SnapFlow results: 1-NFE distillation holds the panel — single draw beats AR at one expert eval

2026-08-06, 15:2xZ. Results for the SnapFlow pre-registration + Amendment 1 (σ_draw finalization: adopt-signal iff chunk_mae ≤ 6.7732). Run fontaine_flow_snapdistill_h1024_30k_1xh100 (seed 1), self-distilled from bijou_flow_artrunk_h1024_40k_ddp2/step_080000 with the φ_s target-time extension, trunk frozen. All frozen reads through fontaine/scripts/snapflow_results.py (banked oracle-before-data 09:xxZ, five oracles green before any endpoint byte existed); report analysis__snapflow_distill_30k_k4l2.json. Keying: v1 panel, index keying — the registered comparators predate the #18.2 stable-key adoption; in-flight reads finish as registered (record-only, stated not hidden).

Gates (all passed, banked at launch)

• Gate (a) zero-init identity oracle: 6/6 bit-exact — step-0 extended model ≡ teacher.
• Gate (b) E1-style drift: step-0 Heun-30 s=t on the stride-7 subset reproduced the banked flow npz, frame-MAE drift 0.01451 < 0.05.
• @10k record-only 1-NFE probe: 5.9222 / 1.8193 — kill line (teacher probe 6.6755 + 3.0 = 9.6755) passed by 3.75; at one-third training the 1-NFE student already beat the teacher’s own Heun-30 probe read (6.676/1.928).

The endpoint reads (30k, full 25,800-frame panel)

State-copy control rows byte-match the banked values on all three evals (11.7848 / 2.6202) — same panel, same join, quotable.

Full HTML eval reports (per-repo tables + worst-frame galleries): single draw · mean-of-5 · mean-of-10 — teacher comparators on the reports index.

Verdict against the pre-registered lines

• Primary (expectation 2): PARITY-ADOPT. 1-NFE single-draw chunk_mae 5.6036 ≤ 6.7732 — the adopt-signal fires, with 1.02 to spare vs the teacher’s own Heun-30 (the pre-reg’s modal outcome was “parity or slightly better”; this is better by 15%). Not falsified, not a miss.
• Deployment headline (expectation 3): FIRES. Mean-of-10 @1-NFE 5.3675 ≤ 5.8026 — the draws win survives distillation at one-thirtieth the compute (10 expert evals vs 300), landing just under the modal band [5.4, 5.6] and matching the teacher’s own mean-of-10 (5.365) to 3 dp. The charter §2 cost caveat on the draws result closes.
• Grounding edge (expectation 4): SURVIVES. first_mae 1.7039 ≤ 1.9831 — and improves on the teacher’s single-draw 1.9331.
• Kill line: never threatened (probe passed by 3.75).

The sharper headline: single-draw 1-NFE already beats AR

The pre-reg asked whether mean-of-10@1-NFE could hold the beat-AR read at ~10-expert-eval cost. The answer is stronger: a single 1-NFE draw (ONE expert eval) scores 5.6036 — already below the AR anchor 5.8026. The mean-of-N machinery is now an optional +0.24 refinement, not the price of admission.

Draw diversity: mostly collapsed into the mean — and that is fine

Averaging gains: teacher −1.258 (6.6232 → 5.365 over 10 draws); student −0.236 (5.6036 → 5.3675). The mean-of-5 point makes the shape unambiguous: student 5.3918 at 5 draws — ~90% of its total averaging gain banked by draw 5, the same fractional shape as the teacher (87% by draw 5: 6.6232 → 5.5235) but at one-fifth the amplitude. The student’s mean-of-5 already beats the teacher’s mean-of-5 (5.3918 vs 5.5235), and its mean-of-10 lands 0.03 below the pre-registered modal band [5.4, 5.6] (inside_modal_band: false in the report) — the band was projected assuming teacher-level residual draw variance, and the student has much less.

This is exactly the fairness-probe finding operating in reverse: the 1-NFE endpoint approximates the posterior mean (chunk MAE rewards mode non-commitment), so the distilled student banks most of the ensembling gain in every single draw, leaving little residual draw spread to average over. The distillation did not preserve the teacher’s draw distribution — it compiled the mean of it. For deployment-style chunk-MAE that is the profitable direction; anyone needing mode diversity (best-of-N search, multimodal planning) should stay on the Heun teacher, whose best-of-10 bound (3.8597 on the probe subset) has no student counterpart.

Per-step horizon read (addendum npz, pre-registered)

The addendum npz eval re-ran the primary with --dump-predictions (the chained stage-4 evals dumped JSON only); npz-pooled 5.6036/1.7039 matches the chained JSON to 2e-05 — same eval, byte-consistent. The pre-registered question — does the 1-NFE student degrade faster along the horizon than the Heun-30 teacher? — answers no, the opposite: the student sits below the teacher at every one of the 50 horizon steps (crossover_step: null), and the per-step delta widens monotonically from −0.229 at step 1 to −1.554 at step 50. The cumulative first-k curve never crosses either. Face-value this is the mean-collapse reading again: later horizon steps carry more draw spread, and a mean-valued prediction profits most exactly where spread is largest — so distillation compressed late-horizon error hardest. The 2026-08-05 paired finding (flow diverges from AR along the horizon) does not transfer to student-vs-teacher.

Panel-v2 descriptive column

Descriptive only (v1 index keying stays the registered read, per the transition convention): on panel-v2 stable keying the student scores 5.6711 / 1.7059 vs the teacher’s banked 6.7151 / 1.9453 — margin −1.04, matching the v1 margin. The parity read is keying-robust.

Cost (record actual, per pre-reg)

30k steps in ~4.5 h wall on 1×H100 (08:43Z → 13:14Z, 0.48–0.51 s/step) — under half the 12–20 h budget. Endpoint evals: draws-1 ~28 min, draws-10 ~25 min at ~1,100 f/min, draws-5 ~26 min; npz addendum re-run ~27 min. Final in-run s=t drift 7.6601 — the velocity-mode read stayed ~1σ high all run and stayed deconfirmed as a 1-NFE proxy (the @10k probe called the endpoint correctly).

What adoption means (owner decision)

The instrument’s assembly (verbatim from the report): parity-adopt — “ADOPT-SIGNAL + DEPLOYMENT HEADLINE: mean-of-10 @1-NFE beats the AR anchor at ~10-expert-eval cost — the charter §2 cost caveat on the draws win closes.” Concretely, the proposal on the table: the 1-NFE student becomes the deployment-class inference config for this lineage — single draw (1 expert eval) as the latency floor, mean-of-10 (~10 expert evals, still 3× cheaper than one Heun-30 draw) as the quality mode. Owner sign-off requested; until then the numbers stand as measured. Proposed follow-ons stay as queued ideas: #1 Golden-Ticket noise search now has a 1-NFE substrate (panel-side search is 30× cheaper), and the stage-4 eval default (--noise-key) flips per #18.2 now that the chain is done.