diff --git a/onnxscript/function_libs/torch_lib/ops/core.py b/onnxscript/function_libs/torch_lib/ops/core.py
index 27d30e377f..02fde7b5c9 100644
--- a/onnxscript/function_libs/torch_lib/ops/core.py
+++ b/onnxscript/function_libs/torch_lib/ops/core.py
@@ -8791,8 +8791,9 @@ def aten_roll(self: TTensor, shifts: Sequence[int], dims: Sequence[int] = ()) ->
     if self_rank == 0:
         return op.Identity(self)
     elif 0 in self.shape:
-        # A tensor with no elements rolls to itself, which is what torch returns. It is
-        # also what keeps a zero length out of the modulo in the helpers below.
+        # A tensor with no elements rolls to itself, which is what torch returns. It also
+        # keeps a zero length out of the modulo in the helpers below, but only when the
+        # length is known to be zero at export time. See the limitation noted there.
         return op.Identity(self)
 
     # NOTE: In pytorch, default value of dims is an empty list.
@@ -8858,6 +8859,12 @@ def aten_roll_complex(
 def _aten_roll_shift_no_dim_onnx(self: TTensor, shift: int) -> TTensor:
     # The element count is the divisor of the Mod below, and Mod by zero is undefined in
     # ONNX. Both callers return a tensor with no elements unchanged before reaching here.
+    # Known limitation: that early return and this assert only cover a length known to be
+    # zero at export time. A dynamic dimension that is zero at run time still reaches Mod
+    # with a zero divisor, which ONNX leaves undefined. Runtimes differ: the onnxruntime CPU
+    # kernel rejects an integer divisor of zero, its CUDA kernel has no such check, and
+    # onnxruntime before 1.25 returned the dividend, which the Slice calls below clamp back
+    # into range so the result still looks correct.
     assert self.shape is None or 0 not in self.shape, (
         "the element count must not be zero because Mod by zero is undefined"
     )
@@ -8887,6 +8894,12 @@ def _aten_roll_shift_and_dim_onnx(self: TTensor, shift: int, dim: int) -> TTenso
     # The length of that dimension is the divisor of the Mod below, and Mod by zero is
     # undefined in ONNX. Both callers return a tensor with no elements unchanged before
     # reaching here.
+    # Known limitation: that early return and this assert only cover a length known to be
+    # zero at export time. A dynamic dimension that is zero at run time still reaches Mod
+    # with a zero divisor, which ONNX leaves undefined. Runtimes differ: the onnxruntime CPU
+    # kernel rejects an integer divisor of zero, its CUDA kernel has no such check, and
+    # onnxruntime before 1.25 returned the dividend, which the Slice calls below clamp back
+    # into range so the result still looks correct.
     assert self.shape is None or self.shape[dim] != 0, (
         "the dimension length must not be zero because Mod by zero is undefined"
     )