gpt-5.6-sol

{
  "cell_id": "87",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:61840,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python pack_selection.py /tmp/profiles4/wiki10.json /tmp/train_wiki10.npy\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000});\ntext(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:99032,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});\ntext(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:53951,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "91",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/workspace python /tmp/train_diag.py --train_npy /tmp/train_wiki10.npy --heldout_npy data/multi_dev.npy --out_json /tmp/wiki10_diag.json --max_iters 3000 --seed 1337\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000});\ntext(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:58326,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "93",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

{
  "input": "const r = await tools.write_stdin({session_id:58326,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "94",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/submission/curate.py\\n@@\\n-    if args.profile == \\\"quality\\\":\\n+    if args.profile == \\\"quality\\\":\\n@@\\n-        # A slight web/news emphasis performed better than literal quarters while\\n-        # retaining substantial support for the two scarcer transfer domains.\\n-        shares = custom_shares or ([0.20, 0.34, 0.30, 0.16] if profile == \\\"balanced\\\" else [0.24, 0.38, 0.34, 0.04])\\n+        # Equal target-domain queues are the published setting.  Scheduling by\\n+        # estimated tokens prevents a run of short pages from monopolizing a queue.\\n+        shares = custom_shares or ([0.25, 0.25, 0.25, 0.25] if profile == \\\"balanced\\\" else [0.24, 0.38, 0.34, 0.04])\\n@@\\n-        # Character/4 estimates only schedule the queues.  We emit far beyond the\\n-        # budget, and below we tokenize the prefix to guarantee exact coverage.\\n+        # Character/3.75 estimates only schedule the queues.  The high-quality\\n+        # fallback tail makes the emitted list far longer than the exact budget.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"sed -n '200,255p' submission/curate.py\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/submission/curate.py\\n@@\\n     def make_order(profile, custom_shares=None):\\n-      if profile == \\\"quality\\\":\\n-        return [z[2] for z in allrank]\\n-      else:\\n-        # A slight web/news emphasis performed better than literal quarters while\\n-        # retaining substantial support for the two scarcer transfer domains.\\n-        shares = custom_shares or ([0.20, 0.34, 0.30, 0.16] if profile == \\\"balanced\\\" else [0.24, 0.38, 0.34, 0.04])\\n+        if profile == \\\"quality\\\":\\n+            return [z[2] for z in allrank]\\n+        else:\\n+            # Equal target-domain queues are the published setting. Scheduling by\\n+            # estimated tokens prevents short pages from monopolizing a queue.\\n+            shares = custom_shares or ([0.25, 0.25, 0.25, 0.25] if profile == \\\"balanced\\\" else [0.24, 0.38, 0.34, 0.04])\\n@@\\n-        # Character/4 estimates only schedule the queues.  We emit far beyond the\\n-        # budget, and below we tokenize the prefix to guarantee exact coverage.\\n+        # Character/3.75 estimates only schedule the queues. The high-quality\\n+        # fallback tail makes the emitted list far longer than the exact budget.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.update_plan({explanation:\"Corpus profiling and mixture evaluation are complete; the equal domain-queue candidate is the best dev result.\",plan:[\n {step:\"Profile corpus, token budget behavior, and dev target\",status:\"completed\"},\n {step:\"Develop reproducible quality/domain/dedup scoring and selection\",status:\"completed\"},\n {step:\"Evaluate candidate mixtures with frozen trainer\",status:\"completed\"},\n {step:\"Finalize deliverables and verify validity/reproducibility\",status:\"in_progress\"}\n]});\nconst e = await tools.exec_command({cmd:\"python -m py_compile submission/curate.py && python submission/curate.py\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000});\ntext(JSON.stringify(e));\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+Under a severe token budget, a curriculum of deduplicated, fluent English pages whose vocabulary is dense under the disclosed high-quality target will outperform either random web data or a single global “quality” ranking. Explicitly balancing encyclopedia, quality-web, news, and technical-Q&A queues is necessary because a global rank suppresses scarce technical and encyclopedic forms.\\n+\\n+# Mechanism\\n+\\n+The target-to-pool smoothed unigram density ratio concentrates the budget on the target's registers, while prose and boilerplate features remove navigation, templating, repetition, spam, and non-English text. Token-estimated round-robin scheduling prevents the abundant general-web class from displacing the other three.\\n+\\n+An observable prediction independent of final perplexity is that the consumed prefix will have (i) lower repeated-line and boilerplate-phrase rates, (ii) higher English stop-word and sentence density, and (iii) substantially more even estimated token mass across the four inferred domains than a global-quality top-k. Its words should also have a lower cross-entropy under the disclosed target unigram distributions than a random prefix.\\n+\\n+# Falsification\\n+\\n+Hold the tokenizer, token count, trainer, and seed fixed. Compare the submitted curriculum with three ablations: shuffled random IDs, the same filter ranked only by generic prose quality, and the same within-domain rankings without balanced queues. The hypothesis is falsified if the predicted corpus statistics do not move in the stated directions, or if the ablations consistently match or beat the balanced selection on disjoint samples from all four domains rather than only on the disclosed dev tokens.\\n+\\n+# Transfer\\n+\\n+For a different target, language, crawl, or budget, re-estimate the target/background density ratios and domain queues; keep the deduplication, transparent prose defects, and budget-aware scheduler. The method does not depend on source-site labels or hand-picked IDs. With a larger model or budget, relax the quality cutoff and retune queue shares on a disjoint development sample while retaining coverage constraints for rare target registers.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:89119,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "100",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:89119,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "101",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:89119,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "102",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:89119,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json, hashlib, os, numpy as np\\np='/workspace/submission/selection.json'\\nx=json.load(open(p))\\nassert isinstance(x,list) and x\\nassert all(type(i) is int for i in x)\\nassert len(x)==len(set(x))\\nassert min(x)>=0 and max(x)<182016\\nprint({'ids':len(x),'unique':len(set(x)),'range':[min(x),max(x)],'bytes':os.path.getsize(p),'sha256':hashlib.sha256(open(p,'rb').read()).hexdigest()})\\na=np.load('/tmp/train_equal3.npy')\\nprint({'validated_packed_tokens':len(a),'dtype':str(a.dtype)})\\nPY\\ncmp -s submission/selection.json /tmp/profiles4/equal.json; echo \\\"reproduces_evaluated_equal=$?\\\"\\ncat /tmp/equal3_diag.json\\nprintf '\\\\nFILES\\\\n'\\nls -lh submission/selection.json submission/curate.py claim.md\\nprintf '\\\\nFROZEN CHECK\\\\n'\\ngit status --short 2>/dev/null || true\\ncat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);\n"
}