CATCH：面向AI心理咨询中高治疗保真度与记忆驱动规划思维链的新型数据合成框架
CATCH: A Novel Data Synthesis Framework for High Therapy Fidelity and Memory-Driven Planning Chain of Thought in AI Counseling
AI总结：
针对现有AI心理咨询数据合成方法治疗保真度低、缺乏回复决策逻辑的问题，提出CATCH框架，通过渐进式对话合成与记忆驱动动态规划思维链生成高保真多轮咨询数据，经实验验证显著提升保真度与逻辑连贯性。
摘要：
近年来，基于大语言模型的AI心理咨询研究已取得显著进展。然而，现有研究采用一次性生成的方法合成多轮对话样本，导致治疗保真度较低，且无法捕捉每一条回复背后的决策逻辑。针对这些挑战，本文提出一种新型数据合成框架CATCH。具体而言，为提升治疗保真度，我们引入渐进式对话合成策略：从来访者的自我陈述中提取目标、资源与解决方案，将其组织为结构化大纲，再逐步生成与阶段匹配的咨询对话。为捕捉每条回复的决策逻辑，我们提出记忆驱动动态规划思维模式，整合记忆增强、全局规划与策略推理；随后由多智能体协同优化器利用MDP（马尔可夫决策过程）为每轮对话附加显式思维链。大量实验与人工评估表明，CATCH显著提升了AI心理咨询的保真度与逻辑连贯性。
英文摘要：
Recently, advancements in AI counseling based on large language models have shown significant progress. However, existing studies employ a one-time generation approach to synthesize multi-turn dialogue samples, resulting in low therapy fidelity and failing to capture the decision-making rationale behind each response. In this work, we propose CATCH, a novel data synthesis framework designed to address these challenges. Specifically, to improve therapy fidelity, we introduce the Progressive Dialogue Synthesis strategy, which extracts goals, resources, and solutions from a client's self-report, organizes them into structured outlines, and then incrementally generates stage-aligned counseling dialogues. To capture decision-making rationale behind each response, we propose the Memory-Driven Dynamic Planning thinking pattern that integrates memory enhancement, global planning, and strategy reasoning; a collaborative multi-agent optimizer then leverages MDP to attach explicit chain-of-thought to each dialogue turn. Extensive experiments and human evaluations demonstrate that CATCH significantly enhances fidelity and logical coherence in AI counseling.
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。
