feat: add TBPTT training with cross-batch hidden state carryover

- config: seq_len=128, batch_size=4 for long-sequence TBPTT
- dataset: create_tbptt_loader with non-overlapping windows, strict temporal order
- model: forward() accepts/exposes hidden state h; add step() for single-frame stateful inference
- train: carry detached hidden state across batches, reset at epoch boundary
- benchmark: fix model call for new (v_body, h) return signature

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

src/velocity_prediction/model.py

@@ -124,14 +124,17 @@ class VelocityPredictionModel(nn.Module):
         # nn.init.uniform_(self.head[-1].weight, -0.001, 0.001)
         # nn.init.zeros_(self.head[-1].bias)
 
-    def forward(self, events: torch.Tensor, tilt: torch.Tensor) -> torch.Tensor:
+    def forward(self, events: torch.Tensor, tilt: torch.Tensor,
+                h: torch.Tensor = None) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Args:
             events: (B, S, 1, H, W)
             tilt:   (B, S, 3)
+            h:      (num_layers, B, hidden_size) or None → zeros (TBPTT state)
 
         Returns:
             v_body: (B, S, 2)  predicted body-frame [v_right, v_forward] at every timestep
+            h_new:  (num_layers, B, hidden_size)  final GRU hidden state (detached by caller)
         """
         B, S = events.shape[:2]
 
@@ -145,12 +148,12 @@ class VelocityPredictionModel(nn.Module):
         # Fuse per frame
         fused = torch.cat([cnn_feat, pose_feat], dim=-1)  # (B, S, 320)
 
-        # GRU temporal modelling
-        gru_out, _ = self.gru(fused)           # (B, S, 128)
+        # GRU temporal modelling — accepts external hidden state for TBPTT
+        gru_out, h_new = self.gru(fused, h)    # (B, S, 128), (num_layers, B, 128)
 
         # Head regression over every timestep (single-layer GRU → gru_out == h_n[-1] at t=-1)
         v_body = self.head(gru_out)           # (B, S, 128) → (B, S, 2)
-        return v_body
+        return v_body, h_new
 
     @torch.no_grad()
     def step(self, events: torch.Tensor, tilt: torch.Tensor,
@@ -195,7 +198,7 @@ if __name__ == "__main__":
     B, S, H, W = 4, 8, 240, 320
     events = torch.randn(B, S, 1, H, W)
     tilt = torch.randn(B, S, 3)
-    out = model(events, tilt)
+    out, h = model(events, tilt)
     print(f"Input events: {events.shape}")
     print(f"Input tilt:   {tilt.shape}")
     print(f"Output:       {out.shape}  (should be [4, 8, 2])")