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    <abstract>LLM-based simulated clients are increasingly used to train novice counselors, evaluate LLM therapists, and generate synthetic data. However, current simulators produce overly cooperative clients that disclose too readily, accept therapeutic reframes without resistance, and resolve core issues within a single session. We trace these issues to profiles that lack causal depth and behavioral mechanisms that treat all content as equally accessible. We present PatientAct, a framework for client simulation grounded in established clinical theories. Our profiles integrate the 5Ps clinical case formulation, providing causal depth without tying the design to any single therapeutic modality. During simulation, profiles include a dynamic memory layer in which items carry trust thresholds (e.g., symptoms are available early, whereas formative memories require a sustained therapeutic alliance). At each turn, we model the client’s emotional reaction and behavior before generating a response. If the therapist approaches gated content, PatientAct expresses resistance in terms of quantity, content, and style rather than defaulting to cooperation or a single resistance pattern. We evaluate our framework on 40 clinical situations and demonstrate that it generates diverse profiles with high clinical plausibility. Moreover, PatientAct significantly outperforms the baselines, yielding substantial gains in resistance quality and behavioral realism. Our code and data are publicly available via github.com/Sahandfer/PatientHub.</abstract>
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%0 Conference Proceedings
%T PatientAct: Theory-Grounded Mental Health Client Simulation
%A Sabour, Sahand
%A Yam, NG Tsz
%A Chen, Yaqian
%A Bi, Guanqun
%A Huang, Minlie
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F sabour-etal-2026-patientact
%X LLM-based simulated clients are increasingly used to train novice counselors, evaluate LLM therapists, and generate synthetic data. However, current simulators produce overly cooperative clients that disclose too readily, accept therapeutic reframes without resistance, and resolve core issues within a single session. We trace these issues to profiles that lack causal depth and behavioral mechanisms that treat all content as equally accessible. We present PatientAct, a framework for client simulation grounded in established clinical theories. Our profiles integrate the 5Ps clinical case formulation, providing causal depth without tying the design to any single therapeutic modality. During simulation, profiles include a dynamic memory layer in which items carry trust thresholds (e.g., symptoms are available early, whereas formative memories require a sustained therapeutic alliance). At each turn, we model the client’s emotional reaction and behavior before generating a response. If the therapist approaches gated content, PatientAct expresses resistance in terms of quantity, content, and style rather than defaulting to cooperation or a single resistance pattern. We evaluate our framework on 40 clinical situations and demonstrate that it generates diverse profiles with high clinical plausibility. Moreover, PatientAct significantly outperforms the baselines, yielding substantial gains in resistance quality and behavioral realism. Our code and data are publicly available via github.com/Sahandfer/PatientHub.
%U https://aclanthology.org/2026.findings-emnlp.1572/
%P 33483-33506

Markdown (Informal)

[PatientAct: Theory-Grounded Mental Health Client Simulation](https://aclanthology.org/2026.findings-emnlp.1572/) (Sabour et al., Findings 2026)

• PatientAct: Theory-Grounded Mental Health Client Simulation (Sabour et al., Findings 2026)

ACL

• Sahand Sabour, NG Tsz Yam, Yaqian Chen, Guanqun Bi, and Minlie Huang. 2026. PatientAct: Theory-Grounded Mental Health Client Simulation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 33483–33506, Budapest, Hungary. Association for Computational Linguistics.