PsychAgent: 一种基于经验的终身学习代理用于自我进化的心理辅导员
PsychAgent: An Experience-Driven Lifelong Learning Agent for Self-Evolving Psychological Counselor
AI总结：
本文提出PsychAgent，通过长期多会话交互的内存增强规划引擎、技能进化引擎和强化内化引擎，实现心理辅导员的自我进化，提升多会话咨询的一致性和质量。
摘要：
现有AI心理辅导员主要依赖静态对话数据集的监督微调，而人类专家通过临床实践和积累经验不断精进。为弥合这一差距，我们提出PsychAgent，首先建立适用于长期多会话交互的内存增强规划引擎，通过持久记忆和战略规划确保治疗连续性；其次设计技能进化引擎，从历史咨询轨迹中提取新实践基础的技能；最后引入强化内化引擎，通过拒绝微调将进化技能整合进模型，以提升在多样场景下的性能。对比分析显示，我们的方法在所有报告的评估维度上均优于强通用LLM（如GPT-5.4、Gemini-3）和领域特定基线。这些结果表明，终身学习可以提高多会话咨询响应的一致性和整体质量。
英文摘要：
Existing methods for AI psychological counselors predominantly rely on supervised fine-tuning using static dialogue datasets. However, this contrasts with human experts, who continuously refine their proficiency through clinical practice and accumulated experience. To bridge this gap, we propose an Experience-Driven Lifelong Learning Agent (\texttt{PsychAgent}) for psychological counseling. First, we establish a Memory-Augmented Planning Engine tailored for longitudinal multi-session interactions, which ensures therapeutic continuity through persistent memory and strategic planning. Second, to support self-evolution, we design a Skill Evolution Engine that extracts new practice-grounded skills from historical counseling trajectories. Finally, we introduce a Reinforced Internalization Engine that integrates the evolved skills into the model via rejection fine-tuning, aiming to improve performance across diverse scenarios. Comparative analysis shows that our approach achieves higher scores than strong general LLMs (e.g., GPT-5.4, Gemini-3) and domain-specific baselines across all reported evaluation dimensions. These results suggest that lifelong learning can improve the consistency and overall quality of multi-session counseling responses.
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。
