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Iteration 78 โ€” the Stability Pair: DOUBLE KILL KILLS #21 ยท #22 ยท THE DOOR LAW'S THIRD FAMILY

In plain words: two candidates entered together โ€” one counts how consistently each feature gets picked when the selection is re-run on reshuffled blocks of data; the other is its smarter sibling, which stops counting swaps among near-identical features as instability. Their controls all passed: the byte-twin was treated identically to its original in every single run, and the sibling's adjustment was caught working on camera (nine real clusters of near-twin features, with an honest surprise on record: the adjustment isn't always calming โ€” it can INCREASE the instability reading when a rarely-picked feature joins a jittery cluster). But both died at the same door that has now killed three families: asked whether their regime-to-regime readings differ more than randomly re-split blocks of the same data would produce, neither cleared the bar. The meta-pattern is now unmistakable โ€” and discriminating: resampling-style regime readings drown in honest block noise on this data, while instruments with real boundary signal (the two distance certificates) cleared the same style of bar at three to fifty times. The pool was verified healthy (124 features) before trusting the verdict; two data-engineering adjustments (named windows, availability screening) are on the record.

Preflight (A0): 2026-07-10 18:05 โ€” load1 4.15/32c (steady) ยท no heavy nasimubd jobs ยท 35 GiB avail, si/soโ‰ˆ0 ยท all four services active โ†’ ALL PASS. Sync: main up-to-date, worktree 0 behind. Two capped runs (1.08 min + pool verification), readonly=2 loader, block resampling of real values (03c). Form fixes recorded: envs selected by NAME (the 131-column complete-case shifted length ordering); pool availability-screened โ‰ฅ90% (iter-8 empty-columns finding) โ€” 126/134 survived, E3 over 124 real features.

Grouped exam results (stability_pair_exam.py)

LegReadingRuling
E1 โ€” twin known-answer (certain)Twin and duration: IDENTICAL selection indicators in every run, all envs; same similarity cluster (ฯ=1)PASS
E2 โ€” the adjustment's own ground truthUnion mathematics held everywhere; 9 swap clusters observed. Honest nuance ON RECORD: reduction +0.28 (gold, 12-member cluster) but โˆ’0.35 in midcycle โ€” cluster-union can increase instability when rarely-selected members join a near-0.5 cluster; the adjustment is not uniformly stabilizingPASS (nuance recorded)
E3 โ€” the door law (rows 83/87), per candidateH-068 unadjusted: obs 0.1686 vs null 97.5th 0.1921 ยท H-069 adjusted: obs 0.1725 vs 0.2056 โ€” neither regime difference exceeds pseudo-regime block noise (124-feature aggregate)FAIL ร—2
Meta-lesson (thrice-witnessed)LGC ร—2, selection-stability ร—2: resampling-flavored between-regime readings drown in honest block nulls โ€” while real boundary signal clears the same bar at 3.5โ€“50ร— (certificates #13/#14). The law discriminates; it does not merely excludeSTANDING
Verdicts (row 89)H-068 EXCLUDE โ€” KILL #21 ยท H-069 EXCLUDE (adjustment verified working, boundary-irrelevant) โ€” KILL #22 ยท BONEYARD #36/#37

โ–ถ Next iteration

Iteration 78 ยท 2026-07-10 ยท VERDICT (row 89) โ€” double EXCLUDE ยท kills #21/#22 ยท boneyard #36/#37 ยท capped (1.08 min + verify) ยท readonly=2 ยท zero generated values ยท append-only ยท evidence: stability_pair_exam.py ยท stability_pair_exam_results.json