DMT-CBT: Longitudinal Therapeutic State Modeling for CBT Counseling

• URL: http://arxiv.org/abs/2606.03132v1
• Date: Tue, 02 Jun 2026 04:18:25 GMT
• Title: DMT-CBT: Longitudinal Therapeutic State Modeling for CBT Counseling
• Abstract summary: We propose DMT-CBT, a framework for Dynamic Modeling of evolving Therapeutic states in CBT counseling.<n>Based on this framework, we construct DMTCorpus, a synthetic multi-session multimodal CBT counseling dataset.
• Score: 9.111886840996501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have shown growing potential for Cognitive Behavioral Therapy (CBT) counseling. However, most existing approaches still formulate counseling as a local response generation problem, focusing on empathetic replies within short, text-only, or single-session interactions. We argue that this formulation fundamentally mismatches the nature of real psychotherapy. In clinical CBT, therapy is a longitudinal process in which therapists continuously infer, update, and intervene on evolving therapeutic states across sessions. Realistic CBT further involves multimodal inference and delayed cross-session intervention effects, requiring models to capture longitudinal therapeutic state evolution under partial observability. We propose DMT-CBT, a framework for Dynamic Modeling of evolving Therapeutic states in CBT counseling. DMT-CBT maintains structured therapeutic states across sessions while incorporating multimodal behavioral grounding and tool-augmented intervention to support adaptive therapeutic reasoning. Based on this framework, we construct DMTCorpus, a synthetic multi-session multimodal CBT counseling dataset featuring evolving therapeutic states, image-grounded client behaviors, and cross-session intervention continuity. Experimental results show that DMT-CBT improves counseling fidelity and therapeutic alliance, produces more favorable longitudinal affective trajectories, and preserves therapeutic states more faithfully than post-hoc extraction approaches.

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