point to #778 (#785)

mltensor-explainer.md

@@ -292,7 +292,8 @@ It's possible `compute()` may have a performance advantage on some platforms for
 
 ### Open Questions
 
-- How will errors be surfaced? Do we need a concept similar to [WebGPU's error scopes](https://www.w3.org/TR/webgpu/#error-scopes), or is [returning errors via a promise for select operations](https://github.com/webmachinelearning/webnn/issues/697#issuecomment-2195656878) and losing the `MLContext` sufficient? See [#477](https://github.com/webmachinelearning/webnn/issues/477)
+- How will errors be surfaced? Do we need a concept similar to [WebGPU's error scopes](https://www.w3.org/TR/webgpu/#error-scopes), or is [returning errors via a promise for select operations](https://github.com/webmachinelearning/webnn/issues/697#issuecomment-2195656878) and losing the `MLContext` sufficient? 
+  - *Update: [#778](https://github.com/webmachinelearning/webnn/issues/778) is a proposal for reporting non-fatal errors from the WebNN timeline*
 - Does the user agent have enough information to appropriately allocate an `MLTensor` if an `MLDeviceType` or `GPUDevice` is not used to create an `MLContext`? See [#350](https://github.com/webmachinelearning/webnn/issues/350) and [#749](https://github.com/webmachinelearning/webnn/issues/749)
 - Should the `dispatch()` method be a part of the `MLGraph` interface rather than `MLContext`? Should `readTensor()` and `writeTensor()` exist on an `MLTensor`? See [#697](https://github.com/webmachinelearning/webnn/issues/697).
 - Is a sync variant of the `importExternalBuffer()` method feasible (1) on platforms where completion of ML compute can be signaled on a GPU timeline, or (2) when blocking WebGPU workloads which do not themselves block compositing.