diff --git a/backends/vulkan/test/test_vulkan_tensor_repr.py b/backends/vulkan/test/test_vulkan_tensor_repr.py
index 5a0fc664c17..83d68195407 100644
--- a/backends/vulkan/test/test_vulkan_tensor_repr.py
+++ b/backends/vulkan/test/test_vulkan_tensor_repr.py
@@ -605,6 +605,29 @@ def test_unary_op_construction(self):
         self.assertEqual(op_repsets.primary_arg_idx, 0)
         self.assertTrue(op_repsets.sync_primary_io_repr)
 
+    def test_single_tensor_output_in_list_construction(self):
+        """An op declared to return Tensor[] that yields exactly one tensor.
+
+        num_tensors_in_node() counts tensors rather than nesting, so such a
+        node reports 1 while meta["val"] is a one-element list rather than a
+        bare FakeTensor.
+        """
+        arg = _make_tensor_arg_node((1, 3, 8, 8))
+        node = _make_op_node(
+            target=torch.ops.aten.split_with_sizes_copy.default,
+            args=(arg, [3]),
+            output_val=[_make_fake_tensor((1, 3, 8, 8))],
+        )
+
+        op_repsets = OpRepSets(
+            TensorRepSetList(ANY_STORAGE),
+            TensorRepSetList(ANY_STORAGE),
+            node,
+            DEFAULT_TEXTURE_LIMITS,
+        )
+
+        self.assertFalse(op_repsets.any_is_empty())
+
     def test_binary_op_syncs_args(self):
         """When a single repset covers all inputs, sync_args_repr is True."""
         op_repsets = self._make_binary_op()
diff --git a/backends/vulkan/utils.py b/backends/vulkan/utils.py
index 84b901b6b6e..066ee78ea0d 100644
--- a/backends/vulkan/utils.py
+++ b/backends/vulkan/utils.py
@@ -1442,8 +1442,15 @@ def __init__(  # noqa: C901
         outs_repset_list = TensorRepSetList([])
         common_out_repset = ANY_STORAGE_INCL_PACKED_INT8
         if num_tensors_in_node(op_node) == 1:
+            out_val = op_node.meta["val"]
+            # num_tensors_in_node counts tensors, not nesting: an op declared
+            # to return Tensor[] still lands here when it happens to produce
+            # exactly one, and meta["val"] is then a one-element list rather
+            # than a bare FakeTensor.
+            if isinstance(out_val, (list, tuple)):
+                out_val = out_val[0]
             common_out_repset = filter_invalid_reprs(
-                op_node.meta["val"], outputs_repsets[0], texture_limits
+                out_val, outputs_repsets[0], texture_limits
             )
             outs_repset_list.append(common_out_repset)
         # Multiple output tensors