Amendment 1 to the noise-draw pre-reg: the mode-averaging fairness reads

2026-08-05, ~22:1xZ. Amends the noise-draw ensembling pre-registration (its method step 1 — the unimodality probe — is superseded by the sharper protocol below). Posted before any per-draw number exists; the probe launches at the first quiet local-GPU boundary after the draws chain completes, never concurrently.

Why now

Owner challenge 21:49Z, mid-exchange: is chunk MAE unfair to flow because it forgives mode-averaging? An AR decode that splits the difference between two valid modes and a flow draw that commits to one valid-but-different mode get very different MAE for the same task competence. Three reads were pre-declared in-channel; this amendment freezes their definitions and instruments before any of them produces a number.

Instrument finding (the reason this needed code)

The pre-declared reads assumed “the draws-10 per-draw dumps” would exist. They could not have: the draws chain launcher passes no dump flag at all, and --dump-predictions stores the post-average prediction — per-draw chunks were averaged away inside BijouPolicy.predict_with_text and never left the process. Landed tonight (check.py green, 184 tests):

• bijou.eval --dump-draws PATH — writes the bijou policy’s pre-average [frames, draws, chunk, dim] stacks + truth/valid + the full frame-identity columns (#18.1 conventions, plus scoring semantics scalars so the npz is standalone). Loud constraints: requires --checkpoint and --sample-draws > 1. The prediction path is untouched — the mean is taken once on the full stack before the per-item split (collapse_draws, unit-tested: dumped draws average back byte-identically to the predicted chunks).
• Oracles: banked AR-100k panel report recomputed through the edited scoring path (12 cells, tolerance 1e-4); the fairness script’s degenerate draws=1 run on the banked flow-80k npz reproduces the 6.6232 anchor exactly on reads 1 and 2 with all-zero dispersion (reports/analysis__draws_fairness_k4l2_validate.json).

Probe protocol (frozen)

• Plan: plans/holdout_curated_v0_k4l2_drawsprobe_s7.json — every 7th core frame of the k4l2 panel plan, file order, offset 0: 2,458 frames, 792 repos, 2,458 of 4,301 panel episodes; labeled panel empty (built by fontaine/scripts/draws_probe_plan.py, deterministic).
• Run: flow-80k checkpoint, --sample-draws 10 --sample-steps 30 (Heun), --noise-key index, --seed 0, same corpus flags as the chain, plus --dump-draws. Cost ≈ 30 min on 1×H100 at the measured draws-10 pacing (~1/7th of a full-panel draws run).
• Instrument gate (E1-style): the probe’s draw 0 re-decodes the banked single-draw predictions — mean per-frame chunk-MAE drift vs the banked flow-80k npz rows (draw0_vs_banked_frame_mae_drift) expected < 0.05; larger = instrument finding, diagnose before any read is quoted.
• Analysis: fontaine/scripts/draws_fairness.py --draws <npz> (pure CPU; joins the probe rows to the banked AR-100k / flow-80k npzs on the corpus concat index — the paired-analysis join, with a hard assert that truth/valid rows agree).

The three reads (definitions frozen)

All pooling valid-element-weighted, matching the report’s chunk_mae exactly (validated against the anchor above).

1. Mean-of-draws MAE — pooled MAE of the 10-draw ensemble mean. Ensembling manufactures the mode-averaged predictor flow “should have been” under an MAE-fair comparison. Primary magnitude comes from the chain’s full-panel draws-10 run (pre-reg E3); the probe read cross-checks it on the subset.
2. Best-of-N MAE — per frame, the best of the 10 draws by that frame’s chunk MAE (first_mae selects its own best draw, independently); pooled. The oracle mode-match bound: how good is flow when “sampled a different valid mode” is forgiven entirely.
3. Dispersion-conditioned deficit — per-frame dispersion = masked element-mean of the across-draw std; probe frames cut into dispersion quartiles; per-quartile mean paired deficit (flow-single-draw − AR-100k, per-frame, from the banked npzs) + flow win rate + Spearman(dispersion, deficit). Per-step dispersion curve reported alongside (the #1 prediction: spread should grow with horizon).

Pre-declared interpretation

• Unfair-penalty signature: deficit concentrating in the high- dispersion quartiles (monotone quartile trend, positive Spearman) — flow is being punished where it commits to modes. Read 2 sizes it: best-of-10 at or below AR’s paired chunk MAE on the probe frames says a valid-mode within 10 draws matches AR.
• Modeling-deficit signature: deficit flat across dispersion quartiles and best-of-10 still well above AR — the gap is not a metric artifact; attribution screens proceed on the AR recipe (owner steer 21:48Z).
• Effect sizes quoted with everything; quartile noise floor read off the quartile n (~615 frames each).

Honest limits (stated in-channel, kept here)

MAE cannot settle actual rig performance either way; the owner’s comm-MAE→rig bridge was built on AR checkpoints. If the unfair- penalty signature confirms, the comm holdout needs a distributional column (best-of-N or an energy-distance-style score) before it can rank flow arms — that column feeds the limit-attribution front, not a new benchmark.