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Language-model agents are expected to adapt to others whose behavior changes through interaction. Yet adaptation may rely on learned behavioral patterns rather than theory of mind (ToM): the ability to infer others'mental states, including beliefs and intentions. We introduce a benchmark and diagnostic framework for re...
A two-level hierarchical reinforcement learning (HRL) framework for conversational agents that bridges the gap between existing token-level and utterance-level RL methods and introduces a dual-granularity reward mechanism that combines the utterance-level satisfaction score with token-level intrinsic self-consistency a...
An emotion-planning framework that determines the emotion prior to the textual generation, grounding the downstream emotional TTS in a streaming manner, and finally implements an entire streaming pipeline for real-time deployment.
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