Kind of reminds me of this more recent paper: https://arxiv.org/html/2602.02459v1 Different use case and implementation, but a similar idea. In this case applied to sharing last state(not the whole KV cache) from big brain model running in the cloud with a smaller/dumber model running on-device in a robot, in a latancy-aware way.
Thanks for the link. This is the kind of "edge AI" adaptation that I expect to see more of in the future.
A "local 8B VLA" that runs at high frequency can be sufficient for things like sensor fusion, plan execution and manipulation dynamics, but insufficient for advanced problem solving and in-depth "exception handling". So being able to ask a larger model for steering cues is going to be valuable for AI robots. And that does introduce latency and interface boundaries.
Not sure if the exact approach described there is the way to go, but I do expect something similar to show up in deployments eventually. If nothing else, having this one described does provide a point of comparison.