feat(ml-alpha): decision-stride loader + CLI + Mamba2 dt_s scaling (Phase 2A)

Decision-stride S lets a length-K sequence span ((K-1)*S + 1) raw
snapshots instead of K consecutive ones — expands the effective
time-window covered by each sequence at the same K-positions compute
cost. With K=64 and S=4, the window covers 256 ticks (~5s on ES MBP-10
at 20ms-tick) instead of 64 ticks (~1.3s).

Loader (crates/ml-alpha/src/data/loader.rs):
- `MultiHorizonLoaderConfig.decision_stride: usize` (default 1, must
  pre-existing call sites add the new field).
- `next_sequence` reads snapshot at `anchor + k * stride`; labels at the
  same indices (labels stay in absolute-snapshot horizons regardless of
  stride, e.g. h=6000 always means "predict 6000 raw snapshots forward").
- `prev` snapshot for microstructure features (prev_mid, prev_ts_ns)
  now points to the prior K-position (`anchor + (k-1)*stride`), NOT the
  consecutive-snapshot prior, so `Δt = ts_ns - prev_ts_ns` carries the
  actual elapsed time between K-positions (consumed by Mamba2's dt_s and
  the planned Phase 2C TGN Fourier features).
- New `#[ignore]` real-data test: `loader_stride_4_yields_correct_spacing`
  asserts Δt monotonicity at stride=4.

Mamba2 dt_s (crates/ml-alpha/src/trainer/perception.rs):
- `PerceptionTrainerConfig.decision_stride: usize` plumbs the stride
  through. dispatch_train_step + evaluate_batched now use
  `dt_s = decision_stride as f32` so Mamba2's selective scan
  `exp(-dt * sigmoid(a))` reflects the real elapsed time. With stride=1
  the behaviour is identical to before.

CLI (crates/ml-alpha/examples/alpha_train.rs):
- `--decision-stride <S>` flag (default 1) wired into both train and val
  loaders + PerceptionTrainerConfig.

Argo workflow:
- `decision-stride` parameter on the template (default "1") +
  `--decision-stride` script flag + propagation into the train pod's
  alpha_train invocation.

Synthetic smoke (tests/perception_overfit.rs):
- `stacked_trainer_loss_shrinks_with_stride_4` proves the trainer-level
  dt_s=4.0 keeps the Mamba2+LN+CfC+GRN chain numerically stable.
  Converges 0.32 → 0.0000 (matches stride=1 smoke trajectory — dt_s
  scaling didn't break the SSM dynamics).

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

scripts/argo-alpha-perception.sh

@@ -34,6 +34,7 @@ EARLY_STOP_PATIENCE=5
 CV_FOLD=0
 CV_N_FOLDS=1
 CV_TRAIN_WINDOW=0
+DECISION_STRIDE=1
 WATCH=false
 
 usage() {
@@ -53,6 +54,7 @@ Usage: $0 [OPTIONS]
   --cv-fold <k>             CV fold index (default: $CV_FOLD)
   --cv-n-folds <N>          Total CV folds (default: $CV_N_FOLDS)
   --cv-train-window <W>     Files per train window (default: $CV_TRAIN_WINDOW = auto)
+  --decision-stride <S>     Snapshot stride for sequence sampling (default: $DECISION_STRIDE)
   --watch                   Follow logs via argo watch
 EOF
 }
@@ -77,6 +79,7 @@ while [[ $# -gt 0 ]]; do
     --cv-fold)           CV_FOLD="$2"; shift 2 ;;
     --cv-n-folds)        CV_N_FOLDS="$2"; shift 2 ;;
     --cv-train-window)   CV_TRAIN_WINDOW="$2"; shift 2 ;;
+    --decision-stride)   DECISION_STRIDE="$2"; shift 2 ;;
     --watch)             WATCH=true; shift ;;
     -h|--help)           usage; exit 0 ;;
     *) echo "Unknown option: $1"; usage; exit 1 ;;
@@ -147,4 +150,5 @@ argo submit -n foxhunt --from=wftmpl/alpha-perception \
   -p cv-fold="$CV_FOLD" \
   -p cv-n-folds="$CV_N_FOLDS" \
   -p cv-train-window="$CV_TRAIN_WINDOW" \
+  -p decision-stride="$DECISION_STRIDE" \
   $WATCH_FLAG