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Golden tickets are real: one searched noise vector ≈ 75% of the mean-of-10 gain

See also: the owner-requested consolidated visual report — the whole thread in five charts.

2026-08-08 05:2xZ. Stages 1–2 of the golden-ticket noise screen (pre-reg, #1), reads frozen there and executed by ticket_scores.py (stage 1) and goldenticket_stage2_results.py (stage 2, oracle-green before its data). Stage 3 (mean-of-top-10, record-only R3) launched 05:16Z, lands ~08:1xZ. Model: flow teacher @80k, Heun-30, k4l2 panel.

The question

Noise-draw ensembling (mean-of-10 random draws) buys the flow family −1.24 chunk MAE. The golden-ticket hypothesis (the test-time-scaling literature’s “lucky noise” claim, transplanted to our stack): is there a fixed noise vector — one ticket, reused at every frame — that captures part of that gain at single-draw cost?

Stage 1 — R1: CONFIRM, loudly

One batched draws-64 eval on the 2,458-frame probe, where draw m IS ticket m (64 candidates from a sha-pinned N(0,I) bank), scored per-ticket by pooled core chunk MAE:

  • sd of the 64 ticket scores = 0.82252 vs kill line 0.0785 (the upper 95% χ²₆₃ edge of the banked null σ_probe 0.0669) — 12× the null. Smooth spread 5.71–9.37, no outlier artifact: tickets differ enormously, in both directions.
  • min = 5.70564 vs line 6.52401 (expected null min − 2 sd).
  • Winner: ticket 33 (5.7056/1.8701 on probe rows — selection-biased by construction; stage 2 exists because of that).

Stage 2 — R2: REAL, bigger on unseen rows than on the rows that picked it

Winner ticket, full 25,800-frame panel, judged ONLY on the 14,746 complement core rows (panel core minus every probe frame-identity triple):

  • Paired Δ (ticket 33 − banked stable-key single draw) = −0.924 [CI95 −0.985, −0.866] vs the REAL line −0.05. The selection-biased probe-row delta was −0.819 — the effect is larger where the ticket was never evaluated during selection. This is not probe-row luck surviving; it is a property of the ticket.
  • Core-pooled, board convention: 5.6468 / 1.8963 — a single Heun-30 draw that lands within 0.005 of AR-100k’s draws-10 row and captures ~75% of the mean-of-10 gain (−0.924 of −1.235) at 1/10th the draws.

The mechanism is directional, not norm. The obvious deflationary story — a small-norm ticket mimicking the mean’s noise-shrinkage — is dead on the data: ticket 33’s norm ranks 29/64 (17.15 vs bank mean 17.26), and corr(norm, ticket score) = −0.05 across the bank. Specific directions in the 300-dim noise space are systematically better across thousands of held-out frames. That is the LAFM/DSRL premise (structured noise carries mode information) showing up in our own decoder, unprompted.

What executes next (all pre-registered)

  • Stage 3 (running): mean of the top-10 tickets, draws-10 ticket noise — does searched noise beat random noise inside the ensembling regime? R3 = pooled Δ vs the banked mean-of-10 5.3645, tie band ±0.02, record-only either way (mean-of-10’s row is not displaced without a paired follow-up). Screen budget after stage 3 ≈ 5.5 of the 6 GPU-h gate.
  • Leaderboard: R2 REAL earns the ticket row (keying stated, sha-pinned): teacher single-draw ticket-33 at 5.6468/1.8963.
  • R4 record-only reads (per-dataset argmin disagreement — the task-locality/LAFM question — plus dispersion-quartile geometry and horizon profile) come with the stage-3 write-up; the stage-1 dumps already carry them.
  • The noise-structure ladder above the screen (per-dataset tickets → LAFM learned priors → DSRL-style state-conditioned noise) was pre-mapped on the noise-space-steering pages; R1+R2 landing this hard is the ladder’s entry condition. Each rung needs its own pre-reg.

Cost

Stage 1 ~1.7 GPU-h, stage 2 ~0.85, stage 3 ~2.9 projected — ~5.5 of the pre-registered 6 GPU-h, one quiet local-GPU day, zero training.

Stage 3 + R4: the screen closes (appended 08-08 ~08:2xZ)

Stage 3 landed 08:15:39Z rc=0 (2.99 GPU-h measured; screen total ~5.55 of the 6 GPU-h gate). Frozen reads via the oracle-green goldenticket_stage3_results.py (top-10 sha pinned, 4-way identity byte-match, pooled column verified equal to the draws-stack mean).

R3 — searched noise wins inside the ensembling regime too: INTERESTING, 9× beyond the band. Mean-of-top-10-tickets, full panel, core-pooled: 5.1847 / 1.3831 vs the banked random-noise mean-of-10 5.3645 / 1.4242 → pooled Δ = −0.180 against a ±0.02 tie band. Both cells’ draw-noise scales are ≤0.008, so this is not pooling luck. Per the pre-reg this is record-only — the mean-of-10 board row is not displaced without a paired follow-up (the banked row’s per-frame npz was never retained, so a paired read needs one re-run) — but the number itself is currently the best chunk MAE and the best first_mae measured on this panel by any config (board best 5.3645/1.4242). The follow-up that could seat it as a row belongs to the noise-ladder pre-reg now in the queue.

R4a — tickets are task-local, exactly as the paper (and 2603.11642) predicted. Per-dataset per-ticket matrix from the stage-1 dump (792 probe datasets): ticket 33 is the per-dataset argmin in only 4.4% of them; every one of the 64 tickets wins somewhere; the per-dataset argmin falls inside the global top-10 set 29.8% of the time (~2× the 15.6% null). Median margin left on the table vs the global winner: 1.18 pooled MAE. The loud caveat the pre-reg banked in advance: the median dataset has 2 probe frames — a 64-way argmin on 2 frames is mostly selection noise, so these per-dataset winners are hypotheses, not results. (For calibration: 2603.11642 measured the same structure cleanly — noise main effect 1.4%, context×noise interaction 39.4% — and its best shared noise was optimal in 3.1% of contexts; our 4.4% is the same picture.)

R4b — the ticket buys most where the draws disagree most. Dispersion-quartile geometry (dispersion = valid-weighted std across the stage-3 top-10 stack; gain = ticket 33 − stable-key per frame, all 17,204 core rows): quartile mean gains −0.35 / −0.75 / −1.09 / −1.44 from tightest to most dispersed. Monotone through all four quartiles — the winner ticket is not shaving uniform noise; it wins where the decoder’s noise-response is largest, which is also where a per-dataset or per-frame escalation has the most to work with.

R4c (horizon): the winner’s complement-row gain is spread across the chunk horizon (banked in analysis__goldenticket_stage2.json / analysis__goldenticket_stage3.json), not a first-step artifact.

Screen verdict, final: R1 CONFIRM → R2 REAL → R3 INTERESTING. One searched, sha-pinned noise vector is real on held-out rows; searched top-10 noise beats random-noise ensembling by −0.180 pooled; the effect is directional, task-local, and dispersion-concentrated. The noise-structure ladder’s entry condition is met with headroom — the per-dataset-tickets rung pre-reg (queued) inherits R4a’s caveat (per-dataset cells need a sample-size floor and held-out confirms) and R4b’s targeting signal.