diff --git a/backends/cadence/aot/memory_constraints.py b/backends/cadence/aot/memory_constraints.py
index af85a62fc7e..2b6c9a049da 100644
--- a/backends/cadence/aot/memory_constraints.py
+++ b/backends/cadence/aot/memory_constraints.py
@@ -452,6 +452,45 @@ def is_cat_along_outermost_dim(
                 return False
         return True
 
+    def _has_duplicate_resolved_sources(
+        self, cat_tensors: Sequence[torch.fx.Node]
+    ) -> bool:
+        """Return True if two cat inputs resolve to the same underlying tensor."""
+        if len(cat_tensors) != len(set(cat_tensors)):
+            return True
+        resolved_sources = set()
+        for arg in cat_tensors:
+            resolved = arg
+            while (
+                info := self.constraint.get_relative_placement_source(resolved)
+            ) is not None:
+                if self.constraint.is_alias_of(info.source, resolved):
+                    resolved = info.source
+                else:
+                    break
+            if id(resolved) in resolved_sources:
+                return True
+            resolved_sources.add(id(resolved))
+        return False
+
+    def _has_unaligned_cat_tensors(
+        self,
+        graph: torch.fx.Graph,
+        node: torch.fx.Node,
+        cat_tensors: Sequence[torch.fx.Node],
+    ) -> bool:
+        """Return True if any non-placeholder cat tensor has misaligned offset."""
+        if is_node_in_flattened_output(graph, node):
+            return False
+        expected_alignment = 8
+        relative_offsets = get_relative_offsets_of_cat_tensors(cat_tensors)
+        for idx, arg in enumerate(cat_tensors):
+            if not (arg.op == "placeholder") and (
+                relative_offsets[idx] & (expected_alignment - 1) != 0
+            ):
+                return True
+        return False
+
     # If A = cat(B, C), and the concatenation is along the outermost dimension, then
     # we can optimize away this cat operation if (1) B and C are placed contiguously,
     # and (2) the absolute memory location of tensor A is the same as B. This function
@@ -486,21 +525,17 @@ def is_removable_cat_op(
             return False
         # If the same tensor appears multiple times in the cat inputs,
         # we cannot place it at multiple different offsets relative to the output.
-        if len(cat_tensors) != len(set(cat_tensors)):
+        # Also check resolved sources: two different alias nodes may resolve to
+        # the same underlying tensor, which can't be at two offsets.
+        if self._has_duplicate_resolved_sources(cat_tensors):
             return False
 
         # Many ops in HiFi require the input to be aligned to 8-byte boundary.
         # If the cat is not the graph's output, then ensure that the relative
         # offset of any concatenated non-placeholder tensor is a multiple of
         # 8 bytes,
-        if not is_node_in_flattened_output(graph_module.graph, node):
-            expected_alignment = 8
-            relative_offsets = get_relative_offsets_of_cat_tensors(cat_tensors)
-            for idx, arg in enumerate(cat_tensors):
-                if not (arg.op == "placeholder") and (
-                    relative_offsets[idx] & (expected_alignment - 1) != 0
-                ):
-                    return False
+        if self._has_unaligned_cat_tensors(graph_module.graph, node, cat_tensors):
+            return False
 
         return True