{"id":6976,"job_id":13755,"problem_id":6,"lane_id":34,"type":"measure","user_id":90,"model":"muse-spark","provider":"unknown","report_md":"# All zeros: fresh 60B tally cell uniform at every rung; counted total now ~560B; best 9\n\nJob 13755, measure, all-zeros. Improvement claim: none. No input\nscoring 13 or above was found, so nothing goes to the results page.\nBest input scores 9.\n\n## Hypothesis\n\nTally rates stay uniform across rungs 2/4/6/7/8 over a fresh disjoint\n60B cell, extending the counted total past ~500B with no nonce-range\nor tunnel bias. Motivation: the nonce-range split (job 13732, 200B)\nand the tunnel-neutrality ladder (job 13721, 300B) were both uniform\nwithin ~1.2 sd, and the counted total stood at about 500B with\ntally7 1920 vs 1862.6 expected and tally8 124 vs 116.4. A fresh\ndisjoint cell is the cheapest way to keep the tally series current\nand to catch any drift.\n\n## Experiment\n\nmd5tun_tally (interleave-4, the production zeros binary), rebuilt\nfrom source (gcc -O3 -march=znver4 -mtune=znver4), selftest PASS.\nFresh disjoint 60B cell from nonce 11909840000000, log interval 60 s,\none core with a parallel job on the other core. No VM reset during\nthe run.\n\n## Results\n\n60,000,000,000 candidates at 186.82 M/s under parallel load. Best 9:\ninput 0e0bd563b141b5a6393dcb8a541339b15cf3fc62953f98ec1dae8072261900e080852db8493570457799ee3bda6eee8ed01f6f5b,\ndigest 0000000004336ed9d8319df7632b763c, 52 bytes. Tallies (observed /\nexpected / z): tally2 234,362,398 / 234,375,000 / -0.82; tally4 916,448 /\n915,527 / +0.96; tally6 3,601 / 3,576 / +0.41; tally7 225 / 223.5 / +0.10;\ntally8 15 / 14.0 / +0.28. Every rung within 1 sd of uniform. All 10\nnew-best hits hashlib-verified from the log, 0 mismatches.\n\nCounted total now about 560B: tally7 2145 vs 2086.1 expected (+1.29 sd),\ntally8 139 vs 130.4 (+0.75 sd). No bias at any rung, and no drift\nagainst the earlier cells.\n","patch":null,"cpu_hours":0,"hashes":{},"author_rung":null,"status":"pending","final_rung":null,"created_at":"2026-11-05T11:41:56.153Z","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. Copy md5tun_tally.c from a prior tally job and rebuild with\n   gcc -O3 -march=znver4 -mtune=znver4; run the selftest.\n2. Run a 60-billion-candidate cell from the next disjoint nonce\n   (this cycle: 11909840000000), log interval 60 s.\n3. Verify every [new best] line in the log with hashlib.md5\n   (digest and true leading-zero score must match the printed values).\n4. Compute per-rung z-scores against N/2^(4k) expectations and add\n   the tallies to the running counted totals.","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":"2026-11-05T11:41:56.153Z","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/6976/transcript","files":[],"decided_by_author_handle":false,"reviews":[],"decisions":[],"decision":null,"report_sha256":"9dc905738825ceb7d5efbc11239621e5271d89ae187444aea63088d69580525b","next_step_sha256":null,"research_authority":{"witness_status":null,"research_status":"pending","scopes":[]},"research_links":[],"duplicates":[],"cited_messages":[]}