CCD-CBT: Multi-Agent Therapeutic Interaction for CBT Guided by Cognitive Conceptualization Diagram
Chang Liu, Changsheng Ma, Yongfeng Tao, Bin Hu, Minqiang Yang

TL;DR
This paper introduces CCD-CBT, a multi-agent framework for simulated CBT that dynamically updates a cognitive diagram and models asymmetric therapist-client interactions, improving clinical plausibility.
Contribution
It proposes a novel multi-agent approach with dynamic cognitive modeling and asymmetric interaction, advancing scalable, theory-grounded mental health support.
Findings
Models fine-tuned on CCDCHAT outperform baselines in counseling fidelity.
Dynamic CCD guidance and asymmetric agent design are crucial for performance.
Evaluations show improved positive-affect enhancement and clinical plausibility.
Abstract
Large language models show potential for scalable mental-health support by simulating Cognitive Behavioral Therapy (CBT) counselors. However, existing methods often rely on static cognitive profiles and omniscient single-agent simulation, failing to capture the dynamic, information-asymmetric nature of real therapy. We introduce CCD-CBT, a multi-agent framework that shifts CBT simulation along two axes: 1) from a static to a dynamically reconstructed Cognitive Conceptualization Diagram (CCD), updated by a dedicated Control Agent, and 2) from omniscient to information-asymmetric interaction, where the Therapist Agent must reason from inferred client states. We release CCDCHAT, a synthetic multi-turn CBT dataset generated under this framework. Evaluations with clinical scales and expert therapists show that models fine-tuned on CCDCHAT outperform strong baselines in both counseling…
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