diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index f9c8589683ba7..27988a451173c 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -5010,15 +5010,14 @@ template SmallVector<int64_t>
     getPackedOuterShapeWithoutTransposition<UnPackOp>(UnPackOp);
 
 // Given the (potentially) updated packed type, `newPackedTy`, generates an
-// updated mixed-tile-sizes attribute. A tile size is updated only
-// when:
-//  * a dim from newPackedTy is static, and
-//  * the corresponding size from mixedTiles is still dynamic.
-// Otherwise, the original tile size is preserved.
+// updated mixed-tile-sizes list. For each inner packed dimension that is static
+// in `newPackedTy`, the tile is set to that static size (replacing SSA values
+// or mismatched constants). Dynamic packed dimensions preserve the original
+// tile. The folded tensor type is treated as authoritative for static extents.
 // Note - packed-type-dim and mixed-tile-size should always match!
 static SmallVector<OpFoldResult>
 getNewMixedTileSizes(PatternRewriter &rewriter, Type newPackedTy,
-                     SmallVector<OpFoldResult> mixedTiles) {
+                     ArrayRef<OpFoldResult> mixedTiles) {
   SmallVector<OpFoldResult> newMixedTileSizes;
   for (auto it : llvm::zip(cast<ShapedType>(newPackedTy)
                                .getShape()
@@ -5029,19 +5028,7 @@ getNewMixedTileSizes(PatternRewriter &rewriter, Type newPackedTy,
       newMixedTileSizes.push_back(std::get<1>(it));
       continue;
     }
-
-    // If the current result dim is static, update the dynamic mixed-size
-    // (provided the original value is dynamic).
-    OpFoldResult tile = std::get<1>(it);
-    if (Attribute attr = llvm::dyn_cast_if_present<Attribute>(tile)) {
-      // Already a constant
-      newMixedTileSizes.push_back(tile);
-    } else {
-      assert(getConstantIntValue(tile).value() == dimSize &&
-             "tile size and dim size don't match!");
-      newMixedTileSizes.push_back(
-          (rewriter.getIntegerAttr(rewriter.getIndexType(), dimSize)));
-    }
+    newMixedTileSizes.push_back(rewriter.getIndexAttr(dimSize));
   }
 
   return newMixedTileSizes;
diff --git a/mlir/test/Dialect/Linalg/canonicalize.mlir b/mlir/test/Dialect/Linalg/canonicalize.mlir
index 0c5a1c6108ae3..019b7433b2777 100644
--- a/mlir/test/Dialect/Linalg/canonicalize.mlir
+++ b/mlir/test/Dialect/Linalg/canonicalize.mlir
@@ -2179,3 +2179,82 @@ func.func @negative_unpack_pack_memref_no_canonicalization(%packed: memref<16x8x
   linalg.pack %unpacked inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %dest : memref<128x256xf32> -> memref<16x8x8x32xf32>
   return
 }
+
+// -----
+// CHECK-LABEL: func.func @fold_unpack_cast_inner_tile_dynamic_arg
+// CHECK-SAME:  %[[SRC:.+]]: tensor<1x3x8x1xi32>, %[[TILE:.+]]: index
+// CHECK:   %[[EMPTY:.+]] = tensor.empty() : tensor<7x3xi32>
+// CHECK:       %[[UNPACK:.+]] = linalg.unpack %[[SRC]]
+// CHECK-SAME:    inner_dims_pos = [0, 1]
+// CHECK-SAME:    inner_tiles = [8, 1]
+// CHECK-SAME:    into %[[EMPTY]] : tensor<1x3x8x1xi32> -> tensor<7x3xi32>
+// CHECK:       return %[[UNPACK]] : tensor<7x3xi32>
+func.func @fold_unpack_cast_inner_tile_dynamic_arg(%arg0: tensor<1x3x8x1xi32>, %arg1: index) -> tensor<7x3xi32> {
+  %0 = tensor.empty() : tensor<7x3xi32>
+  %cast = tensor.cast %arg0 : tensor<1x3x8x1xi32> to tensor<?x3x?x1xi32>
+  %unpack = linalg.unpack %cast inner_dims_pos = [0, 1] inner_tiles = [%arg1, 1] into %0 : tensor<?x3x?x1xi32> -> tensor<7x3xi32>
+  return %unpack : tensor<7x3xi32>
+}
+
+
+// -----
+// Mismatched constant tile vs static packed shape: fold still drops the cast and
+// takes inner tile sizes from the refined packed type.
+// CHECK-LABEL: func.func @fold_unpack_cast_inner_tile_inlined_mismatch
+// CHECK:       %[[EMPTY:.+]] = tensor.empty() : tensor<7x3xi32>
+// CHECK-NOT:   tensor.cast
+// CHECK:       %[[UNPACK:.+]] = linalg.unpack %{{.+}} inner_dims_pos = [0, 1] inner_tiles = [8, 1]
+// CHECK-SAME:    into %[[EMPTY]] : tensor<1x3x8x1xi32> -> tensor<7x3xi32>
+// CHECK:       return %[[UNPACK]] : tensor<7x3xi32>
+func.func @fold_unpack_cast_inner_tile_inlined_mismatch(%arg0: tensor<1x3x8x1xi32>) -> tensor<7x3xi32> {
+  %c256 = arith.constant 256 : index
+  %1 = tensor.empty() : tensor<7x3xi32>
+  %cast = tensor.cast %arg0 : tensor<1x3x8x1xi32> to tensor<?x3x?x1xi32>
+  %unpack = linalg.unpack %cast inner_dims_pos = [0, 1] inner_tiles = [%c256, 1] into %1 : tensor<?x3x?x1xi32> -> tensor<7x3xi32>
+  return %unpack : tensor<7x3xi32>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @no_fold_pack_cast_inner_tile_dynamic_arg
+// CHECK-SAME:  %[[SRC:.+]]: tensor<8x3xi32>, %[[TILE:.+]]: index, %[[DEST:.+]]: tensor<?x3x?x1xi32>
+// CHECK:       %[[PACK:.+]] = linalg.pack
+// CHECK:         padding_value
+// CHECK:         inner_dims_pos = [0, 1]
+// CHECK:         inner_tiles = [%[[TILE]], 1]
+// CHECK:         into %[[DEST]] : tensor
+// CHECK:       return %[[PACK]] : tensor<?x3x?x1xi32>
+func.func @no_fold_pack_cast_inner_tile_dynamic_arg(%arg0: tensor<8x3xi32>, %arg1: index,
+    %dest: tensor<?x3x?x1xi32>) -> tensor<?x3x?x1xi32> {
+  %c0 = arith.constant 0 : i32
+  %cast = tensor.cast %arg0 : tensor<8x3xi32> to tensor<?x?xi32>
+  %pack = linalg.pack %cast
+    padding_value(%c0 : i32)
+    inner_dims_pos = [0, 1]
+    inner_tiles = [%arg1, 1]
+    into %dest : tensor<?x?xi32> -> tensor<?x3x?x1xi32>
+  return %pack : tensor<?x3x?x1xi32>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @no_fold_pack_cast_inner_tile_inlined_mismatch
+// CHECK-DAG:   %[[C256:.+]] = arith.constant 256 : index
+// CHECK:       %[[PACK:.+]] = linalg.pack
+// CHECK:         padding_value
+// CHECK:         inner_dims_pos = [0, 1]
+// CHECK:         inner_tiles = [%[[C256]], 1]
+// CHECK:         into %{{.+}} : tensor
+// CHECK:       return %[[PACK]] : tensor<?x3x?x1xi32>
+func.func @no_fold_pack_cast_inner_tile_inlined_mismatch(%arg0: tensor<8x3xi32>,
+    %dest: tensor<?x3x?x1xi32>) -> tensor<?x3x?x1xi32> {
+  %c0 = arith.constant 0 : i32
+  %c256 = arith.constant 256 : index
+  %cast = tensor.cast %arg0 : tensor<8x3xi32> to tensor<?x?xi32>
+  %pack = linalg.pack %cast
+    padding_value(%c0 : i32)
+    inner_dims_pos = [0, 1]
+    inner_tiles = [%c256, 1]
+    into %dest : tensor<?x?xi32> -> tensor<?x3x?x1xi32>
+  return %pack : tensor<?x3x?x1xi32>
+}