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Iteration 42 โ€” H-021 EILLS EXCLUDED KILL โ€” ฮณ-FRAGILE ยท EMPTY COLLAPSE ฮต-FRONTIER WITNESS #3

In plain words: yesterday's promising candidate โ€” the one that folds "predict well" and "stay steady" into a single price โ€” aced its exam but failed the field test. Turned loose on all 130 real features, its answer machine broke in a revealing way: as soon as the steadiness weight is meaningful, it answers "nothing" โ€” for every single feature. The reason is arithmetic, not mystery: on real market data no feature has a perfectly steady relationship with the anchors (their predictive power is tiny โ€” median 2.6%), so the steadiness charge always outweighs the prediction reward and the empty answer is always cheapest. An instrument that gives the same answer for everything measures nothing โ€” the same "dead weight" failure our very first planted control was designed to catch. The silver lining is scientific: this is now the third independent engine to conclude that perfect steadiness does not exist on this market โ€” steadiness is a matter of tolerance, exactly as iteration 22 discovered. The successor is already in the catalog: the tolerance-based relaxation (H-026 lead). Next up: the queue's next candidate, Causal Dantzig.

Preflight (A0): 2026-07-10 01:05 โ€” load1 2.61/32c (โ‰ค24) ยท second claude session visible but light (<2 cores) ยท 32 GiB avail, si/so=0 ยท CH + sidecar + kintsugi active โ†’ ALL PASS. Sync: main +1 (#601 runbook doc, unrelated); rebased cleanly. One capped run, 0.44 min, 5 workers, readonly=2 loader, watchdog clean, deterministic algebra over real rows only.

Census results (pre-registered rule in eills_m3_census.py)

CheckReadingRuling
ฮณ stability (band [1,1000])Adjacent-ฮณ selection agreement: 1โ†’10 = 0.815 โœ— (bar 0.90) ยท 10โ†’100 = 1.0 ยท 100โ†’1000 = 1.0RULE 1 FIRES โ€” ฮณ-fragile
The collapse behind itAt ฮณ โ‰ฅ 10 the selected support is EMPTY for 130/130 subjects: Q(empty)=1 beats every penalized support once ฮณร—penalty exceeds the MSE savings โ€” and the anchors explain only 2.6% (median) of any feature's variance. Selection readout = constant = the C2 dead-weight failure mode. The kill survives any re-draw of the band: fragile at its edge, dead inside it.DEAD WEIGHT
M3 (moot, recorded)Only informative readout: poi (price-of-invariance) โ€” worst |ฯ| = 0.663 vs e-ICP n_acc. Not redundant, but it is a variance-explained gauge, not an invariance instrument.0.663 < 0.95 โ€” irrelevant given rule 1
Exam-vs-census contrastThe exam (row 52) proved the machinery identifies TRUE invariance where it exists (twin at Q=0 at every ฮณ). The census collapse is therefore a data truth, not a code artifact: no exact invariant anchor relationship exists for ordinary features.MEASUREMENT ATTACKED & TRUSTED
Verdict (row 53)EXCLUDE(ฮณ-fragile at census scale โ€” agreement 0.815 < 0.90 at the 1โ†’10 edge; selection constant-empty for ฮณ โ‰ฅ 10) โ†’ BONEYARD. Successor path on record: ฮต-relaxed / robust variant with a derived ฮณ (the H-026 invariance-guided-relaxation lead).
WHY "NOTHING" IS ALWAYS CHEAPEST (ฮณ โ‰ฅ 10, ordinary features):

  Q(empty)      = 1.0                                (standardized variance, no penalty)
  Q(any S โ‰  โˆ…)  = (1 โˆ’ Rยฒ_S)  +  ฮณ ยท penalty_S       (Rยฒ_S โ‰ค ~0.026 median ยท penalty_S > 0 always,
                     โ‰ฅ 0.974        โ‰ฅ 10 ยท penalty     because NO exact invariance exists)
                                                      โ†’ Q(S) > 1 the moment ฮณยทpenalty > Rยฒ_S
  โ‡’ empty wins everywhere  โ‡’ constant readout  โ‡’  dead weight

  THE SAME FACT, THREE ENGINES (ฮต-frontier, iter 22):
    e-ICP twins exams   โ†’ only the byte-identical twin is exactly invariant
    StabReg (iter 37)   โ†’ zero surviving stable+predictive teams on real targets
    EILLS (this iter)   โ†’ empty-set collapse for all 130 subjects

โ–ถ Next iteration

Iteration 42 ยท 2026-07-10 ยท EXCLUDE (row 53, BONEYARD +1) โ€” kills are wins ยท capped (0.44 min) ยท readonly=2 ยท zero generated values ยท append-only ยท evidence: eills_m3_census.py ยท eills_m3_census_results.json