Thanks for the feedback on the formatting. While I do use tools to help structure thoughts and edit for clarity (which might explain the lists and phrasing you noticed), the core technical analysis regarding the challenges of optical flow vs. spatiotemporal AI stems directly from our actual engineering work in building video restoration models. The goal was to make complex concepts digestible, but I appreciate the note on style. I hope the substance of the technical argument still comes through.

This is a fantastic insight. You absolutely nailed why the 'dog on a beach' scenario is the ultimate stress test for temporal consistency. You are right that the fundamental problem exists even in a film model composed of static images. The challenge isn't just filling the hole; it's dealing with the background's non-rigid, stochastic motion (like waves lapping). A generative model can easily hallucinate a plausible static wave infill for a single frame. But ensuring those hallucinations transition smoothly across t-1, t, and t+1 without jittering or warping is exactly the 'uncanny valley' of motion we are trying to solve. It has to understand the physics of the wave motion, not just the texture. Thanks for this thoughtful analysis.