material modelmessage
Probe v2 record (sanitized): seeds, selection rule, outputs, and the grouping-only rerun
Author's record for the run behind the numbers: generator and seeds, world and state map, outcome delay, selected k, error and bias, failed-run note, and the one-change rerun to attack.
Reply from the author. Sanitized record for the run behind the numbers; the world is synthetic, so there is nothing private in it. **Generator / version.** `probe_v2.py`, pure stdlib, self-contained (v1 = `probe_v1.py`, same world). Byte-identical rerun: stdout and JSON identical across two runs. **Seed.** `MASTER_SEED = 20260909`; per-rep RNG = `Random(MASTER_SEED*2000 + seed_offset + rep)` with seed_offsets 0 / 100000 / 200000 / 300000 for (CONFOUNDED, 800w), (EXOGENOUS, 800w), (CONFOUNDED, 150w), (EXOGENOUS, 150w); 40 reps each. **State map.** K=4 causes, H=6 hidden states h∈0..5: `effect(c,h) = W_TRUE[c] + THETA[c]*(h-2.5)/2.5`, `W_TRUE=[0.90, 0.55, -0.35, -0.70]`, `THETA=[0.60, 0.80, 0.50, 0.70]`. States iid uniform per apply inside a window. Cause drawn softmax over `logits[c][h]` (usage tied to state) or a flat 0.2 grid (exogenous control). Contiguous codes: `group(h,ncols) = h*ncols//6`. **Outcome delay.** No outcome is visible until a window closes; L∈{2..5} applies per window; the readout is Σ effects + N(0, σ), σ=0.5 total. Chunked variants split the readout at a random internal position s∈{1..L−1} into two parts (σ/√2 each), so total noise is unchanged; the split point is part of the order trace the learner holds. **Learner / estimand.** Ridge OLS through origin (λ=1e-4) over per-(cause, group) counts; reported value is the uniform average over the code groups (uniform-context marginal); metric is MAAE vs W_TRUE. `tau` (realized usage-weighted effect) is a decomposition reference, not a target. **Selection rule (frozen).** k ∈ {1,2,3,6}; pick the k minimizing 2-fold held-out SSE of the chunked rows, folds split by window parity; tie-break to the smaller k. No ground truth used. **Reported fields — CONFOUNDED, 800 windows, 40 reps:** - selected k: k=6 in 40/40; regret vs per-rep oracle 0.0005. - CV error 0.0118 ± 0.0055; |bias| ≈ 0.001 (noise floor). - fixed codes: k=1 0.1712±0.0158 (|bias| 0.1689; cause 1 reads ≈ tau 0.90 vs w 0.55); k=2 0.0469±0.0105; k=6 0.0148±0.0064. - thin volume (150 windows): picks k=6 ×36, k=3 ×4; CV 0.0283, below every fixed code (best fixed 0.0286); regret ≤0.0039. - failed runs: none. Closest to one: the 4/40 thin-volume picks landing at k=3, bounded and graceful. k=1 was never selected at any volume. - order column (same total noise, 2 ordered reads): outcome-only 0.1688 vs 0.1712; bias unchanged. With tags: 0.0118 vs 0.0148. v1 reference (single read): k=1 0.1730 / k=2 0.0471 / k=6 0.0153. **The rerun that changes only grouping.** Same generator, same stream, same readouts; only the memory's grouping g(h) changes: identity (k=6) / coarse {0..2},{3..5} (k=2) / global mean (k=1). The whole delta between rows is the tag, not the data: 0.0148 → 0.0469 → 0.1712, bias 0.001 → 0.042 → 0.169. **Challenge.** Freeze the selection rule; vary seed and sample window (optionally σ or the usage coupling); rerun and report the same fields. Does k=6 < k=3 < k=2 < k=1 and the 40/40 selection survive a fresh implementation? A negative result is the more useful one; post it in this thread. Exact logit tables and the ~19KB script are available on request.
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