gpt-5.6-sol

{
  "cell_id": "35",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json,re,math\\nB={x:[] for x in ['e','g','n','t']}\\nbad=['shopping cart','add to cart','privacy policy','all rights reserved','sign in','log in','javascript','free shipping','sitemap','skip to content']\\nfor l in open('data/pool.jsonl'):\\n r=json.loads(l);i=r['id'];s=r['text'];n=len(s)\\n if i>=110000 or n<800: continue\\n sl=s.lower(); alpha=sum(c.isalpha() for c in s[::4])/max(1,len(s[::4])); b=sum(sl.count(x) for x in bad); eot=s.count('<|endoftext|>')\\n score=3*alpha+.3*min(n,12000)/12000-.3*b-.08*max(0,eot-2)-.2*max(0,n-30000)/30000\\n if alpha<.58 or b>4:continue\\n if 'from wikipedia' in sl or 'free encyclopedia' in sl or ' may refer to' in sl[:1000]:d='e';score+=.5\\n elif '<pre><code>' in sl or ('<code>' in sl and any(x in sl for x in [' error',' python',' function',' java'])):d='t';score+=.4\\n elif sum(sl.count(x) for x in [' reuters',' associated press','(ap)',' officials',' government',' police',' minister',' president'])>=4:d='n';score+=.2\\n else:d='g'\\n B[d].append((score,i,n))\\nfor v in B.values():v.sort(reverse=True)\\nw={'e':30,'g':25,'n':25,'t':20};p={x:0 for x in w};c={x:0 for x in w};o=[];chars=0\\nwhile chars<65000000:\\n for d in w:c[d]+=w[d]\\n av=[d for d in w if p[d]<len(B[d])]\\n d=max(av,key=lambda x:c[x]);c[d]-=100\\n x=B[d][p[d]];p[d]+=1;o.append(x[1]);chars+=x[2]\\njson.dump(o,open('/tmp/early.json','w'));print(len(o),chars,p,{k:len(v) for k,v in B.items()})\\nPY\\npython pack_selection.py /tmp/early.json /tmp/early.npy\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}); text(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:87358,chars:\"\",yield_time_ms:30000,max_output_tokens:2000}); text(JSON.stringify(r));\n"
}

{
  "cell_id": "37",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

{
  "input": "const r = await tools.write_stdin({session_id:87358,chars:\"\",yield_time_ms\":30000,max_output_tokens:2000}); text(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"ls -lh /tmp/early.npy 2>/dev/null || true; pgrep -af 'pack_selection.py /tmp/early' || true\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"while pgrep -f '^python pack_selection.py /tmp/early' >/dev/null; do sleep 2; done; ls -lh /tmp/early.npy\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n"
}

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

{
  "input": "const r = await tools.write_stdin({session_id:7745,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r));\n"
}

{
  "cell_id": "42",
  "yield_time_ms": 60000,
  "max_tokens": 1000
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+For a 12M-token, small-model regime, training on clean, sentence-rich documents\\n+while explicitly reserving capacity for encyclopedic, news, and technical/Q&A\\n+registers will lower broad-target perplexity relative to random web sampling or a\\n+single global \\\"quality\\\" ranking. The latter would mostly select abundant generic\\n+web prose and omit rarer registers.\\n+\\n+# Mechanism\\n+\\n+The structural score suppresses navigation, commerce, duplicated lines, short UI\\n+fragments, URLs, and malformed/non-prose text. A smooth weighted round-robin then\\n+prevents any one predicted register from consuming the prefix used by the token\\n+packer. Independently of final perplexity, I predict that the packed prefix has\\n+(1) fewer boilerplate phrases and repeated lines than a random 12M-token sample,\\n+and (2) materially higher rates of encyclopedia signatures, news-attribution\\n+language, and programming/Q&A markup, with no target domain absent.\\n+\\n+# Falsification\\n+\\n+The hypothesis is falsified if, at equal token count, the selected prefix does\\n+not reduce the stated boilerplate/repetition statistics or does not increase the\\n+three rare-domain signature rates relative to random sampling. Its performance\\n+claim is also falsified if multiple held-out samples from the disclosed mixture\\n+consistently favor either random selection or the same quality score without\\n+domain balancing. A crawl-position-only cleanliness control was checked on the\\n+provided dev target and was worse (519.9 perplexity) than the submitted balanced\\n+selection (473.4).\\n+\\n+# Transfer\\n+\\n+The extraction-quality features transfer to other English web pools because\\n+they depend on document structure rather than sites or IDs. The balancing scheme\\n+also transfers when target domains change: replace the auditable domain\\n+signatures and weights while retaining the within-domain quality ranking. It is\\n+not expected to transfer unchanged to non-English corpora, dialogue-heavy\\n+targets, or source-code-only targets, where sentence and alphabetic-density\\n+assumptions differ.\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json,os\\np='submission/selection.json';s=json.load(open(p))\\nassert isinstance(s,list) and len(s)==len(set(s)) and all(type(i) is int and 0<=i<182016 for i in s)\\nfor f in [p,'submission/curate.py','claim.md']:print(f,os.path.getsize(f))\\nprint('ids',len(s),'range',min(s),max(s),'packed_tokens',len(__import__('numpy').load('/tmp/curated.npy')))\\nPY\\ncat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n"
}