Paper recorded by Signals 4 on 2026-10-07 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-07 on arXiv · recorded by Signals 4 on 2026-10-08
Category: cs.AI · 人工智能 · first seen 2026-10-08
Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Long-WAM, a model-system framework for scaling the context of causal world-action models under real-time control constraints. Our central finding is that access to history is not the same as using it: longer histories pay off far more when the video foun