diff --git a/crates/ml-alpha/examples/phase1d_mamba.rs b/crates/ml-alpha/examples/phase1d_mamba.rs
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+//! Phase 1d.1 — Stateful Mamba2 sequence encoder smoke.
+//!
+//! Trains the from-scratch GPU-pure Mamba2 block on the snapshot-stream
+//! fxcache. Compares val AUC against the MLP baseline (Phase 1c snapshot:
+//! AUC=0.685 at K=100 stateless).
+//!
+//! Falsification gate (per the implementation plan):
+//!   - AUC > 0.72 → sequence-state lifts beyond stateless MLP. Proceed
+//!     to Phase 1d.2 (multi-minute horizon).
+//!   - AUC ≤ 0.72 → stateless features have a hard ceiling; richer
+//!     features (not richer model) are the next move.
+//!
+//! Sequence semantics: each training example is a contiguous block of
+//! `seq_len` snapshots ending at bar `t`; the model predicts the binary
+//! direction label at bar `t` (sign of `mid_price[t+H] - mid_price[t]`,
+//! computed by `prepare_phase1a_data`).
+//!
+//! Memory model: feature matrix + labels stay CPU-resident (640 MB
+//! f32 + 1.5 MB labels for the 1.97 M-row snapshot fxcache). Per batch,
+//! we gather contiguous seq_len-bar slices into a pinned host buffer,
+//! DMA to GPU once, then do forward + backward + AdamW step.
+
+use anyhow::{Context, Result};
+use clap::Parser;
+use cudarc::driver::CudaContext;
+use rand::{Rng, SeedableRng};
+use rand_chacha::ChaCha8Rng;
+use std::sync::Arc;
+use tracing_subscriber::EnvFilter;
+
+use ml_alpha::eval::{accuracy_from_logits, auc_from_logits};
+use ml_alpha::mamba2_block::{
+    Mamba2AdamW, Mamba2AdamWConfig, Mamba2Block, Mamba2BlockConfig,
+};
+use ml_alpha::fxcache_reader::FxCacheReader;
+use ml_alpha::purged_split::PurgedSplit;
+use ml_alpha::training::{prepare_phase1a_data, Phase1aConfig};
+
+use ml_core::cuda_autograd::gpu_tensor::GpuTensor;
+
+#[derive(Parser, Debug)]
+#[command(name = "phase1d_mamba", about = "FoxhuntQ-Δ Phase 1d.1 Mamba2 sequence smoke")]
+struct Cli {
+    #[arg(long)] fxcache_path: String,
+    #[arg(long, default_value_t = 100)] horizon: usize,
+    #[arg(long, default_value_t = 32)] seq_len: usize,
+    #[arg(long, default_value_t = 64)] hidden_dim: usize,
+    #[arg(long, default_value_t = 16)] state_dim: usize,
+    #[arg(long, default_value_t = 5)] epochs: usize,
+    #[arg(long, default_value_t = 256)] batch_size: usize,
+    #[arg(long, default_value_t = 1e-3)] lr: f32,
+    #[arg(long, default_value_t = 42)] seed: u64,
+    /// Subsample stride for train sequences (1 = use every starting position;
+    /// larger = subsample). Memory + time both scale linearly with this.
+    #[arg(long, default_value_t = 4)] train_stride: usize,
+}
+
+fn main() -> Result<()> {
+    tracing_subscriber::fmt()
+        .with_env_filter(EnvFilter::try_from_default_env().unwrap_or_else(|_| EnvFilter::new("warn")))
+        .init();
+    let cli = Cli::parse();
+    let ctx = CudaContext::new(0).context("init CUDA")?;
+    let stream = ctx.default_stream();
+
+    // ── Load fxcache, build features+labels (mirrors phase1a) ──────────
+    let mut cfg_p1a = Phase1aConfig::default();
+    cfg_p1a.fxcache_path = cli.fxcache_path.clone();
+    cfg_p1a.horizon = cli.horizon;
+    let reader = FxCacheReader::open(&cfg_p1a.fxcache_path)?;
+    let alpha_dim = reader.alpha_feature_dim()
+        .ok_or_else(|| anyhow::anyhow!("fxcache lacks alpha column"))?;
+    cfg_p1a.mlp.in_dim = alpha_dim;
+    let split = PurgedSplit::new(reader.bar_count(), cfg_p1a.train_frac,
+        cfg_p1a.horizon, cfg_p1a.embargo_bars)?.split();
+    let data = prepare_phase1a_data(&reader, &split, &cfg_p1a)?;
+    println!(
+        "fxcache: {} rows, alpha_dim={}, n_train={}, n_val={}",
+        reader.bar_count(), alpha_dim, data.train_indices.len(), data.val_indices.len()
+    );
+
+    // ── Build sequence start indices ──────────────────────────────────
+    // A sequence ending at bar t requires t-seq_len+1 ≥ 0 and t < bar_count.
+    // We filter train/val to bars where the preceding seq_len-1 bars are
+    // within the same train (or val) range. For simplicity we use bars
+    // where `idx ≥ seq_len - 1`.
+    let seq_len = cli.seq_len;
+    let make_seq_starts = |indices: &[usize], stride: usize| -> Vec<usize> {
+        indices.iter()
+            .filter(|&&i| i + 1 >= seq_len)
+            .step_by(stride)
+            .map(|&i| i + 1 - seq_len)  // start bar of the seq_len-bar window
+            .collect()
+    };
+    let train_starts = make_seq_starts(&data.train_indices, cli.train_stride);
+    let val_starts = make_seq_starts(&data.val_indices, 1);
+    println!("train sequences (stride={}): {}", cli.train_stride, train_starts.len());
+    println!("val sequences:               {}", val_starts.len());
+
+    // ── Build Mamba2 + AdamW ──────────────────────────────────────────
+    let block_cfg = Mamba2BlockConfig {
+        in_dim: alpha_dim,
+        hidden_dim: cli.hidden_dim,
+        state_dim: cli.state_dim,
+        seq_len,
+    };
+    let mut block = Mamba2Block::new(block_cfg.clone(), Arc::clone(&stream))?;
+    let mut opt = Mamba2AdamW::new(&block, Mamba2AdamWConfig {
+        lr: cli.lr,
+        ..Default::default()
+    })?;
+    println!("Mamba2Block params: {}", block.param_count());
+
+    // ── Training loop ─────────────────────────────────────────────────
+    let mut rng = ChaCha8Rng::seed_from_u64(cli.seed);
+    let train_len = train_starts.len();
+    if train_len < cli.batch_size {
+        anyhow::bail!("n_train_sequences ({}) < batch_size ({})", train_len, cli.batch_size);
+    }
+    let batches_per_epoch = train_len / cli.batch_size;
+
+    let bce_loss = |logits: &[f32], labels: &[f32]| -> f32 {
+        let mut s = 0.0_f32;
+        let eps = 1e-7_f32;
+        for (&z, &y) in logits.iter().zip(labels.iter()) {
+            let z = z.clamp(-50.0, 50.0);
+            let p = (1.0 / (1.0 + (-z).exp())).clamp(eps, 1.0 - eps);
+            s += -(y * p.ln() + (1.0 - y) * (1.0 - p).ln());
+        }
+        s / logits.len() as f32
+    };
+
+    for epoch in 0..cli.epochs {
+        let mut perm: Vec<usize> = (0..train_len).collect();
+        for i in (1..train_len).rev() {
+            let j = rng.gen_range(0..=i);
+            perm.swap(i, j);
+        }
+        let mut loss_sum = 0.0_f32;
+        for batch_idx in 0..batches_per_epoch {
+            let sel = &perm[batch_idx * cli.batch_size .. (batch_idx + 1) * cli.batch_size];
+            // Pull labels from train_labels via end-bar lookup. We rebuilt gather_batch
+            // to read train_labels for sequence END bars (sequence labels = last-position).
+            let (input, labels) = gather_batch_for_train(
+                &train_starts, sel, seq_len, alpha_dim,
+                &data.feature_matrix, &data.train_labels, &data.train_indices, &stream
+            )?;
+            let (logit, cache) = block.forward_train(&input)?;
+            let logit_host = logit.to_host(&stream)?;
+            let loss = bce_loss(&logit_host, &labels);
+            loss_sum += loss;
+
+            // d_logit = (sigmoid(z) - y) / N for mean-BCE-with-logits.
+            let n_b = labels.len() as f32;
+            let d_logit_host: Vec<f32> = logit_host.iter().zip(labels.iter())
+                .map(|(&z, &y)| {
+                    let p = 1.0 / (1.0 + (-z.clamp(-50.0, 50.0)).exp());
+                    (p - y) / n_b
+                })
+                .collect();
+            let d_logit_dev = stream.clone_htod(&d_logit_host)?;
+            let d_logit = GpuTensor::new(d_logit_dev, vec![labels.len(), 1])?;
+            let grads = block.backward(&cache, &d_logit)?;
+            opt.step(&mut block, &grads)?;
+        }
+        let mean_loss = loss_sum / batches_per_epoch as f32;
+        println!("epoch {epoch:2}  mean_train_bce={mean_loss:.5}  n_batches={batches_per_epoch}");
+    }
+
+    // ── Validation pass ───────────────────────────────────────────────
+    let val_chunk = cli.batch_size;
+    let mut val_logits: Vec<f32> = Vec::with_capacity(val_starts.len());
+    let mut val_labels_y: Vec<f32> = Vec::with_capacity(val_starts.len());
+    let mut i = 0;
+    while i < val_starts.len() {
+        let this = val_chunk.min(val_starts.len() - i);
+        let sel: Vec<usize> = (i..i + this).collect();
+        let (input, labels) = gather_batch_for_val(
+            &val_starts, &sel, seq_len, alpha_dim,
+            &data.feature_matrix, &data.val_labels, &data.val_indices, &stream
+        )?;
+        let logit = block.forward(&input)?;
+        let logit_host = logit.to_host(&stream)?;
+        val_logits.extend_from_slice(&logit_host);
+        val_labels_y.extend_from_slice(&labels);
+        i += this;
+    }
+    let labels_u8: Vec<u8> = val_labels_y.iter().map(|&y| if y > 0.5 { 1 } else { 0 }).collect();
+    let acc = accuracy_from_logits(&val_logits, &labels_u8);
+    let auc = auc_from_logits(&val_logits, &labels_u8);
+    println!();
+    println!("===========================================================");
+    println!("PHASE 1d.1 — MAMBA2 SMOKE");
+    println!("===========================================================");
+    println!("val sequences: {}", val_logits.len());
+    println!("val accuracy:  {:.4}", acc);
+    println!("val AUC:       {:.4}", auc);
+    println!("MLP baseline (Phase 1c K=100): AUC=0.6849");
+    if auc > 0.72 {
+        println!("GATE PASS: sequence-state lift; proceed to Phase 1d.2 (multi-minute horizon).");
+    } else if auc > 0.6849 {
+        println!("MARGINAL: above MLP baseline ({:.4} > 0.6849) but below gate (0.72). Inspect.", auc);
+    } else {
+        println!("GATE FAIL: AUC ≤ MLP baseline 0.6849; no sequence-state lift.");
+    }
+    Ok(())
+}
+
+/// Gather a training batch. Sequence labels come from `train_labels`, which
+/// is indexed *positionally* (the i-th train_label corresponds to the i-th
+/// train_indices entry). We map start → end_bar → position in train_indices.
+fn gather_batch_for_train(
+    starts: &[usize],
+    sel: &[usize],
+    seq_len: usize,
+    alpha_dim: usize,
+    feature_matrix: &[f32],
+    train_labels: &[f32],
+    train_indices: &[usize],
+    stream: &Arc<cudarc::driver::CudaStream>,
+) -> Result<(GpuTensor, Vec<f32>)> {
+    let b = sel.len();
+    let mut host = Vec::with_capacity(b * seq_len * alpha_dim);
+    let mut labels = Vec::with_capacity(b);
+    for &k in sel {
+        let s = starts[k];
+        let end_bar = s + seq_len - 1;
+        for t in 0..seq_len {
+            let off = (s + t) * alpha_dim;
+            host.extend_from_slice(&feature_matrix[off..off + alpha_dim]);
+        }
+        // Find end_bar in train_indices via binary search (train_indices is
+        // strictly ascending in the prepare_phase1a_data implementation).
+        let pos = train_indices.binary_search(&end_bar).ok();
+        labels.push(pos.map(|p| train_labels[p]).unwrap_or(0.5));
+    }
+    let dev = stream.clone_htod(&host)?;
+    let t = GpuTensor::new(dev, vec![b, seq_len, alpha_dim])
+        .map_err(|e| anyhow::anyhow!("batch tensor: {e}"))?;
+    Ok((t, labels))
+}
+
+fn gather_batch_for_val(
+    starts: &[usize],
+    sel: &[usize],
+    seq_len: usize,
+    alpha_dim: usize,
+    feature_matrix: &[f32],
+    val_labels: &[f32],
+    val_indices: &[usize],
+    stream: &Arc<cudarc::driver::CudaStream>,
+) -> Result<(GpuTensor, Vec<f32>)> {
+    let b = sel.len();
+    let mut host = Vec::with_capacity(b * seq_len * alpha_dim);
+    let mut labels = Vec::with_capacity(b);
+    for &k in sel {
+        let s = starts[k];
+        let end_bar = s + seq_len - 1;
+        for t in 0..seq_len {
+            let off = (s + t) * alpha_dim;
+            host.extend_from_slice(&feature_matrix[off..off + alpha_dim]);
+        }
+        let pos = val_indices.binary_search(&end_bar).ok();
+        labels.push(pos.map(|p| val_labels[p]).unwrap_or(0.5));
+    }
+    let dev = stream.clone_htod(&host)?;
+    let t = GpuTensor::new(dev, vec![b, seq_len, alpha_dim])
+        .map_err(|e| anyhow::anyhow!("batch tensor: {e}"))?;
+    Ok((t, labels))
+}