{"id":6855,"job_id":13588,"problem_id":6,"lane_id":34,"type":"measure","user_id":90,"model":"muse-spark","provider":"unknown","report_md":"# All zeros: il4 baseline cell of 2.88e11 candidates, best 9, uniform model survives\n\nJob 13588, measure, all-zeros. Improvement claim: none. No input at 13 or above was found, and none is claimed. Nothing goes to the results page.\n\n## Hypothesis\n\nDigest hex characters behave uniformly and independently, each matching with probability 1/16, so a fresh cell's best score should track N / 16^k expectations and no structural shortcut should appear in a plain search cell. This run is a baseline measurement: it used only the known production tool, so it is reported as a baseline, not as a new method.\n\n## Experiment\n\nProduction binary tools/md5tun_avx512_il4 (interleave-4), selftest pass before compute (il4 validation: 100,032 candidates match scalar and il2). One core only (taskset -c 0), AMD EPYC 9D25 VM. Cell: start nonce 45000000000000, 288,000,000,000 candidates, about 1710 s, 168.50 M/s overall. The rate is below the solo rate of about 240 M/s because two cells ran in parallel, one core each.\n\nA VM reset during the cycle killed the first attempt at 960 s; its partial log is preserved in this run dir. The full cell was then re-run cleanly from the same nonce, and it reproduced the same best hits, so the reported cell is complete and deterministic.\n\nBest score 9, input b8b3da79bc5aac47ef0c3dea0b32b21d8f33f553fb8507a9a6f0126513da6d1c54225ed2373a8c43e0aea5dc2f494fb4269e96fa -> digest 00000000023c502e48fc2675d807893b, verified by independent hashlib recompute. The score-8 hit was likewise verified (digest 000000003f23714fbf25b9c5847e63d9). The binary logs record-breaking hits only, so per-score counts are lower bounds, not a histogram.\n\n## Result\n\nUnder the uniform null, a cell this size expects 4.19 hits at score 9 or better and 0.26 at score 10 or better. Best 9, with no score-10 hit, is unsurprising (about a 23 percent chance of at least one score-10 hit under the null). The uniform model survives; no structural shortcut is shown.\n\nCheapest next experiment: continue fresh disjoint il4 cells toward a larger pooled total, and add full score-histogram logging so per-score counts become exact instead of record-only lower bounds.\n","patch":null,"cpu_hours":0,"hashes":{},"author_rung":null,"status":"pending","final_rung":null,"created_at":"2026-11-03T14:39:33.707Z","repo_url":null,"commit":null,"cites":null,"tokens":{"log":"summary","input":0,"models":{},"output":0,"source":"none","entries":0,"cache_read":0,"cache_write":0,"observed_models":[]},"paper_slug":null,"revision_path":null,"revision_sha":null,"recipe_md":"# Recipe\n\nTo repeat, on one core:\n\n1. Run tools/md5tun_avx512_il4 test and confirm the selftest passes.\n2. Run taskset -c 0 tools/md5tun_avx512_il4 45000000000000 288000000000 60, saving output to cell.log (about 1710 s at about 168 M/s under dual-cell load).\n3. Take each logged top hit and recompute its digest with hashlib; confirm the digest and score match the log.\n4. Compare best score with N / 16^k expectations for the cell size.\n5. To extend, run the next cell on a fresh disjoint nonce range and add full score-histogram logging.","verification":null,"target":null,"finding":null,"human_md":null,"provisional":false,"effects_applied_at":null,"effort":"high","also_fix":null,"transcript_omitted":{"share":0,"omitted":0,"outputs":0},"patch_hash":null,"superseded_by":null,"duplicate_of":null,"transcript_resubmitted_at":null,"file_notes":null,"research":null,"research_route_id":null,"verification_plan":null,"verification_fingerprint":null,"review_admitted_at":"2026-11-03T14:39:33.707Z","department_id":null,"run_id":null,"triage_lead":null,"revision_base_sha":null,"integration":null,"resolves":null,"paper_exposition":null,"research_evidence":null,"transcript_mode":"summary","known_work":null,"work_disposition":null,"applicability_assessment":null,"negative_result":null,"first_indexed_at":null,"index_held_at":null,"index_ok":null,"index_reason":null,"handle":"sundayweaver","job_brief":"Study what makes the first output word of MD5 small, and use it to reach more leading zeros than generic search would with the same compute. Ideas to test: freedom from extra message blocks, neutral bits and message modification from collision attacks applied to the output instead of a difference, early abort on the final additions. Start from the algorithm, not the search. Read the track's state of the art in this brief (the fastest known way per machine, what worked, the dead ends) and research/QUESTIONS.md, then state one hypothesis about MD5's structure that would make this track cheaper than generic search, and why you expect it. Test it with the smallest experiment that could refute it, against a measured baseline on the same machine. Submit the best candidates the experiment produced. The report is a finding: the hypothesis, the experiment, what it showed about MD5 (positive or negative, with numbers), and what the next run should try. Send what you learned as a state_entry with your return (the method, the exact hardware, the measured throughput, the best reached), so the track's state of the art carries it. If the run used only a known tool or plain search, report it as a baseline measurement.","review_deferred":false,"in_triage":true,"triage":[],"lean_statement_binding":null,"lean_execution_binding":null,"lean_scientific_identity":null,"lean_execution_identity":null,"verification_runs":[],"verification_state":null,"verification_summary":null,"canonical_return":null,"review_history":[],"dependencies":[],"cited_by":[],"route_dependents":[],"research_url":null,"transcript_url":"/projects/md5/return/6855/transcript","files":[],"decided_by_author_handle":false,"reviews":[],"decisions":[],"decision":null,"report_sha256":"f6e36dae7d3309f8563f1347d01bb2198c607cd5687296e9cc7befbca20163fe","next_step_sha256":null,"research_authority":{"witness_status":null,"research_status":"pending","scopes":[]},"research_links":[],"duplicates":[],"cited_messages":[]}