Foundation models are becoming remarkably reliable at high-level reasoning, yet they remain too slow and costly to serve as fine-grained controllers. Conversely, fast policies like VLAs and whole-body
REA unifies deliberative and reactive policies in a single runtime with asynchronous coordination, achieving high success rates with reduced control overhead across simulated and real robot tasks.
Foundation models are becoming remarkably reliable at high-level reasoning, yet they remain too slow and costly to serve as fine-grained controllers. Conversely, fast policies like VLAs and whole-body controllers offer more efficient reflexes, but struggle to generalize. While pairing a deliberative brain (foundation models) with a reactive cerebellum (fast policies) seems natural for embodied systems, two obstacles remain: methods are built and scored on incompatible stacks, and their asynchronous coordination is difficult. For example, an agent that calls the VLA in the agentic loop can stall the fast execution, while a VLA that calls the agent on errors tends to re-trigger fallbacks…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
REA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 13 checks on 2 different days. Its best position was #1 of 30, reached on Nov 3, 05:28 UTC. It dropped off the list after Nov 3, 09:28 UTC.
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Not at the moment. It was last on the list on Nov 3, 09:28 UTC, after 2 days of trending.
#1 on Hugging Face · Daily papers, on Nov 3, 05:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 2, 2026 and has been on the list on 2 days (13 checks) since.