diff --git a/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs b/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
index ec5b95d66..6d229bfb9 100644
--- a/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
+++ b/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
@@ -7256,35 +7256,8 @@ impl GpuDqnTrainer {
         let support_ptr = self.per_sample_support_ptr;
         let q_var_ptr = self.q_var_buf_trainer.raw_ptr();
 
-        // Mamba2: enrich h_s2 with temporal context before Q-value computation
-        self.mamba2_step(batch_size)?;
-
-        // G12: Predictive coding — temporal smoothness on enriched trunk
-        self.compute_predictive_coding_loss(batch_size)?;
-
-        // G9: Regime-conditioned dropout on h_s2 (training path only)
-        self.apply_regime_dropout(batch_size, true)?;
-
-        // ISV forward: encoder MLP → branch gate + gamma mod
-        self.launch_isv_forward()?;
-
-        // ISV temporal routing: update per-feature temporal_weight for next mamba2 call
-        // One-step lag (mamba2 already ran above), same pattern as ISV signals.
-        self.launch_isv_temporal_route()?;
-
-        // ISV feature gate: modulate h_s2 features based on ISV regime embedding
-        self.launch_isv_feature_gate(batch_size)?;
-
-        // Recursive confidence: predict own TD-error from h_s2
-        self.launch_recursive_confidence_forward(batch_size)?;
-
-        // Trade plan forward: h_s2 → plan_params [B, 6]
-        self.launch_trade_plan_forward(batch_size)?;
-        // Plan noise: ±5% temporal diversity on plan_params
-        self.launch_plan_noise_inject(batch_size)?;
-
-        // Risk budget forward: h_s2 → risk_budget R ∈ (0,1) before Q-value computation
-        self.risk_budget_forward(batch_size)?;
+        // All temporal/ISV ops already ran inside the graph replay.
+        // Logit buffers (on_v_logits, on_b_logits) are current — just compute Q-stats.
 
         unsafe {
             self.stream