diff --git a/pyhealth/models/micron.py b/pyhealth/models/micron.py
index ffee2612c..4087cacda 100644
--- a/pyhealth/models/micron.py
+++ b/pyhealth/models/micron.py
@@ -77,6 +77,8 @@ def compute_reconstruction_loss(
         Returns:
             torch.tensor: Mean squared reconstruction loss value.
         """
+        if logits.size(1) < 2:
+            return logits.new_zeros(())
         rec_loss = torch.mean(
             torch.square(
                 torch.sigmoid(logits[:, 1:, :])
@@ -246,21 +248,20 @@ def _ensure_tensor(self, feature_key: str, value) -> torch.Tensor:
             return torch.tensor(value, dtype=torch.long)
         return torch.tensor(value, dtype=torch.float)
 
-    def _pool_embedding(self, x: torch.Tensor) -> torch.Tensor:
-        if x.dim() == 4:
+    def _pool_embedding(self, feature_key: str, x: torch.Tensor) -> torch.Tensor:
+        if isinstance(self.feature_processors[feature_key], SequenceProcessor):
+            # Flat code lists describe one visit, not successive visits.
+            x = x.sum(dim=1, keepdim=True)
+        elif x.dim() == 4:
             x = x.sum(dim=2)
         if x.dim() == 2:
             x = x.unsqueeze(1)
-        # Make sure temporal dimension (dim=1) matches the longest sequence
-        if x.size(1) == 1:
-            # Repeat to handle shorter sequences
-            x = x.repeat(1, 2, 1)
         return x
 
     def _create_mask(self, feature_key: str, value: torch.Tensor) -> torch.Tensor:
         processor = self.feature_processors[feature_key]
         if isinstance(processor, SequenceProcessor):
-            mask = value != 0
+            mask = (value != 0).any(dim=1, keepdim=True)
         elif isinstance(processor, StageNetProcessor):
             if value.dim() >= 3:
                 mask = torch.any(value != 0, dim=-1)
@@ -332,7 +333,7 @@ def forward(self, **kwargs) -> Dict[str, torch.Tensor]:
         for feature_key in self.feature_keys:
             x = embedded[feature_key]
             mask = masks[feature_key]
-            x = self._pool_embedding(x)
+            x = self._pool_embedding(feature_key, x)
             patient_emb.append(x)
 
         # Concatenate along last dim: [batch, seq_len, embedding_dim * n_features]
diff --git a/tests/core/test_micron.py b/tests/core/test_micron.py
index 59107f907..0552a50d3 100644
--- a/tests/core/test_micron.py
+++ b/tests/core/test_micron.py
@@ -124,9 +124,8 @@ def test_model_with_embedding(self):
 
         self.assertIn("embed", ret)
         self.assertEqual(ret["embed"].shape[0], 2)  # batch size
-        expected_seq_len = max(len(data_batch["conditions"][0]), len(data_batch["procedures"][0]))
         expected_feature_dim = self.model.embedding_dim * len(self.model.feature_keys)
-        self.assertEqual(ret["embed"].shape[1], expected_seq_len)
+        self.assertEqual(ret["embed"].shape[1], 1)
         self.assertEqual(ret["embed"].shape[2], expected_feature_dim)
 
     def test_custom_hyperparameters(self):
@@ -151,6 +150,57 @@ def test_custom_hyperparameters(self):
         self.assertIn("loss", ret)
         self.assertIn("y_prob", ret)
 
+    def test_features_with_unequal_sequence_lengths(self):
+        """Unequal code lists must both contribute to the visit's prediction."""
+        samples = [{
+            "conditions": ["a", "b", "c"],
+            "procedures": ["x"],
+            "drugs": ["d1", "d2"],
+        }]
+        dataset = create_sample_dataset(
+            samples=samples,
+            input_schema={"conditions": "sequence", "procedures": "sequence"},
+            output_schema={"drugs": "multilabel"},
+        )
+        model = MICRON(dataset=dataset, embedding_dim=2, hidden_dim=2)
+        # Positive, small weights avoid cancellation and sigmoid saturation.
+        with torch.no_grad():
+            for parameter in model.parameters():
+                parameter.fill_(0.1)
+            for layer in model.embedding_model.embedding_layers.values():
+                layer.weight[0].zero_()
+        data_batch = next(iter(get_dataloader(dataset, batch_size=1, shuffle=False)))
+        ret = model(**data_batch, embed=True)
+        self.assertTrue(torch.isfinite(ret["loss"]))
+        # Each flat list belongs to the same single visit.
+        self.assertEqual(ret["embed"].shape[1], 1)
+
+        ret["y_prob"].sum().backward()
+        for field, codes in {"conditions": ["a", "b", "c"], "procedures": ["x"]}.items():
+            vocab = dataset.input_processors[field].code_vocab
+            grad = model.embedding_model.embedding_layers[field].weight.grad
+            for code in codes:
+                self.assertTrue(torch.isfinite(grad[vocab[code]]).all())
+                self.assertGreater(grad[vocab[code]].abs().sum().item(), 0)
+
+    def test_nested_sequences_preserve_visits(self):
+        samples = [{
+            "conditions": [["a", "b"], ["c"]],
+            "procedures": [["x"], ["y", "z"]],
+            "drugs": ["d1"],
+        }]
+        dataset = create_sample_dataset(
+            samples=samples,
+            input_schema={"conditions": "nested_sequence", "procedures": "nested_sequence"},
+            output_schema={"drugs": "multilabel"},
+        )
+        model = MICRON(dataset=dataset)
+        batch = next(iter(get_dataloader(dataset, batch_size=1, shuffle=False)))
+        result = model(**batch, embed=True)
+        self.assertEqual(result["embed"].shape[1], 2)
+        self.assertTrue(torch.isfinite(result["loss"]))
+        result["loss"].backward()
+
     def test_ddi_adjacency_matrix(self):
         """Test the drug-drug interaction adjacency matrix generation."""
         ddi_matrix = self.model.generate_ddi_adj()