TMComposites gate: per-gun confidence faithful for 3 guns; no pair composes

Adds a per-sample intrinsic-confidence field (GunPrediction.confidence,
threaded through FeedbackEvent/VirtualBullet, populated by Pattern, DecayGF,
KNN, GuessFactor, Tsetlin, TMHorizon) and an offline recorder + analyzer that
reproduce the paper's Figure 2 per gun and its Eq-8 composite.

Measured on 3 held-out tr-bridge DrussGT battles (33k ticks, ~133k samples/gun):
- FAITHFUL: DecayGF (rho +0.133), KNN (+0.090), Pattern (+0.064, weak).
- GuessFactor is ANTI-faithful (rho -0.067); Tsetlin c_max is useless (0.001).
- No pair of guns specialises complementarily: the same gun dominates both
  high-confidence slices in every pair.
- Eq-8 alpha-normalised confidence-weighted composite: 18.41% vs Pattern
  20.45% (McNemar p=3.1e-126). Faithful-only variant 18.68%, still loses.
  Shuffle control passes weakly (composite > shuffle, p=4e-14) so ~0.7pp of
  competence is real but ~2pp short. Offline veto: design is dead.

See docs/tmcomposites_gate.md.

common_libs/guns/guess_factor.nim

@@ -141,6 +141,9 @@ proc predict*(g: var GFGun, state: WorldState, bulletSpeed: float): GunPredictio
   GunPrediction(
     x: clamp(px, BotRadius, state.arenaWidth  - BotRadius),
     y: clamp(py, BotRadius, state.arenaHeight - BotRadius),
+    # TMComposites Eq 4 analogue: the GF histogram is the class distribution over
+    # GF bins, so the class-sum max c_max is the peak bin's accumulated weight.
+    confidence: g.bins[peak],
   )
 
 proc onResult*(g: var GFGun, e: FeedbackEvent) =