Noise-ladder rung 2: per-dataset tickets FALSIFIED out-of-sample

2026-08-08 19:2x–19:4xZ. Stage-2 confirm eval ran per the pre-reg (launched 18:34Z after the owner cleared the credit-cap wait, ~0.83 GPU-h of the ≤ 1.5 gate; frozen reads chained at rc=0, adjudication banked in analysis__noise_ladder_rung2.json). The pre-registered primary read fired its falsifier — and not marginally: the effect is significantly in the WRONG direction. The per-dataset rung of the noise-structure ladder is closed.

The chart-led consolidated visual report now carries the whole thread, including this falsification and the seating verdict below.

The one number

Read 1 (primary, frozen): Δ_route = routed − ticket 33, pooled over the qualifying complement core rows (6,014 rows the map never saw during selection), dataset-clustered bootstrap CI95, seed 0. Pass rule: CI95 entirely below 0.

Result: +0.129, CI95 [+0.060, +0.205] — entirely above zero. Routing each qualifying dataset to its own probe-selected ticket is significantly worse than giving every dataset the single global winner, ticket 33.

The supporting reads agree with the primary rather than softening it. Read 3 (win table): routing wins 34 qualifying datasets, loses 54, ties 9 — win rate 0.386, two-sided sign p = 0.042. Read 4 (record-only mirrors): Spearman between a dataset’s draw-dispersion and its routing gain is −0.05 — the dispersion signal that ordered R4b’s quartiles carries nothing about which datasets benefit from their own ticket.

The selection-transfer inversion, quantified

The probe rows that selected the map showed a selection-biased delta of −0.60. On rows the selection never touched, the same map delivers +0.13. That inversion is the median-2-frame caveat from R4a cashing out: stage 0’s F=6 permutation-null floor guaranteed the per-dataset argmins beat shuffled argmins on the banked data, but with ~6–20 probe frames per dataset the argmin still memorizes its cell. Within-dataset row-holdout was the honest test, and it failed it.

Two record-only reads keep the result well-framed rather than over-read:

• Read 2: routed vs the stable-key baseline is still −0.756 [−0.876, −0.649]. The golden-ticket effect itself (one shared structured noise beats per-sample keys, R2’s −0.924) reproduces on this fresh decode. What failed is specialization, not tickets.
• The horizon mirror shows structure the pooled number hides: routing actually wins the first ~8 steps of the chunk and loses increasingly from step ~15 on — per-dataset tickets help the chunk’s opening and hurt its long tail.

What this closes, and what it doesn’t

Closed: the per-dataset-tickets rung. Board row stays with global ticket 33 (5.6524 full-panel on this decode, consistent with the banked 5.6468). No amendment, no re-run: the pre-reg’s cell sizes were the named risk, the falsifier was built for exactly this outcome, and it fired with room to spare.

Not closed: the ladder above it — but the bar moved. LAFM-style learned mode priors and DSRL-style state-conditioned noise now inherit a measured prior: naive per-context specialization at small per-context n inverts out-of-sample on our panel. Any future specialization rung must show transfer on held-out rows at selection time, not only a permutation-null clearance. The early-vs-late horizon split is the one genuinely new lead (a chunk-position-dependent noise policy would be a different, cheaper axis than per-dataset routing), recorded here as a record-only observation — it gets no arm without its own pre-reg.

Independent and unaffected: the seating arm (launched 19:25Z at stage-2’s rc=0, ~3.0 GPU-h) — it re-runs the random-noise draws-10 config with dumps to give R3’s mean-of-top-10 vs mean-of-random-10 the paired read the board seating requires. Its verdict concerns the top-10 ensemble, not per-dataset routing, and lands tonight.

Seating verdict (landed 2026-08-08 ~23:1xZ): CONFIRMED

The paired read ran after a one-abort detour worth recording. The run finished clean (rc=0, ~3.0 GPU-h) but its base-equality oracle fired: the re-run reproduced the banked 5.3645 chunk at 4dp yet missed first_mae by −1.27e-4 (1.4241 vs 1.4242). Held per the never-re-tolerance clause; the diagnosis (pre-reg Amendment 2, analysis__seating_base_equality_diag.json) adjudicated benign numeric drift, noise reproduction confirmed: state-copy per-dataset cells match exactly (878/878), the bijou cells move at most 1.7e-3 even at 4-frame size — two orders below draw-level dispersion, so resampled noise is excluded — and git locates the mechanism in the batched-ensembling merge (2ee2be5/85cdc0a, 08-07: sequential batch-32 solver calls → one tiled batch-320 call; same noise tensor, different kernel reduction order).

With the amended gate green, the frozen read: paired Δ = −0.17358 [CI95 −0.19556, −0.15214] on 17,204 core frames, entirely below zero (dataset-clustered CI [−0.20188, −0.14756] agrees; first-step mirror −0.041 [−0.047, −0.034], record-only). Expectation 4 confirmed — the board row moves to the mean-of-top-10-tickets ensemble, 5.1847 / 1.3831, the best chunk and first numbers on the panel. The ☆ bar (≤ 5.0) gap shrinks 0.37 → 0.18. Full record: analysis__noise_ladder_seating.json.