@inproceedings{chalakova-etal-2026-soften,
title = "Soften Resistance: Adaptive Therapeutic Policy Management for Disengaged Clients in Mental Health Support",
author = "Chalakova, Hristina and
Mo, Yifan and
Sun, Xin and
Pei, Jiahuan",
editor = "Wang, Zimu and
Jiang, Yiwen and
Shen, Yaling and
Zhao, Xiangyu and
Fong, Stephanie and
Sun, Xin and
Zhou, Yi and
Ji, Shaoxiong and
Desimone, Robert and
Nelson, Barnaby and
Dwyer, Dominic and
Ge, Zongyuan",
booktitle = "Proceedings of the 1st Workshop on Multimodal, Multilingual, and Multicultural Mental Health and Psychotherapy ({M}ulti{P}syche)",
month = nov,
year = "2026",
address = "Hengqin, Zhuhai, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.multipsyche-1.1/",
pages = "1--14",
ISBN = "979-8-89176-448-4",
abstract = "LLM-based AI therapists largely assume cooperative clients, despite real-world therapeutic interactions often involving resistant behaviors: withdrawn, deflecting, denying, or hostile. We propose a resistance-aware AI therapist combining (i) a multi-channel resistance detector that classifies each client turn into one of five types (R0{--}R4), (ii) a stabilized policy selector that maps a detected resistance to an evidence-based therapeutic strategy, and (iii) a strategy-grounded response generator. Across 800 simulated sessions over ten DSM-5 disorder profiles, the detector reaches 85{\%} accuracy and 85.5{\%} macro F1. Within the simulated evaluation, the adaptive policy achieves resistance-reduction rates of 70{--}100{\%} across resistance types, whereas each static policy yields zero reduction for at least one type. The adaptive policy also maintains resistance-reduction performance across ten simulated DSM-5 disorder profiles. Our dataset and implementation are fully open-sourced."
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%0 Conference Proceedings
%T Soften Resistance: Adaptive Therapeutic Policy Management for Disengaged Clients in Mental Health Support
%A Chalakova, Hristina
%A Mo, Yifan
%A Sun, Xin
%A Pei, Jiahuan
%Y Wang, Zimu
%Y Jiang, Yiwen
%Y Shen, Yaling
%Y Zhao, Xiangyu
%Y Fong, Stephanie
%Y Sun, Xin
%Y Zhou, Yi
%Y Ji, Shaoxiong
%Y Desimone, Robert
%Y Nelson, Barnaby
%Y Dwyer, Dominic
%Y Ge, Zongyuan
%S Proceedings of the 1st Workshop on Multimodal, Multilingual, and Multicultural Mental Health and Psychotherapy (MultiPsyche)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, Zhuhai, China
%@ 979-8-89176-448-4
%F chalakova-etal-2026-soften
%X LLM-based AI therapists largely assume cooperative clients, despite real-world therapeutic interactions often involving resistant behaviors: withdrawn, deflecting, denying, or hostile. We propose a resistance-aware AI therapist combining (i) a multi-channel resistance detector that classifies each client turn into one of five types (R0–R4), (ii) a stabilized policy selector that maps a detected resistance to an evidence-based therapeutic strategy, and (iii) a strategy-grounded response generator. Across 800 simulated sessions over ten DSM-5 disorder profiles, the detector reaches 85% accuracy and 85.5% macro F1. Within the simulated evaluation, the adaptive policy achieves resistance-reduction rates of 70–100% across resistance types, whereas each static policy yields zero reduction for at least one type. The adaptive policy also maintains resistance-reduction performance across ten simulated DSM-5 disorder profiles. Our dataset and implementation are fully open-sourced.
%U https://aclanthology.org/2026.multipsyche-1.1/
%P 1-14
Markdown (Informal)
[Soften Resistance: Adaptive Therapeutic Policy Management for Disengaged Clients in Mental Health Support](https://aclanthology.org/2026.multipsyche-1.1/) (Chalakova et al., MultiPsyche 2026)
ACL