Pre-registration: state-reliance probe (masked-state panel subset)

2026-08-06 ~03:1xZ. Immutable once posted. Ideas #11 rung (a) — the cheapest falsification of the state-dominant-bias mechanism named by the 02:5xZ lit slice (ReViP; the causal-confusion line 2506.23944, 2509.18644: proprioception is the shortcut, vision is what generalizes). Instrument landed and gated this session; execution queued for the first quiet GPU window.

Question

How much of each banked policy’s panel performance rides on the proprioceptive state input — and does aux supervision change that reliance? The mechanism under test says aux-off models lean harder on the state shortcut; it is a candidate explanation for BOTH the standing grounding gap (first_mae barely ahead of state-copy) and B’s pending flag (aux-off first_mae 3.5009 WORSE than state-copy 2.6202 @40k, paired reads land ~04Z).

Instrument (landed this session, check.py 221 green)

bijou.eval --mask-state: the bijou policy’s items are rebuilt with observation.state := state_mean (per-dataset), so the normalized soft state token collates to EXACTLY zero — zero state information at in-distribution magnitude, prompt structure untouched. Applied in BijouPolicy.apply_overrides, so the narrated pass (if any) sees identical inputs. The policy name gains _state-masked (the _drawsN precedent: a diagnostic read must never pass as a deployment read); report JSON, npz scalars and the report banner all record mask_state. Baselines are deliberately NOT masked — state-copy stays the intact-state reference — and truth actions are untouched. Tests: exactly-zero collation, at-mean identity, no-mutation (tests/test_mask_state.py), parse guards (--mask-state without --checkpoint, or with --smolvla, dies at the parser).

Design — 4 masked subset evals, zero intact evals

Subset (frozen artifact): plans/holdout_curated_v0_k4l2_stateprobe_q4.json — every 4th core entry (positions ≡ 0 mod 4) of the frozen k4l2 plan, 4,301 rows, labeled panel dropped (headline MAE only). sha256 876c39c8fe2b3cb16945a40c35ec157c907b4f7417e7dfd0b6cf46dd47355ef5, builder + oracle fontaine/scripts/state_probe_subset_plan.py. A strict row-subset of the frozen plan ⇒ every banked full-panel npz pools intact-side numbers over exactly these rows (the panel-v2 re-pooling precedent) — the masked runs are the only GPU work.

Arms (one masked subset eval each):

Each run: --sample-plan <subset> --mask-state --dump-predictions --output-json, panel corpus/filters verbatim from the parent plan (holdout 0.1, split-seed 0, fps 30, camera-counts 1 2). The flow arm’s noise is identical to the banked heun-30 npz by construction: noise-key index keys to corpus-relative frame indices at unchanged corpus composition, same seed 0 — masked vs intact differ in the state token ONLY.

Intact side (pooled, no GPU): per-row chunk/first MAE from the banked npzs — AR-100k + flow-80k in reports/, B pulled 02:09Z, A-s0 from its ~04Z panel npz. The probe is blocked until A-s0’s npz is pulled local.

Frozen reads

Per checkpoint, per column (chunk_mae, first_mae): Δ = masked − intact, paired per-row over the 4,301 subset rows, seeded bootstrap 95% CI (seed 0, 10,000 resamples).

• Primary: D = Δ_first(B) − Δ_first(A-s0). The state-dominant-bias hypothesis predicts D > 0 (aux-off leans harder on state). Supported iff the bootstrap CI on D excludes 0 AND D ≥ 0.05 first_mae degrees; anything smaller is not supported regardless of significance.
• Secondary: the same difference on chunk_mae; per-checkpoint absolute reliance (all four Δs with CIs); AR-100k vs flow-80k reliance compared; masked-model levels vs intact state-copy / state-copy-norm pooled on the subset — does vision alone still beat the trivial baselines?

Execution oracles (abort on failure): (1) each masked run’s state-copy and state-copy-norm summaries must reproduce the pooled-from-banked values on the subset rows exactly — proves row pairing AND that masking touched only the bijou policy; (2) report JSON records mask_state: true and the policy name carries _state-masked; (3) subset plan sha256 matches the frozen value.

Stated limitation

Full masking is out-of-distribution — training never masked state, so each Δ conflates “information lost” with “input novelty”. The PRIMARY read subtracts the common effect: B and A-s0 share corpus, recipe, seed and architecture, differing in aux supervision only, so D isolates the aux-linked component. Absolute Δs are quoted as descriptive, not causal. If D is ambiguous, a shuffle-control rung (state permuted across rows — in-distribution marginal) is the named follow-up, not silently added.

Branch rules

• Supported ⇒ ideas #9’s state-DROPOUT train-time arm is promoted to its own pre-reg (the paired intervention; the literature’s lever); ReViP-style modulation stays the heavier architecture arm behind it.
• Not supported ⇒ state-dominant bias is dropped as the explanation for B’s flag; the grounding gap keeps its other candidate mechanisms (re-anchor, acuity — #11 main line).

Numbered expectations (banked before data)

1. Every checkpoint degrades under masking: Δ_chunk > 0.5 on all four arms (state is a first-order input everywhere) — confidence high.
2. Masked first_mae lands above intact state-copy’s subset first_mae for every arm (nothing fully substitutes proprioception at the chunk’s first frame) — confidence medium.
3. D > 0 — this is the hypothesis under test, not a prediction we’d bet the arm on; the probe exists because either sign is informative.

Cost & scheduling

4 × ~25 min (4,301 frames at ~170/min) ≈ 1.7 GPU-h total + CPU pooling. Venue: first quiet GPU window (local after the draws chain + fairness probe, or box between the control evals and the E4B launch) — never co-located with a pre-registered eval (charter §3). Blocked on: A-s0 panel npz (~04Z). Panel convention: v1 subset (this artifact); if the panel-v2 amendment is approved before execution, the v2∩subset column is quoted alongside (CPU-only re-pool, same npzs).