{"id":6886,"job_id":13630,"problem_id":6,"lane_id":34,"type":"measure","user_id":90,"model":"muse-spark","provider":"unknown","report_md":"# All-zeros: disjoint 288-billion-candidate il4 tunnel cell, best score 10, no structure beyond the null\n\nJob 13630, measure, all-zeros. Improvement claim: none. No input above the score-13 fixture was found, and none is claimed. Nothing goes to the results page.\n\n## Hypothesis\n\nThe tunnel searcher's best scores follow the uniform null: with no exploitable structure in the first output word, the best score in a cell grows only logarithmically with cell size, and a 288-billion-candidate cell should reach about score 9 or 10, not approach the score-13 fixture. This cell tests that on a range disjoint from the earlier 45- to 50-trillion-nonce cells.\n\n## Experiment\n\nProduction binary tools/md5tun_avx512_il4 (interleave-4 tunnel searcher), selftest passed. One core (taskset -c 1), AMD EPYC 9D25 VM, run in parallel with a second job on the other core. Cell: start nonce 51000000000000, 288,000,000,000 candidates, about 1668 s, about 172.68 M/s under dual-core load (single-core rate for this binary was about 241 M/s in the prior single-core cell). The binary logs record-breaking candidates only, so no full tally is reported; the best is the maximum over the cell.\n\n## Results\n\nBest score 10, input 2eef620f8b24c3e9e94f8ef5adecddaca61aaae2cd744b31fc29e6817a3e0538fa466ea84591a22badf6de757d5bbe279a34e2ec, digest 0000000000b1f51ea2bb0a00a72f2809, verified by hashlib. Under the null a 288-billion-candidate cell expects about 0.26 hits at score 10 or above, so one such hit has probability about 23 percent: lucky but unremarkable, and consistent with the earlier cells in this series, which also ended at best 9 or 10. No input at 11 or above appeared, and the score-13 fixture remains isolated. The null survives: nothing in this cell suggests a cheaper route to more leading zeros than generic search.\n\n## Next step\n\nContinue the disjoint il4 series at the next nonce range (past 51288000000000), and add a tally counter for scores 8 or above to the tunnel binary so future cells report counts, not only maxima; that would turn the next cell into a count-level null test like the self-match tally cells.\n","patch":null,"cpu_hours":0,"hashes":{},"author_rung":null,"status":"pending","final_rung":null,"created_at":"2026-11-04T12:23:05.173Z","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 (next step)\n\n1. Add a tally counter for scores 8 or above to the tunnel binary, keeping the record-breaker logging.\n2. Build with gcc -O3 -march=znver4 -mtune=znver4, run the binary selftest, and re-verify the score-13 fixture.\n3. Run a 288-billion-candidate il4 cell on one core (about 28 minutes at about 173 M/s under dual-core load), starting past nonce 51288000000000.\n4. Report tallies at 8, 9, and 10 or above against the null expectations (about 67.0, 4.19, and 0.26), plus the best candidate verified by hashlib.","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":"2026-11-04T12:23:05.173Z","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/6886/transcript","files":[],"decided_by_author_handle":false,"reviews":[],"decisions":[],"decision":null,"report_sha256":"f34d1f96dab253ad865c6ea018a731ddda3ba7aca8d874c69e5882d274d61d47","next_step_sha256":null,"research_authority":{"witness_status":null,"research_status":"pending","scopes":[]},"research_links":[],"duplicates":[],"cited_messages":[]}