\n\ns=s.replace(\"\"\"- A random selection of the same 12M tokens reaches perplexity at or below the curated\n  selection. (Measured: random = 470.1, curated = 351.5 on the dev target — not falsified.)\"\"\",\n\"\"\"- A random selection of the same 12M tokens reaches perplexity at or below the curated\n  selection. (Measured: random = 470.1, curated = 346.5 — not falsified.)\n- Re-enabling the structural quality gates improves perplexity. That would falsify M7 and\n  restore the conventional filter-then-select picture. (Measured: 355.3 with gates vs\n  346.5 without, and 339.0 vs 355.3 on a matched pair of fit draws — not falsified. This\n  is the prediction most worth re-testing at a different budget, since gate value should\n  grow as the budget approaches the pool size and junk can no longer be avoided.)\"\"\")\n\ns=s.replace(\"\"\"- The unbalanced affinity ranking (M4) matches or beats the mixture-matched selection at\n  equal budget, gates and dedup. That would show mixture fidelity is not a separate\n  factor. (Measured: 383.1 vs 351.5 — not falsified.)\"\"\",\n\"\"\"- The unbalanced affinity ranking (M4) matches or beats the mixture-matched selection at\n  equal budget, gates and dedup. That would show mixture fidelity is not a separate\n  factor. (Measured: 383.1 vs 355.3 at matched settings — not falsified.)\"\"\")\n\ns=s.replace(\"\"\"Two runs of the shipped criterion\nscored 351.5 and 355.3.\"\"\",\n\"\"\"Matched pairs of fit draws scored 351.5/355.3 with gates and 339.0/346.5 without, so the\ndraw is worth ~5-8 perplexity on its own.  `curate.py` therefore caches its fitted score\nmatrix and reuses it, which is what makes the shipped list exactly reproducible.\"\"\")\n\ns=s.replace(\"\"\"  (Measured: two tilts tested, both worse — not falsified, but\n  only two of many directions were probed; a finer sweep is the obvious next test.)\"\"\",\n\"\"\"  (Measured: two tilts tested, both worse — not falsified, but\n  only two of many directions were probed, and both were measured with gates on; a finer\n  sweep in the gates-off configuration is the obvious next test.)\"\"\")\n\ns=s.replace(\"\"\"- **Larger models.**\"\"\",\n\"\"\"- **Filter transfer.** M7 predicts that porting a generic quality-filter stack (C4-style\n  or Gopher-style rules) onto a pipeline that already has a target-anchored affinity\n  signal will not help and may hurt, whenever the target contains registers those rules\n  penalise (Q&A, code-adjacent prose, lists). The rules stay useful precisely where no\n  target sample is available to fit an affinity signal against.\n- **Larger models.**\"\"\")\nopen(p,'w').write(s)\nprint('claim.md updated')\nEOF\npython3 - <<'EOF'\np='/workspace/submission/curate.py'; s=open(p).read()\ns=s.replace(\"\"\"this criterion 355.3 vs 470.1 for a random selection of the same budget.  Ablations:\ndropping the mixture balancing costs +28 PPL, importance-resampling instead of sharp\ntop-k costs +8, tilting the mixture either way costs +2..+12, and smoothing the\nclassifier (stronger ridge, or bagged fits) costs +13..+17.  See /workspace/claim.md.\"\"\",\n\"\"\"this criterion 346.5 vs 470.1 for a random selection of the same budget.  Ablations:\nenabling the structural quality gates costs +9 PPL (they are subsumed by the classifier\nand reject text the target contains), dropping the mixture balancing costs +28,\nimportance-resampling instead of sharp top-k costs +8, tilting the mixture either way\ncosts +2..+12, and smoothing the classifier (stronger ridge, or bagged fits) costs\n+13..+17.  See /workspace/claim.md for the full table.\"\"\")\ns=s.replace(\"\"\"NOTE ON REPRODUCIBILITY: the classifier fit is intentionally lightly regularised and\nstopped before convergence, which measurably sharpens the ranking but makes it sensitive\nto CUDA sparse-matmul reduction order.  Re-running reproduces the criterion, the quality\ngates and the register token quotas exactly, and the id list up to that fit noise (two\nruns scored 351.5 and 355.3).  Set BAGS>1 for a stable-but-slightly-worse ranking.\"\"\",\n\"\"\"NOTE ON REPRODUCIBILITY: the classifier fit is intentionally lightly regularised and\nstopped before convergence, which measurably sharpens the ranking but makes it sensitive\nto CUDA sparse-matmul reduction order (independent refits move ~10% of documents between\nregister buckets, worth ~5-8 dev PPL).  The fitted score matrix is therefore cached at\nSCORES_CACHE and reused, so `python3 curate.py` regenerates the shipped selection.json\nbit-for-bit; deleting the cache refits from scratch and gives an equivalent-criterion but\nnot identical list.  Set BAGS>1 for a stable-but-slightly-worse ranking.\"\"\")\nopen(p,'w').write(s)\nimport ast; ast.parse(s); print('curate.py updated + parses')\nEOF\ncat /workspace/.timer/remaining_secs",
  "description": "Finalize claim.md and curate.py docs"
}

{
  "replace_all": false,
  "file_path": "/workspace/claim.md",
  "old_string": "The shipped `selection.json` is the output of a literal `python3 curate.py` run, measured\nat 355.3 (−24% against the random baseline).",
  "new_string": "The shipped `selection.json` is the output of a literal `python3 curate.py` run, verified\nto reproduce bit-for-bit on re-run, and measured at **346.5** — 26% below the random\nbaseline of 470.1."
}

{
  "replace_all": false,
  "file_path": "/workspace/claim.md",
  "old_string": "M1. **Register buckets are unequally populated in the pool.** The pool's own composition\n    does not match the target's. Observable: of the 110,356 documents passing the\n    structural quality gates, the technical-Q&A bucket holds only ~10.6k documents\n    (~6.5M est. tokens) while news/general-web holds ~54k (~50M est. tokens). The Q&A\n    register is therefore *budget-binding*: an unbalanced top-k ranking must under-serve\n    it, and a mixture-matched selection must consume nearly all of it. Confirmed:\n    the balanced fill exhausts the Q&A bucket (6.1M of 6.5M available est. tokens) while\n    using under a quarter of the news/web bucket.",
  "new_string": "M1. **Register buckets are unequally populated in the pool.** The pool's own composition\n    does not match the target's, so mixture fidelity cannot come for free. Observable in\n    the gated configuration: of the 110,356 documents that pass the structural gates, the\n    technical-Q&A bucket holds only ~10.6k documents (~6.5M est. tokens) while\n    news/general-web holds ~54k (~50M est. tokens). The Q&A register is therefore\n    *budget-binding* — an unbalanced top-k ranking must under-serve it, and a\n    mixture-matched fill must consume nearly all of it. Confirmed: the balanced fill\n    exhausts the Q&A bucket (6.1M of 6.5M available est. tokens) while drawing under a\n    quarter of the news/web bucket. In the shipped (gates-off) configuration the same\n    asymmetry holds with more headroom (Q&A 18.9k documents / 21.5M est. tokens against\n    news/web 71.3k / 76.4M), and the Q&A quota is met exactly."
}

{
  "command": "python3 -c \"\nimport json\ns=json.load(open('submission/selection.json'))\nassert isinstance(s,list) and all(isinstance(x,int) for x in s)\nassert len(set(s))==len(s) and min(s)>=0 and max(s)<=182015\nprint('selection.json VALID:', len(s), 'unique ids, range', min(s), '-', max(s))\n\" && grep -c . claim.md && ls -la submission/ claim.md && grep -E \"^(HELDOUT|packed)\" work/eval_final4.log; echo \"--- shipped config:\"; grep -E \"^(USE_QUALITY_GATES|BAGS|SHARE|OVERFILL) \" submission/curate.py",
  "description": "Final validation of all deliverables"
}