提出新框架与评估方法，让AI更真实应对心理辅导中的抗拒行为。
When Clients Stop Following: A Cognitive Conceptualization Diagram-driven Framework for Strategic Counseling
- 用认知概念图建模用户动态抗拒，模拟真实咨询阻力。
- 在高对抗场景下，策略性推理模块使响应准确率提升23.7%。
- 适合研究智能心理咨询的可信评估与抗干扰能力提升。
大型语言模型在心理辅导中展现潜力，但现有基准严重依赖高度配合的模拟用户。我们观察到关键现象：这些用户在仅几轮对话后便从抗拒迅速转为顺从，造成治疗进展的假象，并通过表面共情虚增评分。为解决评估偏差，我们提出基于认知行为疗法（CBT）的抗抗拒框架。引入CARS客户端模拟器，通过认知概念图（CCDs）显式建模动态抗拒。提出STREAMS双模块框架，将策略推理（Thinker）与回复生成（Presenter）解耦，并通过强化学习优化。进一步设计EWTS-MI熵权重指标，用于评估高摩擦交互下的响应能力。在有抗拒与无抗拒场景下的实验验证了评估偏差的存在，并证明抗抗拒训练能显著提升复杂互动中的策略鲁棒性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) show promise in psychological counseling, yet existing benchmarks rely heavily on highly cooperative simulated clients. We observe a critical counselor-following phenomenon: these clients often rapidly shift from resistance to compliance after only a few turns, creating an illusion of therapeutic progress and inflating scores under current evaluation protocols through superficial empathy. To address this evaluation mismatch, we propose a Cognitive Behavioral Therapy (CBT)-grounded resistance-aware framework. We introduce CARS, a client simulator that explicitly models dynamic resistance via Cognitive Conceptualization Diagrams (CCDs). We present STREAMS, a dual-module framework that decouples strategic reasoning (Thinker) from response generation (Presenter) and optimizes it via reinforcement learning. We further propose EWTS-MI, an entropy-weighted metric for evaluating responsiveness under high-friction interactions. Experiments across resistant and non-resistant counseling settings validate our findings on evaluation mismatch and demonstrate the effectiveness of resistance-aware training for improving strategic robustness under challenging counseling interactions.
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