AgentMental: An Interactive Multi-Agent Framework for Explainable and Adaptive Mental Health Assessment
Jinpeng Hu, Ao Wang, Qianqian Xie, Zhuo Li, Hui Ma, Dan Guo
Abstract
Mental health assessment is crucial for early intervention and effective treatment, yet traditional clinician-based approaches are limited by the shortage of qualified professionals. Recent advances in artificial intelligence have sparked growing interest in automated psychological assessment, yet most existing approaches are constrained by their reliance on static text analysis, limiting their ability to capture deeper and more informative insights that emerge through dynamic interaction and iterative questioning. Therefore, in this paper, we propose a multi-agent framework for mental health evaluation that simulates clinical doctor-patient dialogues, with specialized agents assigned to questioning, adequacy evaluation, scoring, and updating. In detail, we introduce an adaptive questioning mechanism in which an evaluation agent assesses the adequacy of user responses to determine the necessity of generating targeted follow-up queries to address ambiguity and missing information. Additionally, we employ a tree-structured memory in which the root node encodes the user
BibTeX
@inproceedings{aaai2026_agentmentalanint,
title = {AgentMental: An Interactive Multi-Agent Framework for Explainable and Adaptive Mental Health Assessment},
author = {Jinpeng Hu and Ao Wang and Qianqian Xie and Zhuo Li and Hui Ma and Dan Guo},
booktitle = {AAAI 2026},
year = {2026}
}