feat(rl): encoder input expansion 40 → 56 dims (trade-context + multires)

Introduces ENCODER_INPUT_DIM = 56 (FEATURE_DIM + 16). All encoder
first-layer weight matrices (VSN gate, Mamba2 L1 input projection)
now sized for 56 input dims. The extra 16 are per-batch state:
4 trade_context + 12 multires features.

- snap_feature_assemble_batched: output stride → ENCODER_INPUT_DIM,
  zero-fills dims [40..56] for the broadcast kernel to overwrite.
- New rl_encoder_context_broadcast.cu: writes trade_context_d[B×4]
  + multires_output_d[B×12] into each of the K sequence rows per
  batch at positions [40..56].
- CfcConfig.n_in, Mamba2 L1 in_dim, VSN gate, window_tensor_d,
  all forward/backward scratch buffers updated to ENCODER_INPUT_DIM.
- CfcTrunk default config updated.

The broadcast kernel launch integration into the forward_only path
is the final wire-up step — until then dims 40-55 are zero-filled
(safe: Xavier init on new columns means encoder starts by learning
to ignore them, then gradually incorporates the signal).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

crates/ml-alpha/build.rs

@@ -85,6 +85,7 @@ const KERNELS: &[&str] = &[
     "rl_recent_outcome_update",      // P10: per-batch signed outcome EMA for anti-martingale sizing
     "rl_trade_context_update",       // P1: per-batch trade-arc features (time_in_trade, unrealized_R, pos_mag, entry_dist)
     "rl_multires_features_update",   // P0: per-batch multi-resolution streaming features (3 horizons × 4 features)
+    "rl_encoder_context_broadcast",  // P2: broadcast per-batch context (16 dims) into encoder input [B,K,56] at cols 40-55
 ];
 
 // Cache bust v31 — five new reduce / derive kernels populate the input