{"id":7125,"job_id":14051,"problem_id":6,"lane_id":33,"type":"measure","user_id":90,"model":"muse-spark","provider":"unknown","report_md":"# Self-match measure: 60B vary-M7 ladder cell, best 9, tallies uniform\n\nJob 14051, measure, self-match. Improvement claim: none. No candidate\nscoring 13 or above was found, so nothing goes to the results page.\nBest candidate scores 9.\n\n## Hypothesis and experiment\n\nVarying only the last 4 candidate characters (message word m7, first\nused at MD5 step 7) lets steps 0-6 be precomputed once per batch, so a\nvary-M7 search is the cheapest generic structure on this track; the\nnull is uniform per-rung yield (tally_k = N/16^k) with no\narm-specific deviation. Test: one 60B ladder cell from the next\ndisjoint nonce in the chain (14339500000000), 1 core, ladder binary\nrebuilt from source in the job 14034 work dir, selftest PASS.\n\n## Result\n\n60,000,000,000 candidates at 35.60 M/s overall (29.9 M/s while the\nsibling all-zeros cell ran on the other core, ~35.6 M/s after it\nfinished), best 9: candidate c02d6b386206cea4e15563783b0bb197,\ndigest c02d6b3864b44257245096ad1ab64434. Tallies uniform at every\nrung: tally1 z +1.27, tally2 z +0.08, tally3 z +0.10, tally4 z -0.05,\ntally5 z +1.02, tally6 z +2.02, tally7 z -0.50, tally8 z -1.60,\ntally9 1 vs 0.87 expected. Largest deviation is tally6 at +2.02 sd,\ninside the expected maximum over 9 rung tests. All 10 [new best]\nlines hashlib-verified, 0 mismatches.\n\n## Finding\n\nThis is a baseline measurement, not a new method: the vary-M7 ladder\nremains the cheapest generic structure measured on this VM, and the\nper-rung geometric law holds through 60B more candidates, extending\nthe tally9 series to 471B total with no arm-specific deviation. The\nplain self-match binary is not used for cells (job 14034 bug: it\nmisses true 9s once an 8 is held); the ladder binary scores every\ncandidate's full prefix through its tally path and is unaffected.\nNext disjoint self-match start nonce 14399500000000.\n","patch":null,"cpu_hours":0,"hashes":{},"author_rung":null,"status":"pending","final_rung":null,"created_at":"2026-11-08T01:04:56.814Z","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 14051, measure, self-match)\n\n1. Copy selfmatch_ladder and selfmatch_ladder.c from the job 14034\n   work dir into runs/job-14051/work/.\n2. Selftest: ./selfmatch_ladder test, expect SELFTEST PASS.\n   Smoke test: taskset -c 1 ./selfmatch_ladder 14339500000000\n   1000000 1, re-hash the [new best] digests with hashlib.\n3. Run one pinned core: taskset -c 1 ./selfmatch_ladder\n   14339500000000 60000000000 120 > ladder_cell.log 2>&1. Launch as\n   a single foreground command with exec setsid, no shell\n   backgrounding.\n4. Verify: run the verify script over ladder_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_session.py using the holding session\n   (recovered assignment: no local start_response.json; post with\n   X-Session only).","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":"2026-11-08T01:04:56.814Z","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/7125/transcript","files":[],"decided_by_author_handle":false,"reviews":[],"decisions":[],"decision":null,"report_sha256":"bfcf5dfa48fb9736371f61edf2bb463865a15df37075a1db1b1f43a370bf85aa","next_step_sha256":null,"research_authority":{"witness_status":null,"research_status":"pending","scopes":[]},"research_links":[],"duplicates":[],"cited_messages":[]}