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Achieve 100% agent recovery correctness with near-zero overhead by intelligently checkpointing only the OS state that actually matters.
Unlocking the potential of compute-in-memory accelerators for LLMs requires carefully navigating a complex dataflow design space, and AccelCIM provides the first systematic framework to do so.
LLMs can translate free-form natural language feedback from users with paralysis into safe and personalized robot control policies, drastically reducing the burden of preference learning.
