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Closed-loop learning in embodied agents can lead to a staggering 11.1x speedup in inference while achieving record performance on complex tasks.
Optimizing multiple moments of failure probabilities can dramatically enhance LLM reasoning performance, outperforming traditional single-moment approaches.
Allocating rollout budgets based on state informativeness allows LLM agents to achieve superior performance in complex decision-making tasks without increasing computational costs.
Proactive VideoLLMs can finally be both accurate AND efficient thanks to a novel propose-match framework that decouples semantic understanding from streaming perception.
