diff --git a/crates/ml-supervised/src/tft/mod.rs b/crates/ml-supervised/src/tft/mod.rs
index 5b51ee603..5d34f9c37 100644
--- a/crates/ml-supervised/src/tft/mod.rs
+++ b/crates/ml-supervised/src/tft/mod.rs
@@ -585,9 +585,57 @@ impl TemporalFusionTransformer {
         temporal: &GpuTensor,
         static_context: &GpuTensor,
     ) -> Result<GpuTensor, MLError> {
-        // For 2D tensors: simple element-wise add (both should be [batch, hidden_dim])
-        // Static context is already [batch, hidden_dim] from VSN+GRN.
-        // Temporal is [batch, hidden_dim] after the linear encoder.
+        // Both tensors may be 3D after variable selection + temporal processing.
+        // temporal:       [batch, seq_len, hidden_dim]
+        // static_context: [batch, 1,       hidden_dim]  (VSN adds seq_len=1 for 2D static input)
+        //
+        // When shapes already match, add directly.  Otherwise broadcast the
+        // static context along the sequence dimension so gpu_add succeeds.
+        if temporal.shape == static_context.shape {
+            return gpu_add(temporal, static_context);
+        }
+
+        // 3D broadcast: tile static_context along dim-1 to match temporal's seq_len
+        if temporal.shape.len() == 3 && static_context.shape.len() == 3 {
+            let batch = temporal.dim(0)?;
+            let seq_len = temporal.dim(1)?;
+            let hidden = temporal.dim(2)?;
+
+            let sc_batch = static_context.dim(0)?;
+            let sc_seq = static_context.dim(1)?;
+            let sc_hidden = static_context.dim(2)?;
+
+            if sc_batch != batch || sc_hidden != hidden {
+                return Err(MLError::DimensionMismatch {
+                    expected: batch * hidden,
+                    actual: sc_batch * sc_hidden,
+                });
+            }
+
+            if sc_seq == 1 && seq_len > 1 {
+                // Broadcast: replicate the single-step static context across seq_len
+                let sc_host = static_context.to_vec()?;
+                let mut expanded = vec![0.0_f32; batch * seq_len * hidden];
+                for b in 0..batch {
+                    for s in 0..seq_len {
+                        for h in 0..hidden {
+                            let src = b * hidden + h;
+                            let dst = b * seq_len * hidden + s * hidden + h;
+                            if let (Some(&v), Some(slot)) =
+                                (sc_host.get(src), expanded.get_mut(dst))
+                            {
+                                *slot = v;
+                            }
+                        }
+                    }
+                }
+                let expanded_tensor =
+                    GpuTensor::from_vec(expanded, &[batch, seq_len, hidden], &temporal.stream)?;
+                return gpu_add(temporal, &expanded_tensor);
+            }
+        }
+
+        // Fallback for 2D or matching shapes
         gpu_add(temporal, static_context)
     }
 
diff --git a/crates/ml/src/ensemble/adapters/tft.rs b/crates/ml/src/ensemble/adapters/tft.rs
index d5cdde6a1..8421e4897 100644
--- a/crates/ml/src/ensemble/adapters/tft.rs
+++ b/crates/ml/src/ensemble/adapters/tft.rs
@@ -382,8 +382,7 @@ mod tests {
     }
 
     #[test]
-    #[ignore = "TFT input dim mismatch (288 vs 32) — needs config alignment after CUDA migration"]
-        fn test_tft_adapter_buffers_and_predicts() {
+    fn test_tft_adapter_buffers_and_predicts() {
         let seq_len = 4;
         let adapter = TftInferenceAdapter::new(test_config(), seq_len)
             .expect("TftInferenceAdapter::new should succeed");
@@ -452,8 +451,7 @@ mod tests {
     }
 
     #[test]
-    #[ignore = "TFT input dim mismatch (288 vs 32) — needs config alignment after CUDA migration"]
-        fn test_tft_adapter_deterministic() {
+    fn test_tft_adapter_deterministic() {
         let seq_len = 4;
         // Create two adapters with identical configs
         let adapter1 = TftInferenceAdapter::new(test_config(), seq_len)