j130 learned movement: outcome label (P(hit|state,g)) mode + Gate A; pre-registered outcome arms

tools/ab/arms_movement_outcome.txt

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+# ─────────────────────────────────────────────────────────────────────────────
+# arms_movement_outcome.txt — the OUTCOME-LABELLED learned-movement arms
+# (job j130), on the FROZEN panel tools/ab/panel_movement.txt.
+#
+# Pre-registered in docs/movement_campaign.md, "Learned movement — outcome
+# label (P(hit))", BEFORE any battle. REFERENCE is `strafe` — the SHIPPED
+# champion. Every delta is (arm − strafe).
+#
+# j128 measured `corr( P(arrival bin), P(hit | arrival bin) ) = -0.342` over the
+# 31 bins: minimising the resolved-position histogram steers INTO the bullets.
+# j130 replaces the label with the dense outcome
+#     hit(state, g) = hit and |g - b_our| <= window(wave)
+# and learns P(hit | state, candidate g) with a counted 2-class SBC.
+#
+# Gate A (common_libs/tests/outcome_label_gate.py, corpus /tmp/tfil_ab2/out):
+# the alignment correlation flips to +0.566 (histogram -0.341), so the veto
+# does NOT fire; the state-conditional outcome model however is NOT better than
+# the state-free one on held-out log-loss, and the open-loop decision
+# counterfactual barely moves (3.53% -> 3.33%). The live panel decides.
+#
+# Format: name | ENV=value ENV=value | label
+# ─────────────────────────────────────────────────────────────────────────────
+
+# 1. THE CHAMPION — the arm a challenger has to beat (round wins + hit rate).
+strafe | TR_MOVEMENT=strafe | champion/reference — shipped strafe defaults
+
+# 2. THE OLD LABEL — j128's state-conditional counted SBC (arrival-bin label),
+#    so the new label is isolated against the old one on the same binary.
+learned | TR_MOVEMENT=learned | j128 histogram label (arrival bin)
+
+# 3. THE NEW LABEL — the same mover, same geometry, same counted+decayed SBC
+#    and penalties; only the training label changes (dense hit outcome).
+learned_outcome | TR_MOVEMENT=learned TR_LEARNED_LABEL=outcome | outcome label P(hit | state, g)
+
+# 4. INFORMATION CONTROL — the outcome label with the state forced to one cell
+#    (state-free outcome model). Isolates whether the state carries anything
+#    under the new label (Gate A says it does not).
+learned_outcome_global | TR_MOVEMENT=learned TR_LEARNED_LABEL=outcome TR_LEARNED_GLOBAL=1 | outcome label, state conditioning OFF