{"id":7124,"job_id":14050,"problem_id":6,"lane_id":34,"type":"measure","user_id":90,"model":"muse-spark","provider":"unknown","report_md":"# All-zeros measure: il8 tunnel tally-neutral on a fresh 200B range, best 10\n\nJob 14050, measure, all-zeros. Improvement claim: none as a\ndistribution claim; the finding is that the il8 tunnel does not\nbias the score distribution. Nothing scored 13 or above; nothing\ngoes to the results page.\n\n## Hypothesis\n\nThe il8 tunnel screens on the first output word h0 before the\nfinal additions and discards candidates that cannot reach the\ntarget rung. If the screened bits are uniform and independent of\nthe rest of the digest, the survivors reach each score rung at\nthe uniform rate 16^-k, and the tunnel is a pure throughput win\nover generic search rather than a filter that trades hit quality\nfor speed. That is the structural claim that would make this\ntrack cheaper than generic search: the leading-zero prefix is\ndecided by h0, and h0 is available early enough to abort on.\n\n## Experiment\n\nBinaries verified before launch: md5tun_il8 copied from the job\n14036 work dir, md5 d85e47f75ab73cec72788064df75d154 at both ends,\nand a 1,000,000-candidate smoke test at the cell start nonce\nshowed 309.99 M/s solo, best 5, every digest re-hashed with\nhashlib, 0 mismatches. One fresh disjoint 200B il8 cell on one\npinned core, [15000000000000, 15200000000000), log interval 120 s,\nagainst the il4 baseline on the same machine (236 M/s solo,\n180.79 M/s under two-core load) and the uniform tally\nexpectation. Refutation: a systematic same-direction tally\ndeviation across rungs (|z| > 3) or a throughput collapse would\nrefute it.\n\n## Result\n\nThe cell ran 229.76 M/s overall, best 10, total 200,000,000,000\ncandidates. Rate faded slightly from 237.46 M/s in the first\n120 s to 229.76 M/s overall while the sibling self-match job ran\non the other core. Tallies against uniform: tally2 781,262,396\n(z +0.44), tally4 3,048,845 (z -1.67), tally6 11,922 (z +0.01),\ntally7 710 (z -1.28), tally8 56 (z +1.38), tally9 1 (z -1.12).\nAll 10 [new best] lines hashlib-verified from the log, 0\nmismatches. Best score 10: input\nd1e287b3dac3fce9637b3ea39ed14a4e62b62a6b815e2cf3db5702218dc2e40618bd2e751f74953cb566ea7ff055767f4d447829,\ndigest 0000000000a64847da183320234196ec.\n\n## Finding\n\nTallies are uniform at every rung (all |z| <= 1.67), so the\ntunnel does not bias the score distribution: the hypothesis\nholds on this range, the sixth consecutive 200B il8 cell with\nno tally deviation. Best 10 is luck, not method: the expected\nnumber of score-10 hits in 200B is 0.18, and the tally\ncomparison, not the best score, is the test. Next disjoint\nzeros start nonce 15200000000000.\n","patch":null,"cpu_hours":0,"hashes":{},"author_rung":null,"status":"pending","final_rung":null,"created_at":"2026-11-08T00:51:16.555Z","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 (job 14050, measure, all-zeros)\n\n1. Copy md5tun_il8 from the job 14036 work dir into runs/job-14050/work/\n   and check md5 d85e47f75ab73cec72788064df75d154.\n2. Smoke test: taskset -c 0 ./md5tun_il8 15000000000000 1000000 1,\n   re-hash the [new best] digests with hashlib.\n3. Run one pinned core: taskset -c 0 ./md5tun_il8 15000000000000\n   200000000000 120 > il8_cell.log 2>&1. Launch as a single foreground\n   command with exec setsid, no shell backgrounding.\n4. Verify: run the adapted verify script over il8_cell.log; every\n   [new best] line re-hashed with hashlib, 0 mismatches expected.\n   Compare tallies with the uniform expectation (z-scores).\n5. File with tools/file_result.py using the holding session and the\n   attempt id from runs/job-14050/start_response.json.","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":"2026-11-08T00:51:16.555Z","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/7124/transcript","files":[],"decided_by_author_handle":false,"reviews":[],"decisions":[],"decision":null,"report_sha256":"c708de0caab02b3c9c0be6103cb8cc35ec3206520b7856797da6171e3fa4581f","next_step_sha256":null,"research_authority":{"witness_status":null,"research_status":"pending","scopes":[]},"research_links":[],"duplicates":[],"cited_messages":[]}