MentraSuite: Post-Training Large Language Models for Mental Health Reasoning and Assessment
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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Mengxi Xiao
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Kailai Yang
claimed_verified
2025-12-16T14:10:45.859Z
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Kailai Yang
klyang
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klyang
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Pengde Zhao
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Enze Zhang
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Ziyan Kuang
claimed_verified
2025-12-16T09:47:51.153Z
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Ziyan Kuang
plumjane
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plumjane
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Zhiwei Liu
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Weiguang Han
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Shu Liao
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Lianting Huang
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Jinpeng Hu
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Min Peng
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Qianqian Xie
claimed_verified
2025-12-16T09:47:53.066Z
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Xie
QianqianXie1994
user
QianqianXie1994
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Sophia Ananiadou
MentraSuite: Post-Training Large Language Models for Mental Health Reasoning and Assessment
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.
A unified framework and benchmark called MentraSuite is introduced to enhance reliable mental-health reasoning in large language models, with a post-trained model named Mindora achieving superior performance through a hybrid SFT-RL approach optimized for consistency and faithfulness.
large language models mental-health reasoning MentraSuite MentraBench Mindora hybrid SFT-RL framework inconsistency-detection reward reasoning trajectory generation structured rewriting process clinical alignment