j133 ledger: appended 'Missed fires + the label question' (catch 98.888->100%, 746/67065 shots were blind: 456 by the server +3*power bonus, 290 by our own same-tick damage; exact label still -0.230, state-conditional model -0.347 -> the observable state is the constraint) + report fixtures

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+# Exact-geometry Gate A/B — learned movement (job j131)
+
+corpus   : /tmp/tfil_ab2/out
+battles  : 70
+shots    : 54923
+base hit : 9.97%
+state    : vlat, dist, room, turn (module's 4 fields, canonical edges)
+
+## A. danger-map alignment (ONE consistent computation)
+
+corr( danger(g) , P(hit | b_our = g) )   [the j128 metric, = -0.342 hist]
+corr( danger(g) , P(hit | b_bullet = g) ) [same danger, bullet-conditioned target]
+
+| danger map | corr vs P(hit\|b_our=g) | corr vs P(hit\|b_bullet=g) |
+|---|---:|---:|
+| histogram (j128): P(arrival = g) | -0.341 | -0.206 |
+| outcome proxy (j130 live): P(hit & |g-b_our|<=w) | +0.566 | +0.604 |
+| EXACT bullet line: P(|g-b_bullet|<=w) | -0.230 | +0.120 |
+| EXACT bullet line & hit: P(hit & |g-b_bullet|<=w) | +0.465 | +0.684 |
+
+Negative = minimising the danger steers INTO where the observed hits
+happen (the j128 defect). The exact bullet line is the physically
+correct 'would this wave hit me at g' map; if its correlation is still
+negative, exact geometry does NOT fix the inversion.
+
+| bin | P(hit\|b_our) | P(hit\|b_bullet) | hist danger | proxy danger | exact danger |
+|---:|---:|---:|---:|---:|---:|
+| 0 | 9.7% | 7.5% | 0.024 | 0.005 | 0.024 |
+| 1 | 14.1% | 12.0% | 0.019 | 0.008 | 0.053 |
+| 2 | 13.2% | 13.6% | 0.026 | 0.008 | 0.077 |
+| 3 | 10.8% | 9.1% | 0.023 | 0.008 | 0.081 |
+| 4 | 8.8% | 8.0% | 0.025 | 0.007 | 0.079 |
+| 5 | 7.8% | 8.9% | 0.029 | 0.007 | 0.079 |
+| 6 | 7.9% | 9.0% | 0.033 | 0.008 | 0.084 |
+| 7 | 8.8% | 9.0% | 0.035 | 0.009 | 0.090 |
+| 8 | 9.2% | 8.8% | 0.036 | 0.009 | 0.097 |
+| 9 | 9.1% | 9.9% | 0.038 | 0.010 | 0.100 |
+| 10 | 9.8% | 9.2% | 0.039 | 0.011 | 0.101 |
+| 11 | 11.0% | 11.3% | 0.040 | 0.011 | 0.106 |
+| 12 | 10.6% | 10.8% | 0.039 | 0.012 | 0.109 |
+| 13 | 11.1% | 10.0% | 0.041 | 0.011 | 0.110 |
+| 14 | 9.0% | 9.6% | 0.040 | 0.011 | 0.111 |
+| 15 | 9.8% | 8.8% | 0.046 | 0.011 | 0.112 |
+| 16 | 9.8% | 9.4% | 0.038 | 0.010 | 0.109 |
+| 17 | 9.2% | 9.8% | 0.039 | 0.010 | 0.107 |
+| 18 | 10.7% | 9.3% | 0.036 | 0.009 | 0.101 |
+| 19 | 8.8% | 8.8% | 0.035 | 0.008 | 0.096 |
+| 20 | 8.5% | 9.1% | 0.034 | 0.008 | 0.091 |
+| 21 | 7.9% | 8.8% | 0.035 | 0.007 | 0.086 |
+| 22 | 7.5% | 7.5% | 0.033 | 0.007 | 0.083 |
+| 23 | 6.7% | 8.7% | 0.031 | 0.007 | 0.080 |
+| 24 | 8.3% | 7.9% | 0.028 | 0.006 | 0.080 |
+| 25 | 7.2% | 7.4% | 0.026 | 0.007 | 0.083 |
+| 26 | 11.5% | 8.9% | 0.023 | 0.007 | 0.088 |
+| 27 | 13.1% | 13.2% | 0.022 | 0.010 | 0.091 |
+| 28 | 17.5% | 19.6% | 0.026 | 0.012 | 0.080 |
+| 29 | 16.2% | 17.1% | 0.028 | 0.012 | 0.053 |
+| 30 | 10.9% | 2.6% | 0.033 | 0.008 | 0.023 |
+
+## B. state-conditional information under the EXACT bullet-line label
+
+held-out per-candidate log-loss (bits) of the exact label, state-conditional vs state-free (same rows, same split):
+
+| model | log-loss (bits) |
+|---|---:|
+| state-free P(label | g) | 0.1879 |
+| state-conditional P(label | state, g) | 0.3747 |
+| Δ (state − state-free) | +0.1868 |
+
+state conditioning is better in 0/3 splits (negative Δ = better).
+
+## C. open-loop decision counterfactual (VETO ONLY)
+
+argmin_g danger with the recorded bullet line as ground truth:
+
+| exact-label argmin (j131) | 2.47% |
+
+## MEASURED vs INFERRED
+
+* MEASURED: every number above, on the recorded corpus.
+* INFERRED: that an offline alignment transfers live — it cannot, the
+  corpus is open loop (`docs/offline_harness_trust.md`).
@@ -0,0 +1,11 @@
+corpus: /tmp/tfil_ab2/out  records: 54923  base hit: 9.97%
+
+corr( danger(g) , P(hit | b_our = g) )  [the j128 metric]
+------------------------------------------------------------
+(i)   histogram label (j128)          : -0.341
+(ii)  outcome proxy label (j130)      : +0.566
+(iii) EXACT bullet-line label (j131)  : -0.230
+(iv)  state-CONDITIONAL outcome model : -0.347 (seeds ['-0.434', '-0.298', '-0.308'])
+      state-FREE outcome model        : +0.001 (seeds ['-0.369', '+0.212', '+0.160'])
+
+VERDICT: the physically-exact label is STILL negative -> the LABEL was never the problem; the observable STATE is the binding constraint (closes the learned-movement family).