diff --git a/src/winml/modelkit/analyze/core/runtime_checker_query.py b/src/winml/modelkit/analyze/core/runtime_checker_query.py
index ec0766c36..24f4b1339 100644
--- a/src/winml/modelkit/analyze/core/runtime_checker_query.py
+++ b/src/winml/modelkit/analyze/core/runtime_checker_query.py
@@ -724,9 +724,12 @@ def update_conditions_(
             update_conditions_(conditions, input_name, is_variadic, is_constant, shape, None)
             conditions[f"{input_name}_is_none"] = False
 
+    conditions["n_outputs"] = len(node.output)
+
     # Try to derive properties, but catch errors for incomplete/invalid model information
     try:
         conditions = runtime_checker_op.derive_properties(conditions)
+        conditions.pop("n_outputs", None)
     except (KeyError, TypeError, IndexError) as e:
         # KeyError: missing required property (e.g., 'input_value', 'input_shape')
         # TypeError: invalid property value (e.g., None when expecting iterable)
@@ -816,10 +819,13 @@ def _compute_dynamic_axes(shape: tuple | None, is_constant: bool) -> tuple[int,
         conditions[f"attr_{attr_name}"] = attr_value
         conditions[f"attr_{attr_name}_is_none"] = attr_value is None
 
+    conditions["n_outputs"] = len(pattern_match.skeleton_match_result.pattern.get_schema().outputs)
+
     # Derive additional properties via pattern input generator
     if gen is not None:
         try:
             conditions = gen.derive_properties(conditions)
+            conditions.pop("n_outputs", None)
             infinite_properties = gen.get_infinite_property_names()
         except Exception as e:
             logger.debug("Could not derive properties for pattern '%s': %s", pattern_name, e)
diff --git a/src/winml/modelkit/pattern/op_input_gen/indexing_input_generator.py b/src/winml/modelkit/pattern/op_input_gen/indexing_input_generator.py
index e481b75fe..045cf678d 100644
--- a/src/winml/modelkit/pattern/op_input_gen/indexing_input_generator.py
+++ b/src/winml/modelkit/pattern/op_input_gen/indexing_input_generator.py
@@ -818,9 +818,8 @@ def derive_properties(self, properties: dict) -> dict:
             split_array = np.array(item["attr_split"])
             item["num_outputs"] = len(split_array)
         else:
-            raise ValueError(
-                "Shouldn't reach here: either split_value or attr_num_outputs must be present."
-            )
+            # caller is get_query_conditions_for_*, "n_outputs" must be present
+            item["num_outputs"] = item["n_outputs"]
         # num_outputs may already be set from the combination
         return item
 
diff --git a/src/winml/modelkit/pattern/op_input_gen/slice_input_generator.py b/src/winml/modelkit/pattern/op_input_gen/slice_input_generator.py
index 0a14fc87e..1910ef982 100644
--- a/src/winml/modelkit/pattern/op_input_gen/slice_input_generator.py
+++ b/src/winml/modelkit/pattern/op_input_gen/slice_input_generator.py
@@ -221,8 +221,20 @@ def derive_properties(self, properties: dict[str, Any]) -> dict[str, Any]:
 
         # Normalize negative axes to positive for comparison
         normalized_axes = np.where(axes_array < 0, axes_array + data_dim, axes_array)
-        # Get dimension sizes for the sliced axes
-        axis_dims = np.array([data_shape[int(ax)] for ax in normalized_axes], dtype=np.int64)
+
+        # Filter to axes whose dimensions are fixed (int, not string/symbolic)
+        fixed_mask = np.array(
+            [
+                isinstance(data_shape[int(ax)], int) and data_shape[int(ax)] >= 0
+                for ax in normalized_axes
+            ]
+        )
+
+        # Get dimension sizes only for fixed-shape axes
+        axis_dims = np.array(
+            [data_shape[int(ax)] if fixed_mask[i] else 0 for i, ax in enumerate(normalized_axes)],
+            dtype=np.int64,
+        )
 
         # Derive starts-related properties
         starts_value = item.get("starts_value")
@@ -231,7 +243,7 @@ def derive_properties(self, properties: dict[str, Any]) -> dict[str, Any]:
         else:
             starts_array = np.array(starts_value, dtype=np.int64)
 
-        # Normalize negative starts: add axis_dim if negative
+        # Normalize negative starts: add axis_dim if negative (only meaningful for fixed axes)
         normalized_starts = np.where(starts_array < 0, starts_array + axis_dims, starts_array)
 
         # Derive ends-related properties
@@ -241,16 +253,19 @@ def derive_properties(self, properties: dict[str, Any]) -> dict[str, Any]:
         else:
             ends_array = np.array(ends_value, dtype=np.int64)
 
-        # Normalize negative ends: add axis_dim if negative
+        # Normalize negative ends: add axis_dim if negative (only meaningful for fixed axes)
         normalized_ends = np.where(ends_array < 0, ends_array + axis_dims, ends_array)
 
-        # Check if starts are at the last element of each sliced axis
-        item["starts_equal_shape"] = bool(np.all(normalized_starts == axis_dims - 1))
-
-        # Check if this is a full slice (starts at 0, ends at dimension size)
-        item["slice_all"] = bool(
-            np.all(normalized_starts == 0) and np.all(normalized_ends >= axis_dims)
-        )
+        # Check starts_equal_shape and slice_all only on fixed-shape axes
+        if np.any(fixed_mask):
+            fixed_starts = normalized_starts[fixed_mask]
+            fixed_ends = normalized_ends[fixed_mask]
+            fixed_dims = axis_dims[fixed_mask]
+            item["starts_equal_shape"] = bool(np.all(fixed_starts == fixed_dims - 1))
+            item["slice_all"] = bool(np.all(fixed_starts == 0) and np.all(fixed_ends >= fixed_dims))
+        else:
+            item["starts_equal_shape"] = False
+            item["slice_all"] = False
 
         # Derive steps-related properties
         steps_value = item.get("steps_value")