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The paper presents the Planner-Composer-Evaluator (PCE) framework, which enhances uncertainty-aware planning for embodied agents in multi-agent environments by converting assumptions from Large Language Model (LLM) reasoning into a structured decision tree. PCE outperforms traditional communication-heavy methods in success rate and task efficiency while maintaining comparable token usage, thus reducing communication costs and improving collaboration with human partners. The framework is shown to be versatil

Author

krisNiswander

Language

EN