@inproceedings{liu-etal-2026-ccd,
title = "{CCD}-{CBT}: Multi-Agent Therapeutic Interaction for {CBT} Guided by Cognitive Conceptualization Diagram",
author = "Liu, Chang and
Ma, Changsheng and
Tao, Yongfeng and
Hu, Bin and
Yang, Minqiang",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.79/",
pages = "1723--1745",
ISBN = "979-8-89176-439-2",
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 fidelity and positive-affect enhancement, with ablations validating dynamic CCD guidance and diagnostic analysis supporting asymmetric interaction design. Our work offers a new paradigm for building theory-grounded, clinically-plausible conversational agents."
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<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 fidelity and positive-affect enhancement, with ablations validating dynamic CCD guidance and diagnostic analysis supporting asymmetric interaction design. Our work offers a new paradigm for building theory-grounded, clinically-plausible conversational agents.</abstract>
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%0 Conference Proceedings
%T CCD-CBT: Multi-Agent Therapeutic Interaction for CBT Guided by Cognitive Conceptualization Diagram
%A Liu, Chang
%A Ma, Changsheng
%A Tao, Yongfeng
%A Hu, Bin
%A Yang, Minqiang
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F liu-etal-2026-ccd
%X 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 fidelity and positive-affect enhancement, with ablations validating dynamic CCD guidance and diagnostic analysis supporting asymmetric interaction design. Our work offers a new paradigm for building theory-grounded, clinically-plausible conversational agents.
%U https://aclanthology.org/2026.aacl-long.79/
%P 1723-1745
Markdown (Informal)
[CCD-CBT: Multi-Agent Therapeutic Interaction for CBT Guided by Cognitive Conceptualization Diagram](https://aclanthology.org/2026.aacl-long.79/) (Liu et al., AACL-IJCNLP 2026)
ACL