Recent advances in large language models have enabled mental health dialogue systems, yet existing approaches remain predominantly reactive, lacking systematic user state modeling for proactive therapeutic exploration. We introduce PsyProbe, a dialogue system designed for the exploration phase of counseling that systematically tracks user psychological states through the PPPPPI framework (Presenting, Predisposing, Precipitating, Perpetuating, Protective, Impact) augmented with cognitive error detection. PsyProbe combines State Builder for extracting structured psychological profiles, Memory Construction for tracking information gaps, Strategy Planner for Motivational Interviewing behavioral codes, and Response Generator with Question Ideation and Critic/Revision modules to generate contextually appropriate, proactive questions. We evaluate PsyProbe with 27 participants in real-world Korean counseling scenarios, including automatic evaluation across ablation modes, user evaluation, and expert evaluation by a certified counselor. The full PsyProbe model consistently outperforms baseline and ablation modes in automatic evaluation. User evaluation demonstrates significantly increased engagement intention and improved naturalness compared to baseline. Expert evaluation shows that PsyProbe substantially improves core issue understanding and achieves question rates comparable to professional counselors, validating the effectiveness of systematic state modeling and proactive questioning for therapeutic exploration.

翻译：近年来，大型语言模型的进展推动了心理健康对话系统的发展，然而现有方法仍主要处于被动反应模式，缺乏用于主动治疗探索的系统化用户状态建模。我们提出了PsyProbe，一个专为咨询探索阶段设计的对话系统，它通过增强认知错误检测的PPPPPI框架（呈现、易感、诱发、维持、保护、影响）系统地追踪用户心理状态。PsyProbe整合了用于提取结构化心理画像的状态构建器、用于追踪信息缺口的记忆构建模块、基于动机性访谈行为编码的策略规划器，以及包含问题构思与批判/修订模块的响应生成器，以生成情境适宜、主动探索的问题。我们在真实的韩语心理咨询场景中，对27名参与者进行了PsyProbe评估，包括消融模式的自动评估、用户评估以及由持证咨询师进行的专家评估。完整PsyProbe模型在自动评估中持续优于基线及消融模式。用户评估表明，与基线相比，用户的参与意愿显著提升，对话自然度也得到改善。专家评估显示，PsyProbe显著提升了对核心问题的理解，且提问率与专业咨询师相当，验证了系统化状态建模与主动提问对于治疗探索的有效性。