diff --git a/backends/cadence/aot/compiler_funcs.py b/backends/cadence/aot/compiler_funcs.py
index e8c0f2a602b..2f8dbf33416 100644
--- a/backends/cadence/aot/compiler_funcs.py
+++ b/backends/cadence/aot/compiler_funcs.py
@@ -22,6 +22,28 @@
 
 logger: logging.Logger = logging.getLogger(__name__)
 QuantArgs = tuple[float, int, int, int, torch.dtype]
+TRANSPARENT_OPS: frozenset[torch._ops.OpOverloadPacket] = frozenset(
+    {
+        torch.ops.aten.view,
+        torch.ops.aten.view_copy,
+        torch.ops.aten._unsafe_view,
+        torch.ops.aten.reshape,
+        torch.ops.aten.permute,
+        torch.ops.aten.permute_copy,
+        torch.ops.aten.transpose,
+        torch.ops.aten.transpose_copy,
+        torch.ops.aten.squeeze,
+        torch.ops.aten.squeeze_copy,
+        torch.ops.aten.unsqueeze,
+        torch.ops.aten.unsqueeze_copy,
+        torch.ops.aten.slice,
+        torch.ops.aten.slice_copy,
+        torch.ops.aten.contiguous,
+        torch.ops.aten.clone,
+        torch.ops.aten.to,
+        torch.ops.aten._to_copy,
+    }
+)
 
 
 @torch.no_grad()
@@ -244,6 +266,11 @@ def extract_input_quant_params_from_graph(
 ) -> dict[int, QuantArgs]:
     """
     Extract quantization parameters from the FX graph for model inputs.
+
+    For each name in ``input_names``, walk forward from the matching input
+    node through value-preserving "transparent" ops (reshape, permute, ...)
+    until reaching the ``quantize_per_tensor`` that fixes that input's scale
+    and zero-point. Results are keyed by the index into ``input_names``.
     """
     quant_args: dict[int, QuantArgs] = {}
     found_names: set[str] = set()
@@ -251,29 +278,39 @@ def extract_input_quant_params_from_graph(
     if not input_names:
         return quant_args
 
+    # Inputs are referenced by node name, which may be a placeholder or a node
+    # that unpacks/derives the input (e.g. a `getitem` off a tuple/multi-output
+    # input, as the modai eye-tracking model does), so look the start node up
+    # across all nodes -- not just placeholders. Build the name->node map once
+    # and reuse it for every requested input.
+    nodes_by_name = {n.name: n for n in module.graph.nodes}
+
+    quantize_ops = _get_quantize_ops()
     for idx, name in enumerate(input_names):
-        for node in module.graph.nodes:
-            if node.op != "call_function":
+        start = nodes_by_name.get(name)
+        if start is None:
+            continue
+        seen: set[torch.fx.Node] = set()
+        to_visit: list[torch.fx.Node] = list(start.users)
+        while to_visit:
+            node = to_visit.pop()
+            if node in seen or node.op != "call_function":
                 continue
-
-            if (
-                node.args
-                and isinstance(node.args[0], torch.fx.Node)
-                and node.args[0].name == name
-                and not node.name.startswith("_assert_tensor_metadata")
-                and "quantize_per_tensor" in str(node.target)
-            ):
-                args = node.args[1:]
-                if len(args) >= 5:
-                    quant_args[idx] = (
-                        float(args[0]),  # scale
-                        int(args[1]),  # zero_point
-                        int(args[2]),  # qmin
-                        int(args[3]),  # qmax
-                        args[4],  # dtype
-                    )
-                    found_names.add(name)
+            seen.add(node)
+            if node.target in quantize_ops:
+                # Normalize args→kwargs so params passed positionally or as
+                # kwargs (or via defaults) are all handled uniformly.
+                quant_args[idx] = (
+                    float(get_arg(node, "scale", float)),
+                    int(get_arg(node, "zero_point", int)),
+                    int(get_arg(node, "quant_min", int)),
+                    int(get_arg(node, "quant_max", int)),
+                    get_arg(node, "dtype", torch.dtype),
+                )
+                found_names.add(name)
                 break
+            if getattr(node.target, "overloadpacket", None) in TRANSPARENT_OPS:
+                to_visit.extend(node.users)
 
     missing_names = set(input_names) - found_names
     if missing_names: