Pre-registration: golden-ticket noise screen (#1, teacher-first)

2026-08-07 ~18:0xZ. Immutable once posted. Idea #1, from the Golden Ticket deep read (2603.15757 at v3 — the sampling-beyond-selection page). Zero training; eval-side only. The instrument (a “ticket” noise-key mode) does NOT exist yet: it lands oracle-gated before launch, and if implementation forces any semantic deviation from this post, an amendment posts before launch (the #19 amendment precedent).

Question

For our frozen flow teacher, does a single searched initial-noise vector — substituted at every frame — beat fresh Gaussian noise on the panel? Golden Ticket showed tickets beat Gaussian sampling on 46/51 task–policy pairs, searched by episodic rollout return. Their search needs environment rollouts; our panel is the offline criterion they lack — the whole search reduces to one batched draws-style eval where the “draws” are candidate tickets.

The honest prior is against a large effect: our panel spans many tasks, and the paper’s own structure says tickets are task-local — per-task tickets always gain (+13 LIBERO-Spatial), the best single shared ticket per suite regressed in all three suites (−2.6 to −12.0). A panel-wide ticket is the shared regime. That is precisely why this is a cheap screen and not an execution arm: stage 1 asks only whether global ticket structure exists at all, and the per-dataset geometry (which comes free from the dump) reads the task-locality question on our own data.

Teacher-first (the deep read’s design note): the SnapFlow student compiled away most of its draw spread (draw-averaging gain −0.236 vs the teacher’s −1.258), so its searchable ticket space may have collapsed. This screen targets the teacher’s noise space only; any student rung is an escalation amendment gated on stage-2 passing.

Frozen design

Checkpoint / decode: bijou_flow_artrunk_h1024_40k_ddp2 @ step_080000, Heun-30 — the leaderboard’s flow-teacher config. Banked anchors: single draw (stable-key) 6.5997 / 1.9355, mean-of-10 5.3645 / 1.4242.

Tickets: M = 64 candidates, i.i.d. N(0, I), shape [50, 6] (chunk × action_dim), generated once at instrument-land time from a domain-separated seed sequence ([TICKET_DOMAIN, 0, m] for m = 0..63), saved to a single tickets npz whose sha256 is quoted in every read. Random search only — CEM or any adaptive refinement is out of scope for the screen (escalation material).

Stage 1 — search (probe subset): one eval on the plans/holdout_curated_v0_k4l2_drawsprobe_s7.json probe (2,458 frames, f_eff 2,341.7) with --sample-draws 64 under the new ticket noise key: draw m at every frame uses ticket m — the defining ticket property. --dump-draws retains per-ticket per-frame predictions; the per-ticket score is pooled core-frame chunk MAE through the same pooling as the fairness reads. Winner = argmin (tie-break: lower first_mae); top-10 = the 10 lowest.

Stage 2 — confirmatory (full panel, complement rows): the winner ticket, full 25,800-frame panel, --sample-draws 1 with ticket noise. Primary read pools complement core rows only (panel core frames minus the probe plan’s frame-identity triples; ≈14,147 f_eff) — the probe rows selected the winner, so they are excluded from the read that judges it. Paired per-frame Δ vs the banked stable-key single-draw npz (eval__..._panel_curated_v0_k4l2_stablekey_heun30.npz) re-pooled on the identical rows.

Stage 3 — the “both” cell (only if stage 2 passes): mean of the top-10 tickets, full panel, --sample-draws 10 ticket noise — does searched noise beat random noise inside the ensembling regime? Compared to the banked mean-of-10 row (5.3645) at pooled level (its per-frame npz was not retained; both cells’ pooled draw-noise scales are ≤ σ_draw/√10 ≈ 0.008, so a pooled comparison with a ±0.02 tie band is honest).

Null scales (all banked, none new)

• Per-draw pooled spread at probe size: σ_probe = 0.0669 (the 10 stable-key draws of analysis__sigma_draw_direct.json; range 6.5766–6.7977). Under the null (no global ticket effect; noise effects frame-idiosyncratic and exchangeable) 64 ticket scores spread with exactly this σ.
• Expected minimum of 64 null tickets: mean − 2.345·σ_probe = mean − 0.157, sd of that minimum ≈ 0.45·σ_probe ≈ 0.030 (Monte-Carlo, 2·10⁵ trials, seeded).
• Panel-scale σ_draw = 0.02367; at complement size σ ≈ 0.0256, so the standing 0.05 adopt floor ≈ 2σ on the stage-2 read.

Frozen reads and decision lines

• R1 (stage 1, headroom): sample sd of the 64 ticket scores, and the minimum. Ticket structure is declared worth confirming iff sd > 0.0785 (the upper 95% χ²₆₃ edge of σ_probe = 0.0669) OR min < mean − 0.22 (expected null min −0.157, minus 2 sd of the min). Otherwise KILL before stage 2: results post records the distribution and the screen closes at ~1.5 GPU-h spent.
• R2 (stage 2, confirmatory): paired per-frame Δ (winner − stable-key) on complement core rows, bootstrap CI95. Ticket is REAL iff Δ ≤ −0.05 and CI95 excludes 0. The full-panel pooled number is quoted alongside for board continuity; a leaderboard row (draws/keying stated: ticket, sha-pinned) only if REAL.
• R3 (stage 3): pooled Δ (mean-of-top-10-tickets − banked mean-of-10 5.3645). Interesting iff ≤ −0.02 (beyond the tie band); either way record-only in this screen — mean-of-10’s row is not displaced without a paired follow-up.
• R4 (record-only, free from the dumps): (a) per-dataset per-ticket score matrix — do datasets disagree on the argmin ticket (the task-locality read; the paper predicts they do)? (b) dispersion-quartile geometry of the winner’s per-frame gain (the selection_ceiling_results.py quartile machinery); (c) per-step horizon profile of the gain.

No other numbers are read. R1’s kill line is the point of the screen’s staging: under the null the probe winner’s edge is frame-idiosyncratic luck and would evaporate on complement rows — we do not pay for stage 2 to learn what R1 already said.

Instrument (to land, oracle-gated, before launch)

bijou.eval gains a ticket noise mode (semantics frozen here; flag spelling is implementation’s): a tickets-npz path replaces noise_for_item’s per-frame keying with noise = tickets[draw], independent of the frame — reusing the batched draws-major tiling, --dump-draws, and the scoring path unchanged. Policy/report provenance carries the mode and the tickets-file sha256 (a ticket read must never pass as a stable-key read).

Oracles (abort-on-red before launch):

1. Contract: ticket mode at draws=1 reproduces bit-exact the direct sample_actions(noise=ticket) call on the same frame.
2. Ticket property: within one run, two different frames receive byte-identical noise for the same draw index (asserted in-process, not by construction).
3. Determinism: same tickets file → two runs byte-identical dumps.
4. Pooling reuse: the search-stage scorer, run over the banked stable-key full-panel npz, reproduces 6.5997 exactly; run over the banked drawsprobe npz per-draw, reproduces the 10 banked per-draw pooled MAEs of analysis__sigma_draw_direct.json exactly.

Cost gate and execution window

Stage 1 ≈ 1.5 GPU-h (draws-64 batched on 2,458 frames; microbench marginal ≈ 33 ms/frame/draw), stage 2 ≈ 0.9, stage 3 ≈ 2.9. Gate: 6.0 GPU-h total, local 1×H100 only, launched via run_detached.sh, GPU-free guard. Window: a quiet local-GPU window strictly after the tsens rungs complete and behind the already-queued selfsubgoal probe (#6) — this screen does not preempt anything pre-registered before it.

Caveats carried from the paper and from us

• A fixed ticket makes the policy deterministic — and can fail hard at unsearched positions; the panel cannot see either rollout property (the #16 offline-vs-rollout gap applies in full).
• The shared-ticket prior: the paper’s own shared-per-suite cells regressed. A null result here is informative, not a failure — it would close #1’s ticket rung and leave mean-of-10 as the flow family’s decode.
• Ticket effects, if REAL, bind to the teacher (30 NFE, unconstrained class until distilled); nothing here licenses a student claim — that is the escalation amendment’s job, gated on the student’s own draw-response pre-check.