State-reliance probe results: SUPPORTED — aux-off leans harder on the state shortcut

2026-08-06, ~06:1xZ. Results for the pre-registered state-reliance probe (pre-reg, ideas #11 rung (a)). Four masked-state subset evals (4,301 frozen rows, plan sha256 asserted at launch and at read), intact side pooled from the banked full-panel npzs — zero intact re-evals. Analysis by the oracle-gated instrument fontaine/scripts/state_probe_results.py (output analysis__state_probe_q4.json, seeded bootstrap, deterministic; oracle mode passed before the real read: degenerate all-zero, synthetic known-effect + common-effect cancellation, misaligned-index abort).

Headline

The pre-registered primary read fired on the SUPPORTED side: D = Δ_first(B) − Δ_first(A-s0) = +0.702 first_mae degrees, 95% bootstrap CI [0.498, 0.916] (paired per-row double difference, n = 4,301) — 14× the pre-registered 0.05 support threshold, CI well clear of zero. The aux-off model loses more when proprioceptive state is masked: aux-off leans harder on the state shortcut. The secondary chunk-MAE read agrees in sign and significance: D_chunk = +0.389, CI [0.106, 0.674].

The state-dominant-bias mechanism named by the 02:5xZ lit slice (ReViP; the causal-confusion line 2506.23944, 2509.18644) survives its cheapest falsification attempt — and B’s box-batch flag now has a supported mechanism: B’s better intact first_mae (3.43 vs A-s0’s 3.87 on the subset) is bought with heavier state reliance, not better vision. Combined with the box-batch primary (aux-off +0.462 worse on chunk), the picture is coherent: aux supervision shifts the representation toward visual evidence; removing it lets the policy fall back on proprioceptive extrapolation, which helps the first frame and hurts the chunk.

(Subset levels read ~0.6–1.3 higher than the full panel — the every- 4th-core-row systematic sample is slightly harder than the full core set; state-copy shifts the same way, 12.36 vs 11.78. Everything above is paired within the identical 4,301 rows, so all deltas are internally consistent.)

All three banked expectations came true

1. Every checkpoint degrades massively under masking — Δ_chunk +15.3 to +16.4 on all four arms, ~30× the pre-registered 0.5 floor. State is a first-order input everywhere.
2. No arm’s masked first_mae beats intact state-copy — masked first lands 23.7–24.1 vs state-copy’s 2.43. Nothing in the learned stack substitutes for proprioception at the first frame.
3. D > 0 — the hypothesis under test, now supported with a CI that excludes zero by 10×.

Reading the absolute Δs honestly

The pre-reg’s stated limitation stands: full masking is out-of-distribution (training never masked state), so each absolute Δ conflates “information lost” with “input novelty” — and the masked levels (~23–26 chunk MAE, roughly 2× worse than state-copy) say the models are severely destabilized by the OOD zero token, not merely deprived of one input. The absolute reliance numbers are descriptive only. The primary read D subtracts the common OOD effect: B and A-s0 share corpus, recipe, seed, and architecture, differing in aux supervision alone, so the +0.702 isolates the aux-linked component. One secondary note: AR-100k shows +1.06 more chunk-reliance than flow-80k under identical masking (16.38 vs 15.32), while flow leans slightly more at the first frame — quoted without interpretation.

Execution oracles (all passed at read time)

Per arm: masked run’s state-copy AND state-copy-norm prediction arrays byte-match the banked full-panel arrays on the subset rows (pairing + mask isolation proven bitwise); truth/valid byte-identical; report JSON records mask_state: true; policy names carry _state-masked; recomputed masked pooled chunk/first reproduce each report’s summaries (<5e-3); subset plan sha256 matches the frozen value; cross-arm row identity asserted.

Branch rule fires

Per the pre-reg: supported ⇒ ideas #9’s state-DROPOUT train-time arm is promoted to its own pre-registration (the literature’s lever: 2506.23944 masks proprioception with p=0.8; input-side, config-only surface, screen rung). ReViP-style modulation and GAP-style phase-guided gradient scaling (2602.12032) stay the heavier arms behind it. Also noted for the dropout pre-reg’s design: GAP predicts the grounding gap concentrates in motion-transition frames — the probe’s npzs (which carry episode/frame indices) support a free descriptive cut of Δ_first by progress-within-episode; that analysis is queued as discussion material, not a frozen read.

The grounding gap (#11 main line) keeps its other candidate mechanisms — this probe supports state-dominant bias as B’s-flag explanation and as a real force in all four checkpoints, but re-anchor and acuity remain live for the residual intact-state gap.

Cost

4 masked subset evals, ~1.6 GPU-h total (04:44–06:06Z on the local H100, including the merge-crash relaunch overhead absorbed earlier); CPU-side pooling and reads, zero intact re-evals — the banked-npz re-pooling pattern’s third use, and its cheapest falsification yet.