Noise-space steering III: attribution, and a selector that needs no judge

Read 2026-08-08 (standing lit slice, the GPU-busy window while golden-ticket stage 3 and the molmo2 draws arm run to their ~08:1xZ landings). Two sources, found by scanning for post-Golden-Ticket work on initial-noise choice: Noise-Space Attribution and Control of the Chunk-Boundary Artifact (2603.11642) shows what kind of variable initial noise is — directional, context-dominated, and only controllable when the sampler keeps the noise→action path intact — and SDN — Self-Improving VLA Policies: Selected Diffusion Noise for Spurious-Robust Action Smoothing (2606.14084) ships a verifier-free per-step noise selector (12 candidates, no external judge) worth +8–18 pp success on π₀/GR00T. Fed: #1 noise-draw ensembling (three priors written into the upcoming per-dataset-tickets pre-reg) and #19 AR sampled-draws (a concrete, label-free selector to hold against the banked selection ceiling — its smoothness half is computable on our already-banked draw stacks for free).

Why these two, today

The ticket screen just adjudicated: one searched noise vector is REAL (complement Δ −0.924), the effect is directional not norm, and the noise-structure ladder’s entry condition is met. The next rung — per-dataset tickets — gets its pre-reg after R3 lands today. These two papers are the freshest evidence on the two questions that pre-reg has to take a stance on: how task-local are noise effects (attribution paper: very — with numbers), and what selects noise at deployment when there is no panel to search against (SDN: the model itself, plus a smoothness prior).

Attribution: noise is a mechanism variable, but only through an intact path (2603.11642)

The paper’s object is the chunk-boundary artifact — the jerk spike where one action chunk hands off to the next (jerk = second difference of the action sequence; their boundary metrics contrast jerk at chunk phases {0,1} against interior phases). The standard story treats it as stochastic debris. Their claim: hold the observation fixed, vary only the latent noise, and the artifact level is a systematic function of the noise draw — attributable, steerable, and causally linked to task success.

Three results carry the page:

1. The information-path result. Same Diffusion Policy checkpoint, three samplers: DDPM’s per-frame correlation between a noise direction and boundary jerk is ~0.11/0.05; zero-variance DDPM — determinism alone — is unchanged; DDIM (η=0) jumps to 0.96/0.93. Removing per-step randomness is not what restores control; DDPM’s Markovian reverse updates remix the signed direction of the initial noise even when deterministic. Only when the sampler preserves the noise→action map does directional structure in noise space survive to the output. Transfer to us: our teacher decodes a deterministic Heun-30 ODE — the flow analog of the intact-path regime, and their OpenPI main experiments are flow-matching policies (π₀/π₀.5-LIBERO). This is the published why behind our screen even finding structure: a 64-ticket search would have read null through a path-breaking sampler at the same checkpoint quality. It also sharpens the ladder’s future student rung: any decode change that breaks the path (or an escalation to stochastic samplers) forfeits the channel, separately from the draw-spread collapse we already measured on SnapFlow.

2. The locality numbers. 192 contexts × 16 shared noises: the variance decomposition of boundary gap is 59.1% context main effect, 1.4% noise main effect, 39.4% interaction. The globally best noise is optimal in only 3.1% of contexts; per-context selection removes 93.8% of the mean gap. This is the quantitative form of Golden Ticket’s shared-vs-per-task table, measured on a different artifact with a different criterion — and it is the prior the per-dataset-tickets pre-reg should encode: expect interaction-dominated structure (a shared ticket captures the 1.4% main effect; per-dataset search is aiming at the 39.4%). Our R4a read (per-dataset per-ticket argmin disagreement, banked at stage 1) is exactly this decomposition’s cheap shadow on our own panel.

3. Steering moves task outcome, in both directions. Local directional search (12 random unit directions, probe at α=±0.5, pick the best separator) finds near-linear jerk response (r 0.90–0.97). Steering along it at held-out matched continuations — same rollout prefix, paired futures, only boundary noise differs — moves success 0.033→0.717 (+68 pp, n=60 pairs) in one context; and the sign of the useful direction flips by context (some contexts want higher artifact). No fixed direction transfers across contexts; their adaptive online version is honest about being not-yet-stable.

The caveat that matters for us: their criterion is a rollout property (boundary jerk, success under execution). Our panel is offline chunk MAE and cannot see chunk hand-offs at all — a ticket that wins our panel could carry arbitrary boundary artifact (the #16 offline-vs-rollout gap, in its sharpest form yet). Banked as a named unknown on ticket 33: if the flow decode ever reaches a rollout, measure boundary jerk ticket-vs-stable-key before trusting the panel win.

SDN: selection without a judge (2606.14084)

SDN is test-time-only, weights frozen, and needs no external evaluator — the niche our #19 selection-ceiling read exists to bound. Per inference step it draws 12 candidate noises, decodes each, and picks in two stages:

1. Contrastive grounding. Decode the same candidates under an object-masked observation (zero out the target object’s pixels). Score each clean candidate by k-NN distance to the masked-decode action set minus distance to the clean set — keep the top 5 that look least like what the policy does when it cannot see the object. This is a self-diagnostic: π₀ drops 63.9%→24.4% under masking, so masked decodes are a live sample of the policy’s ungrounded-shortcut behavior.
2. Kinematic stability. Among survivors, pick the minimum-jerk trajectory (RMS third difference over an extended chunk — decoded past the executed horizon to expose delayed oscillation).

Numbers: π₀ SimplerEnv 63.9→72.6 avg (+8.7 pp), GR00T-N1.6 49.5→54.5, real-robot ALOHA +18.3 pp avg; jerk down 5%. Cost: ~245 ms/step on a consumer GPU — the 12-candidate decode plus one masked forward.

The ablation that matters: smoothness-only gets +16.7 pp of the +18.3 real-robot gain; grounding-only +13.3; both +18.3. The cheap, judge-free, second-forward-free half — pick the smoothest of N draws — is most of the method.

Transfer to us, concrete and free: we hold banked --dump-draws stacks (teacher drawsprobe draws-10, the 64-ticket stack, and the molmo2 draws10_t1 full stack landing today). Min-jerk selection is a pure function of the draw stack — no forwards, no labels. A record-only CPU read can place “pick the smoothest draw” on the selection-ceiling ladder (single draw → mean-of-N → jerk-pick → oracle best-of-N) for both families, before anyone builds a deployment selector. If jerk-pick recovers a nontrivial slice of the oracle gap on our panel, #19’s escalation has a published, verifier-free candidate; if it recovers nothing, the SDN prior is falsified for our stacks at table cost.

Executed same session (jerkpick_selector_results.py, oracle-green; results in reports/analysis__jerkpick_selector.json): a clean two-family split. On the flow teacher’s fresh-noise draws-10 stack jerk-pick is null on every diagnostic — oracle agreement 10.5% vs a 10% null, Spearman(jerk, MAE) +0.13, −2.3% of the oracle gap recovered (i.e. slightly worse than an average single draw); the ticket-64 stack reads the same. Heun-30 ODE draws are uniformly smooth — the criterion has nothing to grip. On the AR q4 stacks the prior is real but small and temperature-monotone: 5.6% / 7.5% / 20.9% of the oracle gap recovered at T = 0.5 / 0.7 / 1.3, Spearman +0.36 — jerky sampled-token decodes really are bad decodes, and the wilder the temperature the more a smoothness filter rescues. On neither family does jerk-pick approach mean-of-N, so the family decodes stand. SDN’s smoothness prior: falsified for our flow stacks, confirmed-in-miniature for AR — consistent with SDN’s own setting (stochastic diffusion policies, not deterministic ODE decodes). The molmo2 draws10_t1 stack gets the same read when the #19 arm lands. The grounding half is heavier (one masked forward per frame, and our observation masking would need object boxes we don’t have) — noted for #11 visual grounding, not queued.

Caveats: SDN’s gains are success-rate under rollout on SimplerEnv / ALOHA — the same offline-vs-rollout gap applies in reverse (our panel may under- or over-credit a smoothness pick); their k-NN grounding is tuned (N=12, k≤10, M=5) with diminishing returns past N=12; and jerk-as-criterion overlaps with what our chunk MAE already partially rewards, so the honest expectation for the offline read is modest.

What moved where

• #1 (noise ladder) — three priors banked for the per-dataset-tickets pre-reg: (i) interaction-dominated locality is the published expectation (1.4% noise main effect vs 39.4% interaction); (ii) the channel exists because our decode is path-intact — any sampler change re-tests the whole ladder; (iii) boundary artifact is a named unknown of ticket 33 the panel cannot see (rollout-gated read banked).
• #19 (selectors) — jerk-pick joins the selector shortlist with published rollout numbers and a zero-cost offline evaluation path on banked stacks; new record-only analysis item queued.
• #11 (grounding) — SDN’s masked-decode contrast noted as a self-diagnostic pattern (needs object masks we don’t have; not queued).