DELTA用多智能体强化学习实现多模态心理辅导，提升情感共鸣能力。
DELTA: Deliberative Multi-Agent Reasoning with Reinforcement Learning for Multimodal Psychological Counseling
- 多智能体分步处理语音、文字、视觉信号，结构化推理心理状态
- 通过情感契合度评分提升模型回应的情感共鸣效果
- 适合研究人机共情、多模态交互与心理AI的开发者
心理辅导本质上是一种多模态认知过程，临床医生需整合语言内容与视觉、语音线索以推断来访者心理状态并作出共情回应。然而，现有基于语言模型的辅导系统仅依赖文本，依赖隐式心理状态推断。我们提出DELTA，一种基于强化学习的思辨式多智能体框架，将辅导建模为对多模态信号的结构化推理过程，分离证据定位、心理状态抽象与回应生成。DELTA进一步引入分布级情感契合度得分（Emotion Attunement Score）指导强化学习，鼓励情感贴合的回应。在多模态辅导基准上的实验表明，DELTA在不同模型上均提升了辅导质量与情感契合度。消融与定性分析显示，显式多模态推理与结构化心理状态表示在支持人机共情交互中起互补作用。
原文摘要 · Abstract (English)
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.
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