MentraSuite: Post-Training Large Language Models for Mental Health Reasoning and Assessment

• URL: http://arxiv.org/abs/2512.09636v2
• Date: Tue, 16 Dec 2025 10:08:59 GMT
• Title: MentraSuite: Post-Training Large Language Models for Mental Health Reasoning and Assessment
• Authors: Mengxi Xiao, Kailai Yang, Pengde Zhao, Enze Zhang, Ziyan Kuang, Zhiwei Liu, Weiguang Han, Shu Liao, Lianting Huang, Jinpeng Hu, Min Peng, Qianqian Xie, Sophia Ananiadou,
• Abstract summary: MentraSuite is a unified framework for advancing reliable mental-health reasoning.<n>MentraBench is a benchmark spanning five core reasoning aspects, six tasks, and 13 datasets.<n>Mindora is a post-trained model optimized through a hybrid SFT-RL framework.
• Score: 35.949107062098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mental health disorders affect hundreds of millions globally, and the Web now serves as a primary medium for accessing support, information, and assessment. Large language models (LLMs) offer scalable and accessible assistance, yet their deployment in mental-health settings remains risky when their reasoning is incomplete, inconsistent, or ungrounded. Existing psychological LLMs emphasize emotional understanding or knowledge recall but overlook the step-wise, clinically aligned reasoning required for appraisal, diagnosis, intervention planning, abstraction, and verification. To address these issues, we introduce MentraSuite, a unified framework for advancing reliable mental-health reasoning. We propose MentraBench, a comprehensive benchmark spanning five core reasoning aspects, six tasks, and 13 datasets, evaluating both task performance and reasoning quality across five dimensions: conciseness, coherence, hallucination avoidance, task understanding, and internal consistency. We further present Mindora, a post-trained model optimized through a hybrid SFT-RL framework with an inconsistency-detection reward to enforce faithful and coherent reasoning. To support training, we construct high-quality trajectories using a novel reasoning trajectory generation strategy, that strategically filters difficult samples and applies a structured, consistency-oriented rewriting process to produce concise, readable, and well-balanced trajectories. Across 20 evaluated LLMs, Mindora achieves the highest average performance on MentraBench and shows remarkable performances in reasoning reliability, demonstrating its effectiveness for complex mental-health scenarios.

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