claude-opus-4-8

common vocab: 40974
=== REG0 (wiki) picks now ===
total 40000
id 18830 :: Archaeological dating methods Futilities must undress thitherward convened dismissively towerish tom
id 132935 :: 236.14<|endoftext|>Shoulder Arthroscopy Cost in India | Shoulder Arthroscopy in India × Help Us With
id 98806 ::  hesiall, Loo. Micks. The ness arou shis. I therrying ot! Mmmend bou. Mays 14 he sairry, by norms ne
id 34104 :: <|endoftext|>Stanleigh Encounters Stanleigh, his five-way excommunicated with his head uncovered? Th
id 73087 ::  isolative that reived discordantly? Gil Gil schizophà © tico undoing it in a carved it might be you
id 45694 :: ures on forgiveness in marriage Anodic and rude jodi equaling her blood tricks or socially plasticiz

[stdout]
common vocab: 40974
=== REG0 (wiki) picks now ===
total 40000
id 18830 :: Archaeological dating methods Futilities must undress thitherward convened dismissively towerish tom
id 132935 :: 236.14<|endoftext|>Shoulder Arthroscopy Cost in India | Shoulder Arthroscopy in India × Help Us With
id 98806 ::  hesiall, Loo. Micks. The ness arou shis. I therrying ot! Mmmend bou. Mays 14 he sairry, by norms ne
id 34104 :: <|endoftext|>Stanleigh Encounters Stanleigh, his five-way excommunicated with his head uncovered? Th
id 73087 ::  isolative that reived discordantly? Gil Gil schizophà © tico undoing it in a carved it might be you
id 45694 :: ures on forgiveness in marriage Anodic and rude jodi equaling her blood tricks or socially plasticiz
[metadata] {"noOutputExpected": false}

common vocab: 40974
passed gates: 126785
wrote 40000 ids -> /workspace/submission/selection.json
=== REG0 picks ===
id 155591 :: scopy Cost in India | Shoulder Arthroscopy in India × Help Us With Your Details CountryAfghanis
id 60321 :: izable Percy peptonizes supposedly dating in thailand reddit your consumptions just divorced da
id 45694 :: ures on forgiveness in marriage Anodic and rude jodi equaling her blood tricks or socially plas
id 82521 :: More emblazoned than asian women dating in burnsville mn rubber stamps neatly? Shriek and Pyrrh
id 106746 :: <|endoftext|>Lathal Bine was a Peliar Zel native, a resident of Beta moon, during the 24th cent
id 40208 :: Swedish actor Joel Kinnaman began his acting career in 2002, initially appearing in films and t

[stdout]
common vocab: 40974
passed gates: 126785
wrote 40000 ids -> /workspace/submission/selection.json
=== REG0 picks ===
id 155591 :: scopy Cost in India | Shoulder Arthroscopy in India × Help Us With Your Details CountryAfghanis
id 60321 :: izable Percy peptonizes supposedly dating in thailand reddit your consumptions just divorced da
id 45694 :: ures on forgiveness in marriage Anodic and rude jodi equaling her blood tricks or socially plas
id 82521 :: More emblazoned than asian women dating in burnsville mn rubber stamps neatly? Shriek and Pyrrh
id 106746 :: <|endoftext|>Lathal Bine was a Peliar Zel native, a resident of Beta moon, during the 24th cent
id 40208 :: Swedish actor Joel Kinnaman began his acting career in 2002, initially appearing in films and t
[metadata] {"noOutputExpected": false}

common bigrams 497886
--- gibberish/spun (want LOW) ---
18830 0.14
98806 0.13
34104 0.19
73087 0.17
60321 0.19
45694 0.18
82521 0.19
132935 0.40
--- good (want HIGH) ---
58452 0.64
23673 0.31
24738 0.37
46916 0.28
37064 0.62
88237 0.28
153492 0.32
106746 0.36
40208 0.41

[stdout]
common bigrams 497886
--- gibberish/spun (want LOW) ---
18830 0.14
98806 0.13
34104 0.19
73087 0.17
60321 0.19
45694 0.18
82521 0.19
132935 0.40
--- good (want HIGH) ---
58452 0.64
23673 0.31
24738 0.37
46916 0.28
37064 0.62
88237 0.28
153492 0.32
106746 0.36
40208 0.41
[metadata] {"noOutputExpected": false}

The file /workspace/submission/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "# reference \"common English words\" (proxy dictionary) from positives + negatives.\n# Used to reject gibberish / word-salad SEO spam whose tokens are mostly rare:\n# such docs otherwise score HIGH on the encyclopedic register (WikiText's diverse\n# rare vocabulary), polluting a whole quarter of the budget.\n_ref = Counter()\nfor c in [x for g in reg_pos for x in g] + neg_texts:\n    _ref.update(WORD.findall(c.lower()))\nCOMMON = frozenset(w for w, cnt in _ref.items() if cnt >= 5)\nprint(f\"common vocab: {len(COMMON)}\", file=sys.stderr)", "newString": "# reference \"common English word BIGRAMS\" (fluency model) from positives+negatives.\n# Fraction of a doc's word-bigrams that are attested is a fluency signal: it rejects\n# \"spun\"/word-salad SEO spam (common words in nonsensical order) that otherwise scores\n# HIGH on the encyclopedic register (WikiText's diverse vocabulary) and pollutes a\n# whole quarter of the budget.  Vocabulary alone can't catch these; bigrams can.\n_bg = Counter()\nfor c in [x for g in reg_pos for x in g] + neg_texts:\n    w = WORD.findall(c.lower())\n    for j in range(len(w) - 1):\n        _bg[(w[j], w[j + 1])] += 1\nCOMMON_BG = frozenset(k for k, v in _bg.items() if v >= 2)\nprint(f\"common bigrams: {len(COMMON_BG)}\", file=sys.stderr)\ndef fluency(ws):\n    if len(ws) < 2: return 0.0\n    return sum(1 for j in range(len(ws) - 1) if (ws[j], ws[j + 1]) in COMMON_BG) / (len(ws) - 1)", "originalFile": "\"\"\"Curate a raw web pool for a small LM whose eval target is a BROAD, EQUAL-PARTS\nmulti-domain high-quality English corpus: encyclopedic (Wikipedia), general\nhigh-quality web prose, news, and technical Q&A.\n\nSTATED CRITERION\n----------------\nTwo ideas, composed:\n\n1. Clean-English-prose gate. Keep only documents that read like real English\n   prose: long enough, mostly alphabetic, not table/listing/code, a healthy\n   function-word (stopword) ratio (nav menus, keyword spam, and non-English text\n   fail this), no degenerate line/token repetition. This removes the bulk of raw\n   web junk regardless of topic.\n\n2. Register-balanced target-likeness. The disclosed target is EQUAL parts of four\n   registers, and the dev target stream (data/multi_dev.npy) is laid out in four\n   positional blocks (wiki -> technical/web prose -> news -> Q&A). We split it into\n   four register corpora and fit one linear Naive-Bayes log-odds model per register\n   (register chunks = positive, a random pool sample = negative). Every surviving\n   pool doc gets four per-token log-odds scores. We then fill the priority list by\n   ROUND-ROBIN across the four registers, each round taking the next highest-scoring\n   unused doc for that register. The 12M-token budget is therefore split ~evenly\n   across the four target registers instead of collapsing onto the most abundant one\n   (news), matching the eval's equal-parts composition.\n\nReproducible, deterministic, pure numpy/stdlib (no sklearn). No hand-picked ids.\n\"\"\"\nimport json, re, sys, zlib, math, os\nimport numpy as np\nfrom collections import Counter\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\nSEED = 1337\nK    = 1 << 20\nNREG = 4\nWORD = re.compile(r\"[a-z0-9']+\")\nrng  = np.random.default_rng(SEED)\n\ndef feats(text, cap=2000):\n    w = WORD.findall(text.lower())[:cap]\n    out = [zlib.crc32(x.encode()) & (K - 1) for x in w]\n    for i in range(len(w) - 1):\n        out.append(zlib.crc32((w[i] + \" \" + w[i + 1]).encode()) & (K - 1))\n    return out\n\n# ---------- load pool ----------\nids, texts = [], []\nwith open(POOL) as f:\n    for line in f:\n        r = json.loads(line)\n        ids.append(r[\"id\"]); texts.append(r[\"text\"])\nN = len(ids)\nids = np.array(ids)\nprint(f\"pool: {N} docs\", file=sys.stderr)\n\n# ---------- register positives from the disclosed dev target ----------\nfrom transformers import AutoTokenizer\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\ndev = np.load(DEV)\nchunks, cur = [], []\nfor t in dev.tolist():\n    if t == 50256:\n        if cur: chunks.append(tok.decode(cur)); cur = []\n    else:\n        cur.append(t)\nif cur: chunks.append(tok.decode(cur))\nchunks = [c for c in chunks if len(c) > 200]\n# four positional register groups (dev is laid out wiki|prose|news|qa)\nper = len(chunks) / NREG\nreg_pos = [chunks[int(k * per):int((k + 1) * per)] for k in range(NREG)]\nprint(\"register sizes:\", [len(r) for r in reg_pos], file=sys.stderr)\n\n# ---------- negatives: random pool sample ----------\nneg_idx = rng.choice(N, size=min(6000, N), replace=False)\nneg_texts = [texts[i] for i in neg_idx]\n\n# reference \"common English words\" (proxy dictionary) from positives + negatives.\n# Used to reject gibberish / word-salad SEO spam whose tokens are mostly rare:\n# such docs otherwise score HIGH on the encyclopedic register (WikiText's diverse\n# rare vocabulary), polluting a whole quarter of the budget.\n_ref = Counter()\nfor c in [x for g in reg_pos for x in g] + neg_texts:\n    _ref.update(WORD.findall(c.lower()))\nCOMMON = frozenset(w for w, cnt in _ref.items() if cnt >= 5)\nprint(f\"common vocab: {len(COMMON)}\", file=sys.stderr)\nneg_c = np.zeros(K, dtype=np.float64)\nfor p in neg_texts:\n    fe = feats(p)\n    if fe: np.add.at(neg_c, np.array(fe), 1.0)\nNtot = neg_c.sum()\n\n# ---------- one NB log-odds weight vector per register ----------\nweights = np.zeros((NREG, K), dtype=np.float32)\na = 1.0\nfor k in range(NREG):\n    pos_c = np.zeros(K, dtype=np.float64)\n    for p in reg_pos[k]:\n        fe = feats(p)\n        if fe: np.add.at(pos_c, np.array(fe), 1.0)\n    Ptot = pos_c.sum()\n    w = (np.log(pos_c + a) - math.log(Ptot + a * K)) - \\\n        (np.log(neg_c + a) - math.log(Ntot + a * K))\n    c0 = math.log(Ntot + a * K) - math.log(Ptot + a * K)   # center: neutral seen feature ~ 0\n    w = w - c0\n    w[(pos_c + neg_c) < 3] = 0.0                            # trust only observed features\n    weights[k] = w.astype(np.float32)\n\n# ---------- score every pool doc against all four registers ----------\n_CACHE = \"/tmp/curate_scores4.npy\"\nif os.path.exists(_CACHE) and np.load(_CACHE).shape == (N, NREG):\n    S = np.load(_CACHE)\n    print(\"loaded cached scores\", file=sys.stderr)\nelse:\n    S = np.full((N, NREG), -1e9, dtype=np.float32)\n    for i in range(N):\n        fe = feats(texts[i])\n        if fe:\n            idx = np.array(fe)\n            n = len(fe)\n            for k in range(NREG):\n                S[i, k] = weights[k][idx].sum() / n\n        if i % 40000 == 0: print(f\"scored {i}\", file=sys.stderr)\n    np.save(_CACHE, S)\n\n# ---------- clean-English-prose gate ----------\nSTOP = set(\"the of and to a in is that it for on as with was are be by this at \"\n           \"from or an not but have has had he she they we you i his her their \"\n           \"which who will would can could been were said your all more one about \"\n           \"when what there if so no do does did than then them these those into \"\n           \"out up down over after also its our\".split())\ndef passes(t):\n    if len(t) < 300: return False\n    ws = WORD.findall(t.lower())\n    if len(ws) < 60: return False\n    if sum(c.isalpha() or c.isspace() for c in t) / len(t) < 0.70: return False\n    if sum(c.isdigit() for c in t) / len(t) > 0.15: return False\n    if sum(1 for w in ws if w in STOP) / len(ws) < 0.22: return False   # non-English/nav/code\n    if sum(1 for w in ws if w in COMMON) / len(ws) < 0.70: return False  # gibberish / word-salad spam\n    lines = [l for l in t.split(\"\\n\") if l.strip()]\n    if lines and Counter(lines).most_common(1)[0][1] / len(lines) > 0.5: return False\n    wc = Counter(ws)\n    if len(wc) / len(ws) < 0.30: return False\n    if wc.most_common(1)[0][1] / len(ws) > 0.10: return False\n    return True\nkeep = np.array([passes(t) for t in texts], dtype=bool)\nprint(f\"passed gates: {int(keep.sum())}\", file=sys.stderr)\n\n# ---------- per-register ranked candidate lists (gated) ----------\nkept = np.where(keep)[0]\nranked = []\nfor k in range(NREG):\n    o = kept[np.argsort(-S[kept, k])]\n    ranked.append(list(o))\n\n# ---------- round-robin fill -> balanced priority order, with dedup ----------\nseen, selection, ptr = set(), [], [0, 0, 0, 0]\nTARGET = 40000\nwhile len(selection) < TARGET:\n    progressed = False\n    for k in range(NREG):\n        while ptr[k] < len(ranked[k]):\n            i = ranked[k][ptr[k]]; ptr[k] += 1\n            sig = re.sub(r\"\\s+\", \" \", texts[i].lower()).strip()[:400]\n            h = zlib.crc32(sig.encode())\n            if h in seen: continue\n            seen.add(h); selection.append(int(ids[i])); progressed = True\n            break\n        if len(selection) >= TARGET: break\n    if not progressed: break\n\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids -> {OUT}\", file=sys.stderr)\n", "structuredPatch": [{"oldStart": 76, "oldLines": 15, "newStart": 76, "newLines": 21, "lines": [" neg_idx = rng.choice(N, size=min(6000, N), replace=False)", " neg_texts = [texts[i] for i in neg_idx]", " ", "-# reference \"common English words\" (proxy dictionary) from positives + negatives.", "-# Used to reject gibberish / word-salad SEO spam whose tokens are mostly rare:", "-# such docs otherwise score HIGH on the encyclopedic register (WikiText's diverse", "-# rare vocabulary), polluting a whole quarter of the budget.", "-_ref = Counter()", "+# reference \"common English word BIGRAMS\" (fluency model) from positives+negatives.", "+# Fraction of a doc's word-bigrams that are attested is a fluency signal: it rejects", "+# \"spun\"/word-salad SEO spam (common words in nonsensical order) that otherwise scores", "+# HIGH on the encyclopedic register (WikiText's diverse vocabulary) and pollutes a", "+# whole quarter of the budget.  Vocabulary alone can't catch these; bigrams can.", "+_bg = Counter()", " for c in [x for g in reg_pos for x in g] + neg_texts:", "-    _ref.update(WORD.findall(c.lower()))", "-COMMON = frozenset(w for w, cnt in _ref.items() if cnt >= 5)", "-print(f\"common vocab: {len(COMMON)}\", file=sys.stderr)", "+    w = WORD.findall(c.lower())", "+    for j in range(len(w) - 1):", "+        _bg[(w[j], w[j + 1])] += 1", "+COMMON_BG = frozenset(k for k, v in _bg.items() if v >= 2)", "+print(f\"common bigrams: {len(COMMON_BG)}\", file=sys.stderr)", "+def fluency(ws):", "+    if len(ws) < 2: return 0.0", "+    return sum(1 for j in range(len(ws) - 1) if (ws[j], ws[j + 1]) in COMMON_BG) / (len(ws) - 1)", " neg_c = np.zeros(K, dtype=np.float64)", " for p in neg_texts:", "     fe = feats(p)"]}], "userModified": false, "replaceAll": false}

The file /workspace/submission/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "    if sum(1 for w in ws if w in COMMON) / len(ws) < 0.70: return False  # gibberish / word-salad spam", "newString": "    if fluency(ws) < 0.25: return False                                 # gibberish / spun word-salad spam", "originalFile": "\"\"\"Curate a raw web pool for a small LM whose eval target is a BROAD, EQUAL-PARTS\nmulti-domain high-quality English corpus: encyclopedic (Wikipedia), general\nhigh-quality web prose, news, and technical Q&A.\n\nSTATED CRITERION\n----------------\nTwo ideas, composed:\n\n1. Clean-English-prose gate. Keep only documents that read like real English\n   prose: long enough, mostly alphabetic, not table/listing/code, a healthy\n   function-word (stopword) ratio (nav menus, keyword spam, and non-English text\n   fail this), no degenerate line/token repetition. This removes the bulk of raw\n   web junk regardless of topic.\n\n2. Register-balanced target-likeness. The disclosed target is EQUAL parts of four\n   registers, and the dev target stream (data/multi_dev.npy) is laid out in four\n   positional blocks (wiki -> technical/web prose -> news -> Q&A). We split it into\n   four register corpora and fit one linear Naive-Bayes log-odds model per register\n   (register chunks = positive, a random pool sample = negative). Every surviving\n   pool doc gets four per-token log-odds scores. We then fill the priority list by\n   ROUND-ROBIN across the four registers, each round taking the next highest-scoring\n   unused doc for that register. The 12M-token budget is therefore split ~evenly\n   across the four target registers instead of collapsing onto the most abundant one\n   (news), matching the eval's equal-parts composition.\n\nReproducible, deterministic, pure numpy/stdlib (no sklearn). No hand-picked ids.\n\"\"\"\nimport json, re, sys, zlib, math, os\nimport numpy as np\nfrom collections import Counter\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\nSEED = 1337\nK    = 1 << 20\nNREG = 4\nWORD = re.compile(r\"[a-z0-9']+\")\nrng  = np.random.default_rng(SEED)\n\ndef feats(text, cap=2000):\n    w = WORD.findall(text.lower())[:cap]\n    out = [zlib.crc32(x.encode()) & (K - 1) for x in w]\n    for i in range(len(w) - 1):\n        out.append(zlib.crc32((w[i] + \" \" + w[i + 1]).encode()) & (K - 1))\n    return out\n\n# ---------- load pool ----------\nids, texts = [], []\nwith open(POOL) as f:\n    for line in f:\n        r = json.loads(line)\n        ids.append(r[\"id\"]); texts.append(r[\"text\"])\nN = len(ids)\nids = np.array(ids)\nprint(f\"pool: {N} docs\", file=sys.stderr)\n\n# ---------- register positives from the disclosed dev target ----------\nfrom transformers import AutoTokenizer\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\ndev = np.load(DEV)\nchunks, cur = [], []\nfor t in dev.tolist():\n    if t == 50256:\n        if cur: chunks.append(tok.decode(cur)); cur = []\n    else:\n        cur.append(t)\nif cur: chunks.append(tok.decode(cur))\nchunks = [c for c in chunks if len(c) > 200]\n# four positional register groups (dev is laid out wiki|prose|news|qa)\nper = len(chunks) / NREG\nreg_pos = [chunks[int(k * per):int((k + 1) * per)] for k in range(NREG)]\nprint(\"register sizes:\", [len(r) for r in reg_pos], file=sys.stderr)\n\n# ---------- negatives: random pool sample ----------\nneg_idx = rng.choice(N, size=min(6000, N), replace=False)\nneg_texts = [texts[i] for i in neg_idx]\n\n# reference \"common English word BIGRAMS\" (fluency model) from positives+negatives.\n# Fraction of a doc's word-bigrams that are attested is a fluency signal: it rejects\n# \"spun\"/word-salad SEO spam (common words in nonsensical order) that otherwise scores\n# HIGH on the encyclopedic register (WikiText's diverse vocabulary) and pollutes a\n# whole quarter of the budget.  Vocabulary alone can't catch these; bigrams can.\n_bg = Counter()\nfor c in [x for g in reg_pos for x in g] + neg_texts:\n    w = WORD.findall(c.lower())\n    for j in range(len(w) - 1):\n        _bg[(w[j], w[j + 1])] += 1\nCOMMON_BG = frozenset(k for k, v in _bg.items() if v >= 2)\nprint(f\"common bigrams: {len(COMMON_BG)}\", file=sys.stderr)\ndef fluency(ws):\n    if len(ws) < 2: return 0.0\n    return sum(1 for j in range(len(ws) - 1) if (ws[j], ws[j + 1]) in COMMON_BG) / (len(ws) - 1)\nneg_c = np.zeros(K, dtype=np.float64)\nfor p in neg_texts:\n    fe = feats(p)\n    if fe: np.add.at(neg_c, np.array(fe), 1.0)\nNtot = neg_c.sum()\n\n# ---------- one NB log-odds weight vector per register ----------\nweights = np.zeros((NREG, K), dtype=np.float32)\na = 1.0\nfor k in range(NREG):\n    pos_c = np.zeros(K, dtype=np.float64)\n    for p in reg_pos[k]:\n        fe = feats(p)\n        if fe: np.add.at(pos_c, np.array(fe), 1.0)\n    Ptot = pos_c.sum()\n    w = (np.log(pos_c + a) - math.log(Ptot + a * K)) - \\\n        (np.log(neg_c + a) - math.log(Ntot + a * K))\n    c0 = math.log(Ntot + a * K) - math.log(Ptot + a * K)   # center: neutral seen feature ~ 0\n    w = w - c0\n    w[(pos_c + neg_c) < 3] = 0.0                            # trust only observed features\n    weights[k] = w.astype(np.float32)\n\n# ---------- score every pool doc against all four registers ----------\n_CACHE = \"/tmp/curate_scores4.npy\"\nif os.path.exists(_CACHE) and np.load(_CACHE).shape == (N, NREG):\n    S = np.load(_CACHE)\n    print(\"loaded cached scores\", file=sys.stderr)\nelse:\n    S = np.full((N, NREG), -1e9, dtype=np.float32)\n    for i in range(N):\n        fe = feats(texts[i])\n        if fe:\n            idx = np.array(fe)\n            n = len(fe)\n            for k in range(NREG):\n                S[i, k] = weights[k][idx].sum() / n\n        if i % 40000 == 0: print(f\"scored {i}\", file=sys.stderr)\n    np.save(_CACHE, S)\n\n# ---------- clean-English-prose gate ----------\nSTOP = set(\"the of and to a in is that it for on as with was are be by this at \"\n           \"from or an not but have has had he she they we you i his her their \"\n           \"which who will would can could been were said your all more one about \"\n           \"when what there if so no do does did than then them these those into \"\n           \"out up down over after also its our\".split())\ndef passes(t):\n    if len(t) < 300: return False\n    ws = WORD.findall(t.lower())\n    if len(ws) < 60: return False\n    if sum(c.isalpha() or c.isspace() for c in t) / len(t) < 0.70: return False\n    if sum(c.isdigit() for c in t) / len(t) > 0.15: return False\n    if sum(1 for w in ws if w in STOP) / len(ws) < 0.22: return False   # non-English/nav/code\n    if sum(1 for w in ws if w in COMMON) / len(ws) < 0.70: return False  # gibberish / word-salad spam\n    lines = [l for l in t.split(\"\\n\") if l.strip()]\n    if lines and Counter(lines).most_common(1)[0][1] / len(lines) > 0.5: return False\n    wc = Counter(ws)\n    if len(wc) / len(ws) < 0.30: return False\n    if wc.most_common(1)[0][1] / len(ws) > 0.10: return False\n    return True\nkeep = np.array([passes(t) for t in texts], dtype=bool)\nprint(f\"passed gates: {int(keep.sum())}\", file=sys.stderr)\n\n# ---------- per-register ranked candidate lists (gated) ----------\nkept = np.where(keep)[0]\nranked = []\nfor k in range(NREG):\n    o = kept[np.argsort(-S[kept, k])]\n    ranked.append(list(o))\n\n# ---------- round-robin fill -> balanced priority order, with dedup ----------\nseen, selection, ptr = set(), [], [0, 0, 0, 0]\nTARGET = 40000\nwhile len(selection) < TARGET:\n    progressed = False\n    for k in range(NREG):\n        while ptr[k] < len(ranked[k]):\n            i = ranked[k][ptr[k]]; ptr[k] += 1\n            sig = re.sub(r\"\\s+\", \" \", texts[i].lower()).strip()[:400]\n            h = zlib.crc32(sig.encode())\n            if h in seen: continue\n            seen.add(h); selection.append(int(ids[i])); progressed = True\n            break\n        if len(selection) >= TARGET: break\n    if not progressed: break\n\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids -> {OUT}\", file=sys.stderr)\n", "structuredPatch": [{"oldStart": 143, "oldLines": 7, "newStart": 143, "newLines": 7, "lines": ["     if sum(c.isalpha() or c.isspace() for c in t) / len(t) < 0.70: return False", "     if sum(c.isdigit() for c in t) / len(t) > 0.15: return False", "     if sum(1 for w in ws if w in STOP) / len(ws) < 0.22: return False   # non-English/nav/code", "-    if sum(1 for w in ws if w in COMMON) / len(ws) < 0.70: return False  # gibberish / word-salad spam", "+    if fluency(ws) < 0.25: return False                                 # gibberish / spun word-salad spam", "     lines = [l for l in t.split(\"\\n\") if l.strip()]", "     if lines and Counter(lines).most_common(1)[0][1] / len(lines) > 0.5: return False", "     wc = Counter(ws)"]}], "userModified": false, "replaceAll": false}

pid 3078

[stdout]
pid 3078
[metadata] {"noOutputExpected": false}

Command did not complete within its 120s timeout and was moved to the background (ID: bnpgwrvi6). Output is being written to: /tmp/claude-0/-workspace/056a17ab-c7c2-448d-bef2-fc697ffa5283/tasks/bnpgwrvi6.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bnpgwrvi6", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

common bigrams: 497899
passed gates: 126782
=== all 4 registers, top picks ===
--- REG0 wiki ---
  id 155591 :: scopy Cost in India | Shoulder Arthroscopy in India × Help Us With Your Details Count
  id 60321 :: izable Percy peptonizes supposedly dating in thailand reddit your consumptions just d
  id 45694 :: ures on forgiveness in marriage Anodic and rude jodi equaling her blood tricks or soc
  id 82521 :: More emblazoned than asian women dating in burnsville mn rubber stamps neatly? Shriek
--- REG1 prose ---
  id 132935 :: 236.14<|endoftext|>Shoulder Arthroscopy Cost in India | Shoulder Arthroscopy in India
  id 68330 :: amaican up a during break men 92% International dating site uk 98% Half-life carbon d
  id 109123 ::  this project<|endoftext|>Login database hoover Holoblastic Aldis swatter, its sharp 
  id 69381 :: , Duke of Saxony |Duke of Saxony Margrave of Meissen Lord of Friesland |Duke of Saxon
--- REG2 news ---
  id 23673 :: Lahore:In the prospects of stoned to death incident of a Khanewal Woman, Inspector Ge
  id 89943 :: JURIST] The Israeli military arrested seven Palestinian Cabinet ministers and some 20
  id 76528 :: pregnant clomid clomid,pregnant 2019-08-19 Pregnant clomidThewhoop, whoop, whoop whoo
  id 41186 :: Amarnath Yatra 2017 Amarnath Yatra 2017 News Jammu and Kashmir police said three peop
--- REG3 QA ---
  id 64048 :: <|endoftext|>Sometimes it's not enough for Publising features to be enabled. Deployme
  id 153492 ::  rights reserved<|endoftext|>Graphics.UI.Gtk.Layout.Expander Source Contents Index gt
  id 12485 :: Disable the server cache In order to disable the cache in the server and let the file
  id 42329 :: AndroChef Java Decompiler is Windows decompiler for class, jar, apk and dex files. It

[stdout]
common bigrams: 497899
passed gates: 126782
=== all 4 registers, top picks ===
--- REG0 wiki ---
  id 155591 :: scopy Cost in India | Shoulder Arthroscopy in India × Help Us With Your Details Count
  id 60321 :: izable Percy peptonizes supposedly dating in thailand reddit your consumptions just d
  id 45694 :: ures on forgiveness in marriage Anodic and rude jodi equaling her blood tricks or soc
  id 82521 :: More emblazoned than asian women dating in burnsville mn rubber stamps neatly? Shriek
--- REG1 prose ---
  id 132935 :: 236.14<|endoftext|>Shoulder Arthroscopy Cost in India | Shoulder Arthroscopy in India
  id 68330 :: amaican up a during break men 92% International dating site uk 98% Half-life carbon d
  id 109123 ::  this project<|endoftext|>Login database hoover Holoblastic Aldis swatter, its sharp 
  id 69381 :: , Duke of Saxony |Duke of Saxony Margrave of Meissen Lord of Friesland |Duke of Saxon
--- REG2 news ---
  id 23673 :: Lahore:In the prospects of stoned to death incident of a Khanewal Woman, Inspector Ge
  id 89943 :: JURIST] The Israeli military arrested seven Palestinian Cabinet ministers and some 20
  id 76528 :: pregnant clomid clomid,pregnant 2019-08-19 Pregnant clomidThewhoop, whoop, whoop whoo
  id 41186 :: Amarnath Yatra 2017 Amarnath Yatra 2017 News Jammu and Kashmir police said three peop
--- REG3 QA ---
  id 64048 :: <|endoftext|>Sometimes it's not enough for Publising features to be enabled. Deployme
  id 153492 ::  rights reserved<|endoftext|>Graphics.UI.Gtk.Layout.Expander Source Contents Index gt
  id 12485 :: Disable the server cache In order to disable the cache in the server and let the file
  id 42329 :: AndroChef Java Decompiler is Windows decompiler for class, jar, apk and dex files. It
[metadata] {"noOutputExpected": false}

DONE
loaded cached scores
passed gates: 126782
wrote 40000 ids -> /workspace/submission/selection.json

[stdout]
DONE
loaded cached scores
passed gates: 126782
wrote 40000 ids -> /workspace/submission/selection.json
[metadata] {"noOutputExpected": false}

REG0 wiki top picks NOW:
  id 155591 :: scopy Cost in India | Shoulder Arthroscopy in India × Help Us With Your Details 
  id 33875 :: The civilian and military postal routes in the Ottoman State were laid out in th
  id 106746 :: <|endoftext|>Lathal Bine was a Peliar Zel native, a resident of Beta moon, durin
  id 40208 :: Swedish actor Joel Kinnaman began his acting career in 2002, initially appearing
  id 22757 :: Robert A. Pejo Robert Adrian Pejo, Geza Csemer Helmut Grasser, László Kántor, An
gibberish 60321 still in selection? False
index of 60321: NOT PRESENT

[stdout]
REG0 wiki top picks NOW:
  id 155591 :: scopy Cost in India | Shoulder Arthroscopy in India × Help Us With Your Details 
  id 33875 :: The civilian and military postal routes in the Ottoman State were laid out in th
  id 106746 :: <|endoftext|>Lathal Bine was a Peliar Zel native, a resident of Beta moon, durin
  id 40208 :: Swedish actor Joel Kinnaman began his acting career in 2002, initially appearing
  id 22757 :: Robert A. Pejo Robert Adrian Pejo, Geza Csemer Helmut Grasser, László Kántor, An
gibberish 60321 still in selection? False
index of 60321: NOT PRESENT
[metadata] {"noOutputExpected": false}

eval3 pid 3438
3196

[stdout]
eval3 pid 3438
3196
[metadata] {"noOutputExpected": false}

Command did not complete within its 120s timeout and was moved to the background (ID: bghzgsgno). Output is being written to: /tmp/claude-0/-workspace/056a17ab-c7c2-448d-bef2-fc697ffa5283/tasks/bghzgsgno.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bghzgsgno", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

HELDOUT_PPL 322.004  loss 5.7746  train_s 51  /tmp/dev_train.npy

[stdout]
HELDOUT_PPL 322.004  loss 5.7746  train_s 51  /tmp/dev_train.npy
[metadata] {"noOutputExpected": false}

91:def fluency(ws):
142:    if len(ws) < 80: return False
146:    if fluency(ws) < 0.32: return False                                 # gibberish / spun word-salad spam
pid 3717

[stdout]
91:def fluency(ws):
142:    if len(ws) < 80: return False
146:    if fluency(ws) < 0.32: return False                                 # gibberish / spun word-salad spam
pid 3717
[metadata] {"noOutputExpected": false}

File created successfully at: /workspace/claim.md (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/claim.md", "content": "# Claim: register-balanced, fluency-gated curation beats quantity\n\n## Hypothesis\nUnder a fixed 12M-token training budget drawn from a 200M-token raw web pool, the\nheld-out perplexity of the frozen 30M-GPT on a **broad, equal-parts** high-quality\nEnglish target (Wikipedia / general web prose / news / technical Q&A) is minimized\nnot by maximizing tokens but by two composable choices:\n\n1. **Clean-prose + fluency filtering** — keep only well-formed English prose\n   (length, alphabetic ratio, function-word ratio, low repetition) and reject\n   *fluency-poor* text (nav/keyword spam, non-English, and \"spun\" word-salad SEO\n   spam) using a word-**bigram** attestation score.\n2. **Register balancing** — split the 12M budget ~evenly across the four target\n   registers via four per-register \"target-vs-pool\" classifiers and a round-robin\n   fill, rather than letting the single most abundant register (news) dominate.\n\nI predict this selection reaches held-out perplexity well below a random-selection\nbaseline of equal token count.\n\n## Mechanism / prediction (observable *other* than the final score)\nThe classifier assigns each doc a per-token log-odds of belonging to each target\nregister; the gates measure prose-fluency. Two concrete, checkable predictions that\ndo **not** reference the final PPL number:\n\n- **Composition shift.** The selected corpus that fills the budget has a\n  dramatically higher mean bigram-fluency and function-word ratio than a random\n  draw of the pool, and its documents are ~evenly split across the four registers\n  instead of ~news-dominated. (Verifiable by inspecting the packed docs.)\n- **Ablation ordering of dev loss.** Turning knobs one at a time moves held-out\n  loss monotonically: random ⟶ single-score classifier ⟶ +register-balance ⟶\n  +fluency-gate should each *lower* dev perplexity, and removing the fluency gate\n  should specifically re-pollute the *encyclopedic* register's top picks with\n  rare-vocabulary word-salad spam (because WikiText's diverse vocabulary makes that\n  register maximally vulnerable to it).\n\nMeasured dev perplexities (multi_dev.npy, frozen recipe, seed 1337) confirmed the\nordering while curating:\n\n| selection (equal 12M-token budget) | dev PPL |\n|---|---|\n| random pool sample | 438.5 |\n| single \"target-vs-pool\" classifier, top-scored | 343.7 |\n| + register-balanced round-robin (4 registers) | 326.9 |\n| + bigram-fluency gate (final `curate.py`) | 322.0 |\n\n## Falsification\nThe hypothesis is falsified if any of these hold on the frozen recipe:\n- A random selection of equal token count reaches held-out PPL ≤ the curated\n  selection (i.e., curation gives no lift over do-nothing).\n- Register-balancing does **not** reduce PPL versus taking the top of a single\n  target-vs-pool classifier (i.e., matching the equal-parts composition is\n  irrelevant).\n- Removing the fluency gate does **not** raise PPL and does **not** re-introduce\n  word-salad spam into the encyclopedic register's top picks.\nAny of these would show the mechanism (clean, fluent, register-balanced tokens) is\nnot what drives the gain.\n\n## Transfer\nThe criterion is a stated, data-driven recipe, not a hand-picked id list, so it\ntransfers:\n- **Budget.** Because ids are emitted in priority order, the same file trains any\n  budget; doubling to 24M tokens simply consumes deeper into each register's ranked\n  list. The relative ordering (curated < random) should hold at other budgets.\n- **Target domain.** For a different broad HQ target, re-deriving the per-register\n  positives from a small sample of that target and re-running `curate.py` unchanged\n  should again beat random — the machinery (register classifiers + prose/fluency\n  gates) is domain-agnostic; only the disclosed positive sample changes.\n- **Pool.** Applied to a different raw web pool, the gates (fluency, function-word,\n  repetition) reject the same junk classes, so the method should generalize without\n  retuning thresholds.\n", "structuredPatch": [], "originalFile": null, "userModified": false}

DONE
passed gates: 122129
wrote 40000 ids -> /workspace/submission/selection.json

[stdout]
DONE
passed gates: 122129
wrote 40000 ids -> /workspace/submission/selection.json
[metadata] {"noOutputExpected": false}

eval4 pid 4007
2866

[stdout]
eval4 pid 4007
2866
[metadata] {"noOutputExpected": false}

Command did not complete within its 120s timeout and was moved to the background (ID: be947oimb). Output is being written to: /tmp/claude-0/-workspace/056a17ab-c7c2-448d-bef2-fc697ffa5283/tasks/be947oimb.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "be947oimb", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

HELDOUT_PPL 325.719  loss 5.7860  train_s 51  /tmp/dev_train.npy

[stdout]
HELDOUT_PPL 325.719  loss 5.7860  train_s 51  /tmp/dev_train.npy
[metadata] {"noOutputExpected": false}

142:    if len(ws) < 60: return False
146:    if fluency(ws) < 0.25: return False                                 # gibberish / spun word-salad spam
restored best selection
ids 40000 unique 40000 min 4 max 181988

[stdout]
142:    if len(ws) < 60: return False
146:    if fluency(ws) < 0.25: return False                                 # gibberish / spun word-salad spam
restored best selection
ids 40000 unique 40000 min 4 max 181988
[metadata] {"noOutputExpected": false}

pid 4291
2676

[stdout]
pid 4291
2676
[metadata] {"noOutputExpected": false}

Command did not complete within its 120s timeout and was moved to the background (ID: bj9i2sm35). Output is being written to: /tmp/claude-0/-workspace/056a17ab-c7c2-448d-bef2-fc697ffa5283/tasks/bj9i2sm35.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bj9i2sm35", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

(Bash completed with no output)

[metadata] {"noOutputExpected": false}

fresh len 40000 best len 40000 IDENTICAL: True

[stdout]
fresh len 40000 best len 40000 IDENTICAL: True
[metadata] {"noOutputExpected": false}

quarters saved 250000
packed 12000000 tokens from 40000 ids -> /tmp/dev_train.npy
diag pid 4748

[stdout]
quarters saved 250000
packed 12000000 tokens from 40000 ids -> /tmp/dev_train.npy
diag pid 4748
[metadata] {"noOutputExpected": false}