DELTA: 基于强化学习的多智能体推理用于多模态心理辅导
DELTA: Deliberative Multi-Agent Reasoning with Reinforcement Learning for Multimodal Psychological Counseling
AI总结：
DELTA 通过强化学习和多智能体推理提升多模态心理辅导的质量与情感契合度。
摘要：
心理辅导本质上是一个多模态的认知过程，临床医生整合口头内容与视觉和语音线索以推断客户的心理状态并产生共情回应。然而，大多数现有的基于语言模型的辅导系统仅依赖文本，并依赖于隐含的心理状态推断。我们介绍了DELTA，一个 deliberative 多智能体框架，将辅导建模为对多模态信号的结构化推理过程，将证据 grounding、心理状态抽象和回应生成分离开来。DELTA 进一步结合由分布层面的 Emotion Attunement Score 引导的强化学习，以鼓励产生情感契合的回应。在多模态辅导基准测试中，DELTA 在不同模型上提高了辅导质量和情感契合度。消融分析和定性分析表明，显式的多模态推理和结构化的心理状态表示在支持共情的人机交互中起互补作用。
英文摘要：
Psychological counseling is a fundamentally multimodal cognitive process in which clinicians integrate verbal content with visual and vocal cues to infer clients' mental states and respond empathically. However, most existing language-model-based counseling systems operate on text alone and rely on implicit mental state inference. We introduce DELTA, a deliberative multi-agent framework that models counseling as a structured reasoning process over multimodal signals, separating evidence grounding, mental state abstraction, and response generation. DELTA further incorporates reinforcement learning guided by a distribution-level Emotion Attunement Score to encourage emotionally attuned responses. Experiments on a multimodal counseling benchmark show that DELTA improves both counseling quality and emotion attunement across models. Ablation and qualitative analyses suggest that explicit multimodal reasoning and structured mental state representations play complementary roles in supporting empathic human-AI interaction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
