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This paper presents MACBT, a multi-agent cognitive behavioral therapy decision support system with longitudinal memory, trained on Chinese dialogues and shown to improve clinical authenticity and session quality compared to other AI systems.
This paper investigates why LLMs fail to apply CBT principles effectively despite high theoretical accuracy, introducing a knowledge-guided framework and a behavioral metric (Protocol Leverage Force) to measure intervention shifts. Experiments show that even with multi-chain-of-thought prompting, models remain biased toward validation and reflection.
Researchers from Lanzhou University propose a CBT-grounded framework for LLM-based psychological counseling that addresses the 'counselor-following' phenomenon in existing benchmarks, introducing CARS (a resistant client simulator), STREAMS (a dual-module strategic reasoning framework), and EWTS-MI (an entropy-weighted evaluation metric) to better handle real-world resistant counseling interactions.
DMT-CBT proposes a framework for longitudinal therapeutic state modeling in CBT counseling, addressing the need for multi-session, multimodal inference and intervention. It introduces DMTCorpus, a synthetic multi-session dataset, and shows improvements in counseling fidelity and therapeutic alliance over existing methods.