Keyboard shortcuts

Press ← or → to navigate between chapters

Press S or / to search in the book

Press ? to show this help

Press Esc to hide this help

1. Inference-time noise-draw ensembling — flow side BANKED (mean-of-10 5.365 beats the AR anchor; fairness + energy-score reads in); open rung: the golden-ticket noise screen (pre-reg POSTED 2026-08-07, execution awaits a quiet local window)

Tag: noise-draws · idea #1 · index

  • Hypothesis: mean-of-N noise draws through the flow expert cuts panel MAE substantially in the unconstrained class (mainline measured 5.30°→2.88° on motion frames for a ft’d model, mean-of-10); the stage-2 flow-on-AR-trunk lineage (6.623 panel) should benefit similarly.

  • Expected effect: large on the flow lineage’s panel number; unconstrained-class only (charter §2) until distilled.

  • Cost: ~20 lines eval-side + one eval burst per N in {1,5,10}. Zero training.

  • Falsification: paired panel eval, same checkpoint, draws stated. Check unimodality of draws first (averaging multi-modal draws is wrong): per-frame draw spread on a few hundred panel frames. If mean-of-10 does not beat single-draw beyond the panel’s pairing noise, kill.

  • Open sub-question: an AR-family analogue (temperature/nucleus chunk ensembles, chunk-level medians) — separate screen.

  • Instrument note (2026-08-05): upstream already ships bijou.eval --sample-draws N (a16e65a) — verify its semantics (independent draws? mean-of-N in action space?) before writing any code; the pre-reg’s eval-side work may reduce to a flag.

  • Paired-analysis prediction (2026-08-05 ~20:15Z, before the draws-10 numbers land): flow’s deficit vs AR is a monotone horizon-divergence (crossover at step 2; +1.2 by step 40 — post). If per-draw spread grows along the horizon, mean-of-N should close the late-horizon deficit preferentially: chunk_mae moves a lot, first_mae barely. Score the draws-10 run per-step, not just pooled.

  • Fairness reads pre-registered (2026-08-05 ~22:1xZ, Amendment 1), from the owner’s 21:49Z is-MAE-unfair-to-flow challenge: instrument finding — per-draw chunks never left the process (--dump-predictions stores the post-average), so bijou.eval --dump-draws landed (tests + bit-exact scoring oracle), a 2,458-frame stride-7 probe plan + launcher (~/eval_flow80k_drawsprobe_dump.sh, ~30 min 1×GPU) is frozen, and fontaine/scripts/draws_fairness.py computes the three pre-declared reads (mean-of-draws / best-of-N / dispersion- conditioned deficit; degenerate draws=1 validation reproduces 6.6232 exactly). Launch at the first quiet local-GPU boundary after the draws chain.

  • FIRST DRAWS-10 PANEL READ (2026-08-05 23:31Z, run 2 of the chain, full 25.8k-frame panel): chunk_mae 5.365 / first_mae 1.424 vs single-draw 6.6232/1.9331 (−19%/−26%) — mean-of-10 flow beats the AR-100k anchor (5.8026/2.1431) on both columns (unconstrained class: 10× NFE; charter §2 caveat until distilled — #12 SnapFlow leg). Banked-prediction check: “chunk_mae moves a lot” ✅; “first_mae barely” ❌ — first_mae moved 26%, so the gain is NOT purely late-horizon; per-step decomposition promoted to a required read in the results post (after runs 3–5 + the fairness probe).

  • Read 4 pre-declared (2026-08-05 ~22:5xZ, Amendment 2, from the lit slice): the energy score (RMS-normalized, valid- element mask, N=10 vs AR’s degenerate N=1) — a strictly proper scoring rule where neither mode-averaging nor scatter wins for free; the principled middle between MAE and the best-of-N oracle bound, and the candidate distributional column for ranking flow arms on the comm holdout. Source: Energy Policy (2510.12483) trains on it; we take the metric, computable on CPU from the same --dump-draws npz. read4_energy_score in draws_fairness.py + degenerate draws=1 validation must land BEFORE the probe npz is opened (next CPU work item alongside the E4B launch checklist).

  • FAIRNESS READS IN (2026-08-06 ~07:4xZ, results) — the unfair-penalty signature FIRED on every declared criterion: E1 gate passed (draw-0 drift 0.0145 < 0.05); dispersion-quartile deficit monotone 0.23→0.60→0.87→1.42 (Spearman +0.13, q4 = 6.2× q1); best-of-10 3.8597 is 2.01 BELOW AR’s paired 5.8680; energy score (read 4): flow 5.9308 vs AR 8.7696 — flow wins the proper score while losing single-draw MAE. Honest residual: deficit positive even in the tight quartile (+0.23), win rate < 0.5 everywhere — partly artifact, not wholly; ES is now the candidate distributional column (owner decision to adopt). σ_draw direct = 0.02367 SUPERSEDES the 0.0159 pin (floors 0.045/0.05 still bind → both live bands numerically unchanged; sigma_draw_direct.py, cross-estimator inside the χ²₉ band).

  • Golden-ticket noise search (lit slice 2026-08-06, 2603.15757 “You’ve Got a Golden Ticket”): a single searched noise vector (Monte Carlo over candidate tickets, weights frozen, inference-only) improved 38/43 tasks across diffusion/flow policies incl. SmolVLA-LIBERO, with gains growing at fewer solver steps. Their search needs env rollouts; our panel gives the offline criterion they lack — score M candidate tickets by probe-subset MAE via sample_actions(noise=...) (the hook already exists), then validate the winner on the full panel. Caveats to carry: their LIBERO-Spatial cell regressed (−3%), tickets showed limited cross-task universality, and a fixed ticket makes the policy deterministic. Pairs with #12’s 1-NFE distill (fewer-steps trend) and with mean-of-N (ticket vs mean-of-10 vs both). Cheap eval-side screen; needs its own pre-reg before any number is read. Correction hooks (papers-page deep read 2026-08-07, page): the 38/43 figure was v1 — v3 reports 46/51; “Spatial −3%” was imprecise — per-task tickets always gain (+13 Spatial), it’s the single shared ticket per suite that regresses (−2.6 to −12). Design note banked: the 1-NFE student’s draw collapse may have shrunk the searchable ticket space — screen the teacher’s noise space first, or verify the student still responds to noise.

  • Golden-ticket screen PRE-REGISTERED (2026-08-07 ~18:0xZ, pre-reg): teacher-first, M=64 i.i.d. tickets scored in ONE batched draws-64 eval on the drawsprobe_s7 subset (the “draws” are the tickets — the batched-draws merge makes the search ~1.5 GPU-h); frozen null from banked data (σ_probe 0.0669 per-draw pooled spread; null min₆₄ = mean − 0.157); staged kill line BEFORE the confirmatory full-panel read (winner judged on complement rows only, adopt floor −0.05); “both” cell = mean-of-top-10-tickets vs banked mean-of-10; free R4: per-dataset argmin tickets = the task-locality read the paper predicts. Honest prior against: a panel-wide ticket is the paper’s shared-ticket regime (regressed in all 3 LIBERO suites). Instrument = a “ticket” noise-key mode in bijou.eval (to land oracle-gated; 4 oracles frozen in the post). Gate 6 GPU-h; window strictly after tsens rungs + behind the selfsubgoal probe.

  • Batched draws MERGED + speedup measured (2026-08-07, main-sync review): the owner’s 2ee2be5 integrates all draws in ONE solver call at draws×B; same-harness microbench on the leaderboard configs: mean-of-N now costs single-draw latency — teacher Heun-30 mean-of-10 single-stream 11,283.6 → 1,245.0 ms/frame (9.1×), student 1-NFE mean-of-10 277.9 → 111.2 (2.5×); draws=1 control cells reproduce ≤0.3%. The unconstrained-class caveat on mean-of-10 rows is now almost purely about panel semantics, not deployment cost — the deployment argument for draws is live.

  • **Noise-space steering ladder READ (2026-08-07 ~20:2xZ, papers page — DSRL 2506.15799

    • LP-DS 2606.01151 + FRS 2606.13675):** the rung LAFM only named is now mapped. DSRL: RL with the noise AS the action (dual critic Q^A→Q^W distilled through the frozen decoder, noise aliasing for sample efficiency; steered a real-world π₀/DROID checkpoint, black-box access only). LP-DS: names the failure mode — unconstrained noise search drifts off the N(0,I) support and the frozen decoder answers with mode collapse; fix = state-conditioned residual w = ε + Δ_θ(s) inside a Lagrangian trust region (real Franka 33/40 vs 18/40 frozen; preserves action entropy where DSRL collapses it). FRS: reverse-ODE the flow to recover the noise behind a reference action, then DSBC-distill 10 successful trajectories into a tiny noise policy (<1 min, ~1 GB, up to +95% absolute on real tasks; explicitly inapplicable to AR policies). Consequences banked: (a) stage 1 is safe by construction (i.i.d. prior candidates can’t go off-manifold) but any CEM escalation pre-reg MUST carry LP-DS’s trust-region clause (‖ε‖ near the √300 ≈ 17.3 typical shell); (b) R4 gains a third interpretation — per-dataset argmin structure is the offline shadow of what DSRL/LAFM exploit online; (c) the rig-time story from the 18:5x owner exchange now has published shapes one and two rungs up (DSRL needs rewards + rollouts → gated on #16’s rig benchmark; DSBC needs 10 reference demos only → banked as a #16 lever). Whole ladder stays gated on the screen’s R1/R2 verdicts — no new arm from this read.

  • Noise-steering part II read (2026-08-07 ~20:3xZ, papers page — PAINT 2606.19774 + UniSteer 2605.10821, the two hooks the ladder page banked): PAINT uses initial-noise selection for async execution (invert prefix, repaint suffix — fed #22, not this ladder); UniSteer fills the FRS↔DSRL gap — human corrections fixed-point-inverted into noise targets, SFT-then-RL on a tiny noise actor over frozen π₀ (20%→90% avg in ~66 min; OOD 100% where DSRL drops to 0–25%). Banked here: (a) PAINT’s locality assumption (prefix of ε controls prefix of chunk under OT flow matching) is a probeable property of our teacher — fix a draw, perturb ε[:d], measure chunk movement; free record-only diagnostic that would mechanistically support (or salt) reading structure into [50,6] tickets; (b) the inversion catalogue is three deep (FRS full reverse-ODE / PAINT backward-Euler prefix / UniSteer per-step fixed-point — the last wins its head-to-head 8/8 vs 4/8 at 500× less wall clock, the numbers-backed default if inversion ever lands in bijou.eval); (c) UniSteer acknowledges its noise targets drift slightly off-prior — LP-DS’s warning surfacing in practice, third citation for the CEM trust-region clause. No new arm, no gate change; stage 1 R1/R2 still gate the entire ladder.

  • 2026-08-08 16:0x–17:0xZ (work session): rung-2 instrument + preflight LANDED, one pre-reg amendment earned by the apparatus. --noise-ticket-map in bijou.eval (per-dataset routing off the committed stage-01 map; _ticketmap policy suffix; routed provenance in report + predictions npz; 15 CPU oracles in tests/test_ticket_map.py); preflight apparatus per the pre-reg’s stage-2 item 5 (committed 2-dataset ticket-2 plan + t2-only bank + abort-on-red adjudicator + launchers for preflight/stage-2/seating — the seating arm pins --noise-key index because the banked 5.3645 mean-of-10 row predates --noise-key). Amendment 1: the adjudicator’s first real run caught the committed map enumerating only the probe universe (792 datasets) while the panel decodes 878 — fixed with a panel-total extension (86 added → 33 per the pre-reg’s own non-qualifying rule; restriction to the 792 must reproduce the pre-registered sha exactly, enforced abort-on-red; amendment posted before stage 2). Stage-2 + seating GPU runs open post-60k-close behind the preflight green json; the read script is the remaining CPU cell.

  • 2026-08-08 18:3x–19:4xZ: rung-2 stage-2 FALSIFIED out-of-sample (results): Δ_route +0.129 [CI95 +0.060, +0.205] entirely above zero on 6,014 held-out complement rows — the in-sample −0.60 probe delta inverted; per-dataset argmin at ~6–20-frame cells memorizes its cell. Board row stays global ticket 33. Measured prior banked for the whole specialization ladder: transfer-at-selection-time is now the mandatory test. Record-only lead: routing wins chunk steps ~1–8, loses ~15+ (a chunk-position noise policy would be a different, cheaper axis).

  • 2026-08-08 ~23:1xZ: SEATING CONFIRMED — the board row moves to the top-10 ensemble (results §seating): paired Δ −0.17358 [CI95 −0.19556, −0.15214] on 17,204 core frames (clustered CI agrees; first mirror −0.041). Leaderboard row 2 is now mean-of-top-10-tickets 5.1847/1.3831 — best chunk AND first on the board; ☆ gap 0.37 → 0.18. The read survived its own base-equality abort first: the re-run missed the banked first_mae by −1.27e-4 (4dp gate), diagnosed as benign numeric drift from the batched-ensembling merge (state-copy cells exact 878/878, bijou cells ≤1.7e-3 vs draw-dispersion ~0.05–0.5 — resampling excluded; Amendment 2 on the pre-reg, analysis__seating_base_equality_diag). The 1-NFE student’s distillation target moved: it now trails the teacher’s best ensemble by 0.18 at 30× less compute — re-screening the student’s ticket response is the natural next rung.

  • 2026-08-09 — score-before-integrate instrument banked (ForesightFlow page, 2606.04968): the 1-NFE endpoint preview (x₀ + v(x₀,0,c)) ranks flow candidates at Kendall τ ≈ 0.83 vs full integration, keeping ~97% of the best-of-K gain — a measured license to score draws cheaply before deciding which to integrate; composes with the ELASTIC dispersion-gated allocation rung. Also the K-sweep caution for any selector rung here: an external critic ranked its policy’s own candidates at chance; jointly-trained scoring did not.

  • 2026-08-09 ~14:3xZ — the boundary term gets a shape and a free read (SEAM page, 2607.04609): the SDN read’s null was within-chunk (our ODE draws uniformly smooth); SEAM names the term that read cannot see — cross-chunk mode incompatibility from independent Gaussian latents — and fixes it with a closed-form λ(1−t) nudge toward the previous chunk’s unexecuted tail (+1% cost, jerk −28%, success preserved). Banked hook, CPU-only: measure our own boundary incompatibility from banked panel npz (tail-vs-head disagreement on the overlap of temporally adjacent panel frames, the SDN-read pattern). Panel-blind caveat carried: an offline MAE panel scores chunks independently and would price a steered chunk worse — this term is #16/rollout territory by construction; details on #22.

  • 2026-08-09 ~15:2xZ — the boundary read EXECUTED: the cross-chunk term is real and noise-dominated (results): at dt ≤ 5 (near-identical observations, matched horizons), fresh-noise flow draws disagree with each other by 6.04 MAE — vs 2.66 for the mean-of-10 ensemble, ~2.7 for greedy AR, and 2.07 for a single shared noise ticket. The draw dispersion the within-chunk SDN read could not see shows up whole at the seam, and noise coupling deletes it. Record-only; any deployment-side use is #16/#22 territory (full record).

  • 2026-08-09 — ladder footnote (π-StepNFT page, 2603.02083): online RL explores through per-step SDE noise blindly and lets binary outcomes sort it — the same channel our screen measured as directional and context-interacting (interaction 39.4% vs noise main effect 1.4%). A ticket-informed exploration prior is an obvious unpublished hybrid; named on the ladder, no rung, everything still gated as before.