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learned movement: danger-map alignment diagnostic (corr(P(arrival bin), P(hit|bin)) = -0.342) in the gate + ledger

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Davide Cappellini
2026-09-26 10:45:26 +02:00
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@@ -1835,3 +1835,32 @@ better than the global histogram, the unconditional average and chance, with a
consistent cross-battle sign. But it clears the bar by ~1% of a bit, so the
live panel is the decider, and the pre-registered prediction above is that the
module will NOT beat `strafe`.
### Gate A, second half — is the danger map the module minimises the RIGHT one?
This is the diagnosis of *why* the offline skill is tiny, and it is
independent of the learner. The mover minimises **P(arrival bin)**. The
quantity it *should* minimise is **P(hit | arrival bin)**. Measured on the same
54 936 shots (gate report section F):
| quantity | value |
|---|---|
| base hit rate | 9.98% |
| **corr( P(arrival bin), P(hit | arrival bin) )** over the 31 bins | **−0.342** |
| safest bin by the MASS the mover minimises | bin 1 — mass 1.9%, **hit rate 14.1%** |
| safest bin by the ACTUAL hit rate | bin 23 — mass 3.1%, hit rate 6.8% |
**The histogram the surfer minimises is NEGATIVELY correlated with the hit
probability.** The bins with the least mass (the clamped extremes, where a
strong dodger spends its time) are exactly the bins where this corpus's gun
lands the most hits (bins 1–2 and 28–29: 14–17.5%; bins 23–25: 6.7–7.2%). A
mover that steers to the lowest-mass bin steers *into* the bullets. This is the
mechanistic explanation of the j115 failure and of the result below, and no
amount of state conditioning can repair it: the label is the wrong quantity.
*(MEASURED: the correlation and the per-bin table. INFERRED: that this is why
the crude surfer lost — it is consistent with j115's 13.51% incoming hit rate
against `strafe`'s 9.40%. What the mover **should** learn is the outcome: a
counted/decayed SBC over states and bins labelled by HIT/MISS would estimate
P(hit | state, bin) directly. That is the natural next experiment and it is NOT
what was measured here.)*