发表机构
Central China Normal University; Shanghai Jiao Tong University（华中师范大学; 上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出MASC多智能体自校准框架，通过闭环校准确保心理咨询中来访者角色扮演的一致性，并引入CRPC-Bench基准，实验证明其优于现有方法。
AI 中文摘要
大型语言模型越来越多地被用于模拟来访者，以支持咨询师培训和心理咨询研究，但可靠的模拟要求来访者在长时间互动中保持心理一致性。现有的角色扮演方法主要依赖静态档案提示，可能表现出人格漂移、不切实际的合作性，或心理状态、沟通行为和情绪不一致。现有评估也缺乏一个统一的测试平台，同时涵盖稳定的来访者特征和动态的心理变化。我们提出了MASC，一种具有潜在构念对齐的多智能体自校准框架，用于心理咨询中一致的来访者角色扮演。MASC在闭环校准回路中结合了构念引导生成、协作细化、一致性验证和基于记忆的修订，在对话展开时检测并纠正不一致。我们进一步引入了CRPC-Bench，一个涵盖会话级档案信息和五大人格特质，以及回合级心理状态、沟通行为和情绪表达的基准。CRPC-Bench包含38个动机性访谈来访者档案，并附有性格和情绪标注。实验表明，MASC在档案、人格、接受性和回合级一致性方面优于现有方法，其中异构配置取得了最强的整体性能。MASC和CRPC-Bench为开发和评估心理一致的来访者模拟提供了一个统一的基础，以支持AI辅助的咨询研究和培训。
英文摘要
Large language models are increasingly used to simulate clients for counselor training and psychological counseling research, but reliable simulation requires clients to remain psychologically coherent across extended interactions. Existing role-playing methods largely rely on static profile prompts and may exhibit persona drift, unrealistic cooperativeness, or inconsistent psychological states, communicative actions, and emotions. Existing evaluations also lack a unified testbed for both stable client characteristics and evolving psychological dynamics. We propose MASC, a Multi-Agent Self-Calibration framework with latent construct alignment for consistent client role-playing in psychological counseling. MASC combines construct-guided generation, collaborative refinement, consistency verification, and memory-based revision in a closed calibration loop that detects and corrects inconsistencies as dialogue unfolds. We further introduce CRPC-Bench, a benchmark covering session-level profile information and Big-Five personality traits, as well as turn-level psychological state, communicative action, and emotion expression. CRPC-Bench contains 38 motivational interviewing client profiles augmented with personality and emotion annotations. Experiments show that MASC outperforms existing methods across profile, personality, receptivity, and turn-level consistency, with the heterogeneous configuration achieving the strongest overall performance. MASC and CRPC-Bench provide a unified foundation for developing and evaluating psychologically coherent client simulations for AI-assisted counseling research and training.
