feat: add stateful evaluation with model.step() for per-frame GRU inference

- Add VelocityPredictionModel.step() for single-frame forward pass
  with external GRU hidden state management
- Add evaluate_stateful() that processes frames sequentially,
  maintaining hidden state across timesteps (seq_len=1, stride=1, batch=1)
- Evaluation uses strict temporal ordering (num_workers=0)

Generated by Mistral Vibe.
Co-Authored-By: Mistral Vibe <vibe@mistral.ai>

src/velocity_prediction/model.py

@@ -153,6 +153,33 @@ class VelocityPredictionModel(nn.Module):
         v_body = self.head(last_hidden)       # (B, 2)
         return v_body
 
+    @torch.no_grad()
+    def step(self, events: torch.Tensor, tilt: torch.Tensor,
+             h: torch.Tensor = None) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Single-frame stateful forward pass.
+
+        Processes one timestep and returns the prediction plus updated
+        GRU hidden state.  Call repeatedly with the output ``h`` to
+        accumulate temporal context across frames.
+
+        Args:
+            events: (B, 1, 1, H, W)  single-frame event map
+            tilt:   (B, 1, 3)         single-frame tilt vector
+            h:      (num_layers, B, hidden_size) or None  → zeros
+
+        Returns:
+            v_body: (B, 2)  body-frame [v_right, v_forward]
+            h_new:  (num_layers, B, hidden_size)
+        """
+        cnn_feat = self.cnn(events)          # (B, 1, 256)
+        pose_feat = self.pose_mlp(tilt)      # (B, 1, 64)
+        fused = torch.cat([cnn_feat, pose_feat], dim=-1)  # (B, 1, 320)
+        _, h_new = self.gru(fused, h)        # (num_layers, B, 128)
+        last_hidden = h_new[-1]              # (B, 128)
+        v_body = self.head(last_hidden)      # (B, 2)
+        return v_body, h_new
+
 
 def count_parameters(model: nn.Module) -> int:
     """Count trainable parameters."""