diff --git a/crates/ml-alpha/examples/alpha_train.rs b/crates/ml-alpha/examples/alpha_train.rs
index 76771b5d1..90df33f31 100644
--- a/crates/ml-alpha/examples/alpha_train.rs
+++ b/crates/ml-alpha/examples/alpha_train.rs
@@ -2,8 +2,9 @@
 //!
 //! Reads predecoded MBP-10 sidecars via `MultiHorizonLoader`, drives
 //! `PerceptionTrainer` end-to-end. Each training step consumes ONE
-//! seq_len-snapshot window and emits one BCE loss against the labels
-//! at the window's last position (per-horizon).
+//! multi-resolution window (total positions = sum of per-scale counts)
+//! and emits one BCE loss against the labels at the window's last
+//! position (per-horizon).
 //!
 //! Emits `alpha_train_summary.json` on exit for downstream gate
 //! consumption.
@@ -13,7 +14,7 @@
 //!     --mbp10-data-dir   <path> \
 //!     --predecoded-dir   <path> \
 //!     --epochs           5 \
-//!     --seq-len          32 \
+//!     --multi-resolution 1:10,30:10,100:12 \
 //!     --mamba2-state-dim 16 \
 //!     --lr-cfc           3e-3 \
 //!     --lr-mamba2        1e-3 \
@@ -54,15 +55,14 @@ struct Cli {
     #[arg(long, default_value_t = 5)]
     epochs: usize,
 
-    /// Snapshots per training sequence (Mamba2 + CfC in-window context).
-    /// Default 64 — empirically the 3-fold ISV CV ran K=32 and observed
-    /// the longest-horizon AUC saturating around 0.70-0.74. K=64 doubles
-    /// in-window context relative to K=32 and gives Mamba2's SSM state
-    /// more material to build long-horizon predictions from. Kernel
-    /// maximum is 96 (MAMBA2_KERNEL_SEQ_MAX). Per-epoch wall scales ~K
-    /// (more K-loop launches in the captured graph).
-    #[arg(long, default_value_t = 64)]
-    seq_len: usize,
+    /// Multi-resolution input window: `<scale>:<count>[,<scale>:<count>]...`.
+    /// Default `1:10,30:10,100:12` gives 32 positions covering 1510 ticks
+    /// (10 raw + 10 aggregated@30 + 12 aggregated@100). Use `1:32` for the
+    /// degenerate single-scale parity-check config.
+    ///
+    /// See `docs/superpowers/plans/2026-05-22-multi-resolution-input.md`.
+    #[arg(long, default_value = "1:10,30:10,100:12")]
+    multi_resolution: ml_alpha::data::aggregation::MultiResolutionConfig,
 
     #[arg(long, default_value_t = 16)]
     mamba2_state_dim: usize,
@@ -341,7 +341,7 @@ fn main() -> Result<()> {
         w.copy_from_slice(&parts);
         w
     } else if cli.auto_horizon_weights {
-        auto_horizon_weights(cli.seq_len, &HORIZONS)
+        auto_horizon_weights(cli.multi_resolution.total_positions(), &HORIZONS)
     } else {
         [1.0; N_HORIZONS]
     };
@@ -354,7 +354,7 @@ fn main() -> Result<()> {
 
     anyhow::ensure!(cli.batch_size >= 1, "batch_size must be >= 1");
     let trainer_cfg = PerceptionTrainerConfig {
-        seq_len: cli.seq_len,
+        seq_len: cli.multi_resolution.total_positions(),
         mamba2_state_dim: cli.mamba2_state_dim,
         lr_cfc: cli.lr_cfc,
         lr_mamba2: cli.lr_mamba2,
@@ -487,12 +487,18 @@ fn main() -> Result<()> {
     // PRELOAD: construct loaders ONCE (each preloads its files into RAM).
     // Per-epoch we call reset(seed) to re-shuffle anchor sampling — no
     // disk IO. Cuts wall time ~5× on a 6-epoch run, ~7× on 15 epochs.
+    tracing::info!(
+        multi_resolution = %cli.multi_resolution,
+        total_positions = cli.multi_resolution.total_positions(),
+        lookback_ticks = cli.multi_resolution.required_lookback_ticks(),
+        "multi-resolution config",
+    );
     tracing::info!("preloading train + val MBP-10 files into RAM (slow startup, fast per-epoch)…");
     let preload_start = std::time::Instant::now();
     let mut train_loader = MultiHorizonLoader::new(&MultiHorizonLoaderConfig {
         files: train_files,
         predecoded_dir: cli.predecoded_dir.clone(),
-        seq_len: cli.seq_len,
+        multi_resolution: cli.multi_resolution.clone(),
         horizons: HORIZONS,
         n_max_sequences: cli.n_train_seqs,
         seed: cli.seed,
@@ -504,7 +510,7 @@ fn main() -> Result<()> {
     let mut val_loader = MultiHorizonLoader::new(&MultiHorizonLoaderConfig {
         files: val_files,
         predecoded_dir: cli.predecoded_dir.clone(),
-        seq_len: cli.seq_len,
+        multi_resolution: cli.multi_resolution.clone(),
         horizons: HORIZONS,
         n_max_sequences: cli.n_val_seqs,
         seed: cli.seed.wrapping_add(0xC0FFEE),
@@ -707,7 +713,7 @@ fn main() -> Result<()> {
             val_steps += 1;
             // probs_all is [K, B, N_HORIZONS] row-major. Score AUC from
             // the LAST-position predictions for each sample in the batch.
-            let last = cli.seq_len - 1;
+            let last = cli.multi_resolution.total_positions() - 1;
             for (b_idx, last_lbl) in val_last_label_batch.iter().enumerate() {
                 let off = (last * cli.batch_size + b_idx) * N_HORIZONS;
                 let last_probs = &probs_all[off..off + N_HORIZONS];
@@ -852,7 +858,7 @@ fn main() -> Result<()> {
 
     let summary = AlphaTrainSummary {
         epochs: epochs_completed,
-        seq_len: cli.seq_len,
+        seq_len: cli.multi_resolution.total_positions(),
         horizons: HORIZONS,
         final_train_loss: if train_steps > 0 { train_loss_running / train_steps as f32 } else { 0.0 },
         final_val_loss,