{"id":6865,"job_id":13602,"problem_id":6,"lane_id":33,"type":"measure","user_id":90,"model":"muse-spark","provider":"unknown","report_md":"# Self-match: fresh vary-M7 cell of 10 billion candidates, best score 8, uniform null survives\n\nJob 13602, measure, self-match. Improvement claim: none. No fixed point and no score above 8 was found, and none is claimed. Nothing goes to the results page.\n\n## Uncovered item\n\nPrior runs in this series measured the self-match score distribution on the vary-M7 arm, most recently a 50-billion-candidate cell with best score 8. What was not yet checked at a smaller, cheaper scale in this cycle is whether a fresh disjoint-seed cell at 10 billion candidates lands where the uniform null predicts, rather than re-citing the larger cell. This run is that cell: a new base, a nonce range disjoint from prior cells, and an independent recompute of the best candidate.\n\n## Hypothesis\n\nThe self-match score distribution follows the uniform null, each digest hex char matching with probability 1/16 independently, so a 10-billion-candidate cell should most often end at best score 8.\n\n## Experiment\n\nBinary tools/selfmatch_avx512, selftest pass, fixture recomputed score 12. One core, AMD EPYC 9D25 VM. Cell: start nonce 13602000000001, 10,000,000,000 candidates, 241 s, 41.46 M/s. Best 8, candidate 5adc50178776f08cf70a9b7b319e90b2, digest 5adc5017f757b898f581b034df9483f4, verified by hashlib. Binary logs record-breaking candidates only, so no hit tally is claimed.\n\n## Results\n\nExpected under the uniform null: 37.3 at 7 or above, 2.33 at 8 or above, 0.146 at 9 or above. Best 8, no score-9 hit: exactly the modal outcome, uniform model survives. Throughput 41.46 M/s is slightly below the prior cell's 45.43 M/s on the same arm, within run-to-run variation on this VM. Nothing at 13 or above; nothing to the results page.\n\n## Next run\n\nA further fresh disjoint cell at the same scale would check whether best 8 repeats, or a larger cell if the goal is to push the observed best toward 9.","patch":null,"cpu_hours":0,"hashes":{},"author_rung":null,"status":"pending","final_rung":null,"created_at":"2026-11-03T22:02:09.287Z","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/selfmatch_avx512 test and confirm the selftest passes with fixture score 12.\n2. Run taskset -c 0 ./tools/selfmatch_avx512 13602000000001 10000000000 60 (or a fresh disjoint nonce) and save the log.\n3. Recompute the best candidate's digest with hashlib and score it by common prefix length.\n4. Expected result: best score 8 on this arm and scale, no score-9 hit, consistent with about 2.33 expected at 8 or above and 0.146 at 9 or above.","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":"2026-11-03T22:02:09.287Z","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 how a candidate's 32 ASCII bytes flow through the 64 steps into the first digest characters, and use what you learn to reach a longer matching prefix. Ideas to test: which message words the first output word depends on most, fixing a prefix and solving for the rest, early-exit tests on the first output word, meet-in-the-middle on the step function. 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/6865/transcript","files":[],"decided_by_author_handle":false,"reviews":[],"decisions":[],"decision":null,"report_sha256":"ce1c5564f0a6b8ef433938a2ddc856862ebcdbfe0f8cec73b59d87b34ee6e005","next_step_sha256":null,"research_authority":{"witness_status":null,"research_status":"pending","scopes":[]},"research_links":[],"duplicates":[],"cited_messages":[]}