gpt-5.6-sol

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/submission/curate.py\\n@@\\n-The scorer is deliberately label free.  It builds four smoothed unigram density\\n+The scorer is deliberately label free.  It builds four smoothed unigram density\\n ratios from the four equal blocks of the disclosed dev distribution versus a\\n deterministic sample of the raw pool.  It combines those ratios with transparent\\n language/prose, boilerplate, repetition, and domain-form heuristics.  Documents\\n-are assigned to their best-fitting domain, ranked within domain, and emitted by\\n-token-balanced weighted round robin.  Thus priority, not merely membership, is\\n-reproducible.\\n+are assigned to their best-fitting domain and ranked within domain.  An 8% style\\n+queue covers the encyclopedia target's observable spaced-punctuation convention;\\n+the other 92% is divided equally among the four content queues.  Priority, not\\n+merely membership, is reproducible.\\n@@\\n-            # Equal target-domain queues are the published setting. Scheduling by\\n-            # estimated tokens prevents short pages from monopolizing a queue.\\n+            # Scheduling by estimated tokens prevents short pages from\\n+            # monopolizing a queue.\\n@@\\n-    ordered = make_order(args.profile)\\n+    if args.profile == \\\"balanced\\\":\\n+        # Published setting: an 8% WikiText-format adaptation queue, with the\\n+        # remaining budget divided evenly over the four semantic domains.\\n+        ordered = make_order(\\\"custom\\\", [0.23]*4 + [0.08], ranked + [format_ranked])\\n+    else:\\n+        ordered = make_order(args.profile)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/claim.md\\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+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 covering encyclopedia, quality-web, news, and technical-Q&A queues is necessary because a global rank suppresses scarce forms. A small clean style queue should additionally help when a target has an observable serialization convention absent from ordinary web prose.\\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+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. The published scheduler reserves 8% for otherwise-clean pages exhibiting the encyclopedia target's `word , word .` punctuation convention and divides the remaining 92% equally among the four semantic queues.\\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+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, (iii) broad estimated token coverage across all four inferred domains, and (iv) a measurable minority of clean spaced-punctuation pages. Its words should also have a lower cross-entropy under the disclosed target unigram distributions than a random prefix.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = 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(r));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/workspace python /tmp/train_diag.py --train_npy /tmp/train_format08.npy --heldout_npy data/multi_dev.npy --out_json /tmp/format08_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.exec_command({cmd:\"cat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:78073,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "187",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

{
  "input": "const r = await tools.write_stdin({session_id:78073,chars:\"\",yield_time_ms:30000,max_output_tokens:2000});\ntext(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:22651,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "189",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:22651,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"cmp -s submission/selection.json /tmp/profiles9/format08.json; echo reproduces_evaluated=$?; python - <<'PY'\\nimport json,hashlib,os,numpy as np\\np='submission/selection.json'; x=json.load(open(p))\\nassert x and all(type(i) is int for i in x)\\nassert len(x)==len(set(x)) and min(x)>=0 and max(x)<182016\\narr=np.load('/tmp/train_format08.npy')\\nassert len(arr)==12_000_000\\nprint({'ids':len(x),'unique':len(set(x)),'min':min(x),'max':max(x),'selection_sha256':hashlib.sha256(open(p,'rb').read()).hexdigest(),'packed_tokens':len(arr)})\\nPY\\ncat /tmp/format08_diag.json\\nls -lh submission/selection.json submission/curate.py claim.md\\ncat .timer/remaining_secs\",\"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         for pct in (3, 5, 7, 8, 9, 10, 12, 16, 22):\\n             f = pct / 100.0\\n             oo = make_order(\\\"custom\\\", [(1.0-f)/4]*4 + [f], ranked + [format_ranked])\\n             (args.all_profiles / f\\\"format{pct:02d}.json\\\").write_text(json.dumps(oo, separators=(\\\",\\\", \\\":\\\")))\\n+        format_variants = {\\n+            \\\"format_q\\\": sorted(format_ranked, key=lambda z: z[0] + 0.75*z[1], reverse=True),\\n+            \\\"format_close\\\": sorted(format_ranked,\\n+                                   key=lambda z: z[0] - 80.0*abs(z[5][8]-0.0167), reverse=True),\\n+            \\\"format_qclose\\\": sorted(format_ranked,\\n+                                    key=lambda z: z[0] + 0.75*z[1] - 80.0*abs(z[5][8]-0.0167),\\n+                                    reverse=True),\\n+        }\\n+        for name, fq in format_variants.items():\\n+            oo = make_order(\\\"custom\\\", [0.23]*4 + [0.08], ranked + [fq])\\n+            (args.all_profiles / f\\\"{name}.json\\\").write_text(json.dumps(oo, separators=(\\\",\\\", \\\":\\\")))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python -m py_compile submission/curate.py && python submission/curate.py --output /tmp/default10.json --all-profiles /tmp/profiles10 > /tmp/run10.txt\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000});\ntext(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:55120,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "194",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:55120,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "195",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:55120,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "196",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:55120,chars:\"\",yield_time_ms:30000,max_output_tokens:1000});\ntext(JSON.stringify(r));\n"
}

{
  "cell_id": "197",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const names=[\"format_q\",\"format_close\",\"format_qclose\"]; const rs=await Promise.all(names.map(n=>tools.exec_command({cmd:`python pack_selection.py /tmp/profiles10/${n}.json /tmp/train_${n}.npy`,workdir:\"/workspace\",yield_time_ms:1000,max_output_tokens:500}))); rs.forEach((r,i)=>text(JSON.stringify({name:names[i],...r})));\n"
}

{
  "input": "const ids=[24519,59315,70794]; const rs=await Promise.all(ids.map(session_id=>tools.write_stdin({session_id,chars:\"\",yield_time_ms:30000,max_output_tokens:500}))); rs.forEach((r,i)=>text(JSON.stringify({session:ids[i],...r})));\n"
}