gpt-5.6-sol

{
  "input": "const r = await tools.write_stdin({session_id:70095,chars:\"\",yield_time_ms:10000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:70095,chars:\"\",yield_time_ms:10000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /tmp/make_sel.py\\n@@\\n-bw=float(sys.argv[4]) if len(sys.argv)>4 else 0.0\\n+bw=np.asarray(list(map(float,sys.argv[4].split(',')))) if len(sys.argv)>4 and ',' in sys.argv[4] else float(sys.argv[4]) if len(sys.argv)>4 else 0.0\\n@@\\n-if bw or cw:\\n+if np.any(bw) or cw:\\n@@\\n-scores=base[:,None]+w*aff+bw*baff+cw*np.asarray(covz)[:,None] if np.ndim(covz) else base[:,None]+w*aff\\n+scores=base[:,None]+w*aff+bw*baff+cw*np.asarray(covz)[:,None] if np.ndim(covz) else base[:,None]+w*aff\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.exec_command({\n  cmd: \"python3 /tmp/make_sel.py 5.0 /tmp/q11.json 1,1,1,1 0,0,2,2 0 && python3 /workspace/pack_selection.py /tmp/q11.json /tmp/q11.npy && PYTHONPATH=/workspace python3 /tmp/train_diag.py --train_npy /tmp/q11.npy --heldout_npy /workspace/data/multi_dev.npy --out_json /tmp/q11.out --max_iters 3000 --seed 1337\",\n  workdir: \"/workspace\",\n  yield_time_ms: 30000,\n  max_output_tokens: 3000\n});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "103",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "const r = await tools.write_stdin({session_id:34319,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

{
  "cell_id": "104",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "const r = await tools.write_stdin({session_id:34319,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

{
  "cell_id": "105",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "const r = await tools.write_stdin({session_id:34319,chars:\"\",yield_time_ms:10000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:34319,chars:\"\",yield_time_ms:10000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.exec_command({\n  cmd: \"python3 /tmp/make_sel.py 5.0 /tmp/q12.json 1,1,1,1 0,0,4,4 0 && python3 /workspace/pack_selection.py /tmp/q12.json /tmp/q12.npy && PYTHONPATH=/workspace python3 /tmp/train_diag.py --train_npy /tmp/q12.npy --heldout_npy /workspace/data/multi_dev.npy --out_json /tmp/q12.out --max_iters 3000 --seed 1337\",\n  workdir: \"/workspace\",\n  yield_time_ms: 30000,\n  max_output_tokens: 3000\n});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "108",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "const r = await tools.write_stdin({session_id:72470,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

{
  "cell_id": "109",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "const r = await tools.write_stdin({session_id:72470,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

{
  "cell_id": "110",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "const r = await tools.write_stdin({session_id:72470,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

{
  "cell_id": "111",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "const r = await tools.exec_command({cmd:\"sed -n '1,260p' /tmp/score_pool.py && sed -n '1,220p' /tmp/make_sel.py && cat /workspace/.timer/remaining_secs\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:20000});text(r.output);\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /workspace/submission/curate.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Curate 16M ordered tokens using target-relative LM scores and quality priors.\\n+\\n+The disclosed dev array consists of four contiguous, equal-sized registers.  This\\n+script estimates a smoothed unigram Moore-Lewis score (target register versus\\n+the pool) for every document.  Hashed target-bigram affinity is additionally\\n+used for news and technical Q&A, where it helped in dev ablations.  Four ranked\\n+queues are interleaved by token count, with quality filters and exact-content\\n+deduplication.  No pool IDs, source labels, or hand annotations are embedded.\\n+\\\"\\\"\\\"\\n+import hashlib\\n+import json\\n+import math\\n+import os\\n+import re\\n+\\n+import numpy as np\\n+from transformers import AutoTokenizer\\n+\\n+POOL = \\\"/workspace/data/pool.jsonl\\\"\\n+DEV = \\\"/workspace/data/multi_dev.npy\\\"\\n+OUT = \\\"/workspace/submission/selection.json\\\"\\n+VOCAB = 50_257\\n+DOMAINS = 4\\n+PER_DOMAIN_OUTPUT = 4_000_000  # 16M total gives ample margin over 12M.\\n+COMMON = {\\n+    \\\"the\\\", \\\"of\\\", \\\"and\\\", \\\"to\\\", \\\"in\\\", \\\"a\\\", \\\"is\\\", \\\"that\\\", \\\"for\\\", \\\"it\\\",\\n+    \\\"on\\\", \\\"with\\\", \\\"as\\\", \\\"was\\\", \\\"are\\\", \\\"by\\\", \\\"this\\\", \\\"be\\\", \\\"from\\\", \\\"or\\\",\\n+    \\\"an\\\", \\\"at\\\", \\\"which\\\", \\\"but\\\", \\\"not\\\", \\\"have\\\", \\\"has\\\", \\\"had\\\", \\\"were\\\",\\n+    \\\"their\\\", \\\"they\\\",\\n+}\\n+BOILER = (\\n+    \\\"cookie policy\\\", \\\"privacy policy\\\", \\\"terms of service\\\", \\\"all rights reserved\\\",\\n+    \\\"skip to content\\\", \\\"toggle navigation\\\", \\\"sign up\\\", \\\"log in\\\", \\\"shopping cart\\\",\\n+    \\\"javascript is required\\\", \\\"enable javascript\\\", \\\"click here\\\", \\\"contact us\\\",\\n+    \\\"related posts\\\", \\\"share this\\\", \\\"subscribe to\\\", \\\"free shipping\\\", \\\"add to cart\\\",\\n+)\\n+SPAM = (\\n+    \\\"payday loan\\\", \\\"casino\\\", \\\"viagra\\\", \\\"coupon code\\\", \\\"promo code\\\", \\\"buy now\\\",\\n+    \\\"best price\\\", \\\"order now\\\", \\\"weight loss\\\", \\\"essay writing service\\\",\\n+)\\n+\\n+\\n+def main():\\n+    tokenizer = AutoTokenizer.from_pretrained(\\\"gpt2\\\")\\n+    tokenizer.model_max_length = 10**9\\n+    dev = np.load(DEV).astype(np.int64)\\n+    quarter = len(dev) // DOMAINS\\n+    dcount = np.stack([\\n+        np.bincount(dev[d * quarter:(d + 1) * quarter], minlength=VOCAB)\\n+        for d in range(DOMAINS)\\n+    ])\\n+\\n+    texts, ids, basic, digests = [], [], [], []\\n+    with open(POOL) as f:\\n+        for line in f:\\n+            row = json.loads(line)\\n+            s = row[\\\"text\\\"]\\n+            lo = s.lower()\\n+            lines = s.splitlines()\\n+            chars = max(1, len(s))\\n+            words = re.findall(r\\\"[A-Za-z]+\\\", s)\\n+            nw = max(1, len(words))\\n+            normlines = [\\n+                re.sub(r\\\"\\\\s+\\\", \\\" \\\", x.strip().lower())\\n+                for x in lines if len(x.strip()) >= 20\\n+            ]\\n+            texts.append(s)\\n+            ids.append(row[\\\"id\\\"])\\n+            digests.append(hashlib.blake2b(\\n+                re.sub(r\\\"\\\\s+\\\", \\\" \\\", lo.strip()).encode(), digest_size=12\\n+            ).digest())\\n+            basic.append((\\n+                len(s),\\n+                sum(c.isalpha() for c in s) / chars,\\n+                sum(ord(c) < 128 for c in s) / chars,\\n+                sum(w.lower() in COMMON for w in words) / nw,\\n+                sum(s.count(x) for x in \\\".?!\\\") / nw,\\n+                sum(lo.count(x) for x in BOILER),\\n+                sum(lo.count(x) for x in SPAM),\\n+                len(lines),\\n+                sum(len(x.strip()) < 35 for x in lines) / max(1, len(lines)),\\n+                1 - len(set(normlines)) / max(1, len(normlines)),\\n+                lo.count(\\\"<|endoftext|>\\\"),\\n+            ))\\n+    basic = np.asarray(basic, dtype=np.float32)\\n+    ids = np.asarray(ids)\\n+    print(f\\\"loaded {len(texts)} documents\\\", flush=True)\\n+\\n+    # A deterministic 1/12 sample supplies the raw-pool reference LM.\\n+    bg = np.zeros(VOCAB, dtype=np.int64)\\n+    sample = texts[::12]\\n+    for j in range(0, len(sample), 512):\\n+        batch = tokenizer(sample[j:j + 512], add_special_tokens=False,\\n+                          return_attention_mask=False)[\\\"input_ids\\\"]\\n+        for z in batch:\\n+            bg += np.bincount(z, minlength=VOCAB)\\n+\\n+    smoothing = 8.0\\n+    lp_bg = np.log((bg + smoothing) / (bg.sum() + smoothing * VOCAB))\\n+    lp_dom = np.log((dcount + smoothing) /\\n+                    (dcount.sum(1, keepdims=True) + smoothing * VOCAB))\\n+    lp_mix = np.log((dcount.sum(0) + smoothing) /\\n+                    (dcount.sum() + smoothing * VOCAB))\\n+    token_log_ratio = lp_dom - lp_bg\\n+\\n+    # Collision-tolerant 22-bit bigram table (about 6% occupied/colliding).\\n+    bits = 22\\n+    mask = (1 << bits) - 1\\n+    bigram_count = np.zeros((DOMAINS, 1 << bits), dtype=np.uint32)\\n+    for d in range(DOMAINS):\\n+        x = dev[d * quarter:(d + 1) * quarter].astype(np.uint64)\\n+        h = ((x[:-1] * 1_000_003) ^ x[1:]) & mask\\n+        np.add.at(bigram_count[d], h, 1)\\n+    bigram_mix = bigram_count.sum(0)\\n+    bigram_rel_table = (\\n+        np.log(bigram_count + 0.20)\\n+        - np.log(bigram_mix[None, :] + 0.80)\\n+    ).astype(np.float32)\\n+\\n+    n = len(texts)\\n+    ntok = np.zeros(n, dtype=np.int32)\\n+    ce = np.zeros(n, dtype=np.float32)\\n+    rel = np.zeros((n, DOMAINS), dtype=np.float32)\\n+    bigram_rel = np.zeros((n, DOMAINS), dtype=np.float32)\\n+    diversity = np.zeros(n, dtype=np.float32)\\n+    for j in range(0, n, 256):\\n+        batch = tokenizer(texts[j:j + 256], add_special_tokens=False,\\n+                          return_attention_mask=False)[\\\"input_ids\\\"]\\n+        for k, z0 in enumerate(batch, j):\\n+            z = np.asarray(z0, dtype=np.int64)\\n+            ntok[k] = len(z) + 1  # pack_selection appends EOS.\\n+            if not len(z):\\n+                continue\\n+            ce[k] = lp_mix[z].mean()\\n+            rel[k] = token_log_ratio[:, z].mean(1)\\n+            zz = z[:4096]\\n+            diversity[k] = len(np.unique(zz)) / math.sqrt(len(zz))\\n+            if len(z) > 1:\\n+                zu = z.astype(np.uint64)\\n+                h = ((zu[:-1] * 1_000_003) ^ zu[1:]) & mask\\n+                bigram_rel[k] = bigram_rel_table[:, h].mean(1)\\n+        if j and j % 16384 == 0:\\n+            print(f\\\"scored {j}/{n}\\\", flush=True)\\n+\\n+    eligible = (\\n+        (ntok >= 300) & (ntok <= 10_000)\\n+        & (basic[:, 1] > .68) & (basic[:, 2] > .92)\\n+        & (basic[:, 3] > .10) & (basic[:, 4] > .012)\\n+        & (basic[:, 6] == 0) & (basic[:, 9] < .25)\\n+    )\\n+    # Transparent quality prior: English prose, sentences, lexical diversity,\\n+    # little navigation/template repetition, and no high-risk spam markers.\\n+    base = (\\n+        2 * np.clip((basic[:, 3] - .12) / .18, 0, 1)\\n+        + np.clip((basic[:, 1] - .65) / .15, 0, 1)\\n+        + .5 * np.clip((basic[:, 2] - .90) / .10, 0, 1)\\n+        + np.clip((basic[:, 4] - .015) / .05, 0, 1)\\n+        + .7 * np.clip((diversity - 5) / 12, 0, 1)\\n+        - .25 * basic[:, 5]\\n+        - 2 * np.maximum(0, basic[:, 8] - .65)\\n+        - 5 * basic[:, 9]\\n+        + .15 * np.clip(ce + 9, -2, 2)\\n+    )\\n+    shrink = np.sqrt(ntok[:, None] / (ntok[:, None] + 1000))\\n+    affinity = (\\n+        (rel - np.median(rel[eligible], 0)) / np.std(rel[eligible], 0)\\n+    ) * shrink\\n+    bigram_affinity = (\\n+        (bigram_rel - np.median(bigram_rel[eligible], 0))\\n+        / np.std(bigram_rel[eligible], 0)\\n+    ) * shrink\\n+    # Bigram ablation helped news and technical Q&A, but hurt the two prose\\n+    # queues because of target-specific Wikipedia punctuation/spacing.\\n+    weights = np.asarray([0.0, 0.0, 2.0, 2.0])\\n+    scores = base[:, None] + 5.0 * affinity + weights * bigram_affinity\\n+    scores[~eligible] = -99\\n+\\n+    queues = [np.argsort(-scores[:, d]).tolist() for d in range(DOMAINS)]\\n+    ptr = [0] * DOMAINS\\n+    totals = [0] * DOMAINS\\n+    order, used, seen = [], set(), set()\\n+    while min(totals) < PER_DOMAIN_OUTPUT:\\n+        d = min(range(DOMAINS), key=lambda x: totals[x])\\n+        while True:\\n+            i = queues[d][ptr[d]]\\n+            ptr[d] += 1\\n+            if i not in used and digests[i] not in seen:\\n+                break\\n+        used.add(i)\\n+        seen.add(digests[i])\\n+        order.append(int(ids[i]))\\n+        totals[d] += int(ntok[i])\\n+\\n+    os.makedirs(os.path.dirname(OUT), exist_ok=True)\\n+    with open(OUT, \\\"w\\\") as f:\\n+        json.dump(order, f)\\n+    print(f\\\"wrote {len(order)} unique ids; queue tokens={totals}; total={sum(totals)}\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+    main()\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+A small model trained on a token-balanced mixture of clean documents whose token distribution matches each disclosed target register will generalize better than one trained on globally high-scoring prose or random web text.\\n+\\n+# Mechanism\\n+\\n+Smoothed target-versus-pool unigram likelihood identifies register while prose, repetition, boilerplate, spam, and exact-duplicate controls preserve quality. Bigram affinity is used only where ablation supports it (news and technical Q&A). Independently of final perplexity, the selected prefix should contain about 3M tokens from each queue, have fewer repeated documents and navigation phrases than random data, and show higher held-out register-classification scores.\\n+\\n+# Falsification\\n+\\n+The hypothesis is falsified if a frozen-model run on the balanced selection does not beat a size-matched random selection, or if removing balance while retaining the same quality score does not worsen broad dev loss. It is also weakened if gains occur in only one target quarter while average loss rises.\\n+\\n+# Transfer\\n+\\n+The method transfers to a new corpus or target by replacing only the target sample used to estimate the smoothed n-gram distributions. The quality features, empirical-Bayes length shrinkage, deduplication, and token-balanced queue construction are source-agnostic; the register-specific bigram weights should be re-ablated.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python3 /workspace/submission/curate.py\",workdir:\"/workspace\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

{
  "cell_id": "114",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

{
  "input": "const r = await tools.write_stdin({session_id:44314,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}