gpt-5.6-sol

{
  "input": "const r = await tools.exec_command({cmd:\"python3 /workspace/work/pack_cache.py /workspace/work/combo_b30.json /workspace/work/train_combo_b30.npy && python3 /workspace/work/train_diag.py --train_npy /workspace/work/train_combo_b30.npy --heldout_npy /workspace/data/multi_dev.npy --out_json /workspace/work/combo_b30_eval.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));"
}

{
  "input": "const r = await tools.write_stdin({session_id:89729,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "cell_id": "129",
  "yield_time_ms": 20000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.write_stdin({session_id:89729,chars:\"\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:89729,chars:\"\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python3 /workspace/work/pack_cache.py /workspace/work/pen65.json /workspace/work/train_pen65.npy && python3 /workspace/work/train_diag.py --train_npy /workspace/work/train_pen65.npy --heldout_npy /workspace/data/multi_dev.npy --out_json /workspace/work/pen65_eval.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));"
}

{
  "input": "const r = await tools.exec_command({cmd:\"cat /workspace/.timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:39045,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "cell_id": "134",
  "yield_time_ms": 20000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.write_stdin({session_id:39045,chars:\"\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:39045,chars:\"\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python3 /workspace/work/pack_cache.py /workspace/work/pen135.json /workspace/work/train_pen135.npy && python3 /workspace/work/train_diag.py --train_npy /workspace/work/train_pen135.npy --heldout_npy /workspace/data/multi_dev.npy --out_json /workspace/work/pen135_eval.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /workspace/submission/curate.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Target-density curation with explicit four-domain token balancing.\\n+\\n+Criterion\\n+---------\\n+Treat the four equal quarters of ``multi_dev.npy`` as disclosed, unlabeled\\n+examples of the desired registers.  Estimate smoothed GPT-2 unigram and hashed\\n+bigram density ratios between that target and the whole web pool.  A document's\\n+quality score is its mean clipped density ratio minus deterministic crawl-noise\\n+penalties.  For each target quarter, rank documents by quality plus 0.85 times\\n+quarter-specific token evidence, then interleave the four rankings until each\\n+has supplied 3M tokens.  Append the remaining ids in score order so the output\\n+always provides ample coverage.\\n+\\n+This is intentionally a corpus-level rule: no pool id, URL, or hand label is\\n+embedded here.  Running the script recreates ``selection.json`` from the two\\n+provided data files.  It uses a temporary 16-bit token cache to keep memory use\\n+reasonable; on the benchmark host it takes roughly ten minutes.\\n+\\\"\\\"\\\"\\n+\\n+import json\\n+import math\\n+import re\\n+import tempfile\\n+import time\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+from transformers import AutoTokenizer\\n+\\n+POOL = Path(\\\"/workspace/data/pool.jsonl\\\")\\n+DEV = Path(\\\"/workspace/data/multi_dev.npy\\\")\\n+OUTPUT = Path(\\\"/workspace/submission/selection.json\\\")\\n+VOCAB = 50_257\\n+HASH_MASK = (1 << 20) - 1\\n+DOMAIN_QUOTA = 3_000_000\\n+\\n+\\n+def bigram_hash(a, b):\\n+    return (a.astype(np.uint64) * 1_000_003 + b.astype(np.uint64)) & HASH_MASK\\n+\\n+\\n+def crawl_penalty(q, lengths):\\n+    words, lines, alpha, printable, eos, questions, code, urls, bars, unique, punct, maxline = q.T\\n+    p = np.zeros(len(lengths), dtype=np.float64)\\n+    p += np.maximum(0, 0.60 - alpha) * 4\\n+    p += np.maximum(0, 0.985 - printable) * 8\\n+    p += np.maximum(0, 0.20 - unique) * 3\\n+    p += np.maximum(0, bars / np.maximum(words, 1) - 0.015) * 8\\n+    p += np.maximum(0, urls / np.maximum(words, 1) - 0.015) * 5\\n+    p += (words < 80) * 0.8 + (words < 30) * 1.5\\n+    p += np.maximum(0, np.log1p(lengths / 6000)) * 0.10\\n+    p += ((lines >= 5) & (punct < 0.12)) * 0.3\\n+    return p\\n+\\n+\\n+def interleave(rankings, lengths):\\n+    \\\"\\\"\\\"Draw from the token-furthest-behind domain, de-duplicating ids.\\\"\\\"\\\"\\n+    pos = [0] * 4\\n+    used = [0] * 4\\n+    out = []\\n+    seen = set()\\n+    while any(used[k] < DOMAIN_QUOTA and pos[k] < len(rankings[k]) for k in range(4)):\\n+        active = [k for k in range(4)\\n+                  if used[k] < DOMAIN_QUOTA and pos[k] < len(rankings[k])]\\n+        k = min(active, key=lambda j: used[j] / DOMAIN_QUOTA)\\n+        while pos[k] < len(rankings[k]) and int(rankings[k][pos[k]]) in seen:\\n+            pos[k] += 1\\n+        if pos[k] == len(rankings[k]):\\n+            continue\\n+        doc_id = int(rankings[k][pos[k]])\\n+        pos[k] += 1\\n+        out.append(doc_id)\\n+        seen.add(doc_id)\\n+        used[k] += int(lengths[doc_id]) + 1  # packer adds one EOS per document\\n+    return out, seen, used\\n+\\n+\\n+def tokenize_pool(rows, token_path):\\n+    tokenizer = AutoTokenizer.from_pretrained(\\\"gpt2\\\")\\n+    backend = tokenizer.backend_tokenizer\\n+    n = len(rows)\\n+    offsets = np.zeros(n + 1, dtype=np.int64)\\n+    quality = np.zeros((n, 12), dtype=np.float32)\\n+    total = 0\\n+    started = time.time()\\n+    with token_path.open(\\\"wb\\\") as token_file:\\n+        for base in range(0, n, 256):\\n+            batch = rows[base:base + 256]\\n+            encoded = backend.encode_batch([r[\\\"text\\\"] for r in batch],\\n+                                           add_special_tokens=False)\\n+            batch_ids = []\\n+            for j, (row, enc) in enumerate(zip(batch, encoded)):\\n+                i = base + j\\n+                s = row[\\\"text\\\"]\\n+                ids = enc.ids\\n+                batch_ids.extend(ids)\\n+                total += len(ids)\\n+                offsets[i + 1] = total\\n+                words = re.findall(r\\\"\\\\b\\\\w+\\\\b\\\", s)\\n+                lines = [x.strip() for x in s.splitlines() if x.strip()]\\n+                alpha = sum(c.isalpha() for c in s)\\n+                printable = sum(c.isprintable() or c in \\\"\\\\n\\\\t\\\" for c in s)\\n+                code = len(re.findall(\\n+                    r\\\"</?(?:p|pre|code|blockquote)\\\\b|\\\"\\n+                    r\\\"\\\\b(?:def|class|function|SELECT|import)\\\\b|[{};]\\\", s, re.I))\\n+                quality[i] = (\\n+                    len(words), len(lines), alpha / max(1, len(s)),\\n+                    printable / max(1, len(s)), s.count(\\\"<|endoftext|>\\\"),\\n+                    s.count(\\\"?\\\"), code, s.count(\\\"http\\\"), s.count(\\\"|\\\"),\\n+                    len(set(w.lower() for w in words)) / max(1, len(words)),\\n+                    sum(x[-1:] in \\\".!?\\\\\\\"'\\\" for x in lines) / max(1, len(lines)),\\n+                    len(max(lines, key=len, default=\\\"\\\")),\\n+                )\\n+            np.asarray(batch_ids, dtype=np.uint16).tofile(token_file)\\n+            if base and base % 20_480 == 0:\\n+                print(f\\\"tokenized {base}/{n} documents in {time.time()-started:.0f}s\\\",\\n+                      flush=True)\\n+    return offsets, quality\\n+\\n+\\n+def main():\\n+    rows = [json.loads(line) for line in POOL.open()]\\n+    if not all(r[\\\"id\\\"] == i for i, r in enumerate(rows)):\\n+        raise ValueError(\\\"pool ids must be the consecutive range 0..N-1\\\")\\n+\\n+    with tempfile.TemporaryDirectory(prefix=\\\"curate-\\\") as temp:\\n+        token_path = Path(temp) / \\\"tokens.uint16\\\"\\n+        offsets, quality = tokenize_pool(rows, token_path)\\n+        del rows\\n+        lengths = np.diff(offsets)\\n+        pool = np.memmap(token_path, dtype=np.uint16, mode=\\\"r\\\")\\n+        dev = np.load(DEV)\\n+        if len(dev) != 1_000_000:\\n+            raise ValueError(\\\"expected the disclosed 1M-token, four-quarter dev set\\\")\\n+\\n+        # Smoothed unigram target-vs-pool density ratio.\\n+        dcount = np.stack([\\n+            np.bincount(dev[k * 250_000:(k + 1) * 250_000], minlength=VOCAB)\\n+            for k in range(4)\\n+        ])\\n+        pcount = np.bincount(pool, minlength=VOCAB)\\n+        alpha = 10.0\\n+        target_lp = np.log((dcount.sum(0) + alpha) /\\n+                           (len(dev) + alpha * VOCAB))\\n+        ratio = len(pool) / len(dev)\\n+        pool_lp = np.log((pcount + alpha * ratio) /\\n+                         (len(pool) + alpha * ratio * VOCAB))\\n+        unigram_density_weight = np.clip(target_lp - pool_lp, -2.5, 2.5)\\n+        domain_lp = np.log((dcount + alpha) / (250_000 + alpha * VOCAB))\\n+        unigram_domain_weight = domain_lp - domain_lp.mean(0, keepdims=True)\\n+\\n+        # Hashed bigrams add sequence/markup style without a huge 50k^2 table.\\n+        d_hash_count = np.stack([\\n+            np.bincount(\\n+                bigram_hash(dev[k*250_000:(k+1)*250_000-1],\\n+                            dev[k*250_000+1:(k+1)*250_000]).astype(np.int64),\\n+                minlength=HASH_MASK + 1)\\n+            for k in range(4)\\n+        ])\\n+        p_hash_count = np.zeros(HASH_MASK + 1, dtype=np.int64)\\n+        for start in range(0, len(pool) - 1, 4_000_000):\\n+            x = np.asarray(pool[start:min(len(pool), start + 4_000_001)])\\n+            p_hash_count += np.bincount(\\n+                bigram_hash(x[:-1], x[1:]).astype(np.int64),\\n+                minlength=HASH_MASK + 1)\\n+        hash_alpha = 1.0\\n+        target_hash_lp = np.log((d_hash_count.sum(0) + hash_alpha) /\\n+                                (len(dev) + hash_alpha * (HASH_MASK + 1)))\\n+        pool_hash_lp = np.log((p_hash_count + hash_alpha * ratio) /\\n+                              (len(pool) + hash_alpha * ratio * (HASH_MASK + 1)))\\n+        hash_density_weight = np.clip(target_hash_lp - pool_hash_lp, -2.5, 2.5)\\n+        hash_domain_lp = np.log(\\n+            (d_hash_count + hash_alpha) /\\n+            (250_000 + hash_alpha * (HASH_MASK + 1)))\\n+        hash_domain_weight = hash_domain_lp - hash_domain_lp.mean(0, keepdims=True)\\n+\\n+        density = np.zeros(len(lengths), dtype=np.float32)\\n+        domain = np.zeros((len(lengths), 4), dtype=np.float32)\\n+        for i, (lo, hi) in enumerate(zip(offsets[:-1], offsets[1:])):\\n+            x = pool[lo:hi]\\n+            if not len(x):\\n+                continue\\n+            density[i] = unigram_density_weight[x].mean()\\n+            domain[i] = unigram_domain_weight[:, x].mean(1)\\n+            if len(x) > 1:\\n+                h = bigram_hash(x[:-1], x[1:]).astype(np.int64)\\n+                density[i] += 0.45 * hash_density_weight[h].mean()\\n+                domain[i] += 0.45 * hash_domain_weight[:, h].mean(1)\\n+\\n+        penalty = crawl_penalty(quality, lengths)\\n+        base = density.astype(np.float64) - penalty\\n+        valid = (lengths >= 64) & (lengths <= 20_000) & (quality[:, 2] >= 0.45)\\n+        candidates = np.where(valid)[0]\\n+        rankings = []\\n+        for k in range(4):\\n+            score = base + 0.85 * domain[:, k]\\n+            rankings.append(candidates[np.argsort(-score[candidates], kind=\\\"stable\\\")])\\n+\\n+        chosen, seen, used = interleave(rankings, lengths)\\n+        tail_score = base + 0.85 * domain.max(1)\\n+        tail = np.argsort(-tail_score, kind=\\\"stable\\\")\\n+        chosen.extend(int(i) for i in tail if int(i) not in seen)\\n+\\n+    OUTPUT.parent.mkdir(parents=True, exist_ok=True)\\n+    with OUTPUT.open(\\\"w\\\") as f:\\n+        json.dump(chosen, f)\\n+    print(f\\\"wrote {len(chosen)} unique ids to {OUTPUT}; domain tokens {used}\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+    main()\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"sed -n '85,175p' /workspace/work/make_candidates.py | nl -ba && python3 -m py_compile /workspace/submission/curate.py\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":12000});\ntext(r.output);"
}

{
  "input": "const r = await tools.write_stdin({session_id:21543,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "cell_id": "140",
  "yield_time_ms": 20000,
  "max_tokens": 5000
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/work/make_variants.py\\n@@\\n-for beta in (.55, .70, .85, .95):\\n+for beta in (.55, .70, .80, .85, .88, .95):\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/workspace/work python3 /workspace/work/make_variants.py | rg 'mid(80|88)'\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python3 /workspace/work/pack_cache.py /workspace/work/mid80_w30.json /workspace/work/train_mid80.npy && python3 /workspace/work/train_diag.py --train_npy /workspace/work/train_mid80.npy --heldout_npy /workspace/data/multi_dev.npy --out_json /workspace/work/mid80_eval.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+Under a severe token budget, a small model benefits more from clean documents whose local token statistics match the intended evaluation registers than from a random sample of the crawl. A mixture explicitly balanced across encyclopedia, polished web prose, news, and technical Q&A should beat a globally ranked mixture that lets abundant news-like prose dominate.\\n+\\n+# Mechanism\\n+\\n+The selection rule estimates clipped, smoothed GPT-2 unigram and hashed-bigram density ratios between the disclosed target and the full pool. Bigram evidence captures prose/markup style; unigram evidence captures register and vocabulary. It subtracts penalties for short, non-alphabetic, unprintable, repetitive, URL-heavy, table-heavy, or whole-site records. Four domain-conditioned rankings are interleaved by consumed tokens.\\n+\\n+An observable prediction independent of final perplexity is that the consumed prefix will contain approximately 3M tokens from each conditioned ranking and will have substantially higher held-out target-density score and lower crawl-noise penalty than an equal-sized random prefix. It should also retain visibly distinct factual, article/news, and code/Q&A records instead of collapsing onto the most common register.\\n+\\n+# Falsification\\n+\\n+The hypothesis is falsified if the selected prefix does not improve the preregistered density/noise statistics over random data, if removing domain balancing improves all four per-register validation losses, or if gains disappear on a disjoint target sample while the disclosed dev sample improves. A large reversal on the hidden sample would specifically falsify the assumption that smoothed token statistics capture source-level register rather than sample-specific topics.\\n+\\n+# Transfer\\n+\\n+The method transfers to another raw pool when a modest representative calibration corpus and the intended mixture weights are available: retokenize, re-estimate target-versus-pool density ratios, and rerank. The weights and quarter boundaries are not expected to transfer to a differently weighted, multilingual, conversational, or highly specialized target; those require new calibration slices and quotas.\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);"
}

{
  "input": "const r = await tools.write_stdin({session_id:3240,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "cell_id": "145",
  "yield_time_ms": 20000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.write_stdin({session_id:3240,chars:\"\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python3 /workspace/work/pack_cache.py /workspace/work/mid88_w30.json /workspace/work/train_mid88.npy && python3 /workspace/work/train_diag.py --train_npy /workspace/work/train_mid88.npy --heldout_npy /workspace/data/multi_dev.npy --out_json /workspace/work/mid88_eval.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));"
}

{
  "input": "const r = await tools.write_stdin({session_id:88538,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}