MoPHES:Leveraging on-device LLMs as Agent for Mobile Psychological Health Evaluation and Support

• URL: http://arxiv.org/abs/2510.16085v1
• Date: Fri, 17 Oct 2025 15:22:42 GMT
• Title: MoPHES:Leveraging on-device LLMs as Agent for Mobile Psychological Health Evaluation and Support
• Authors: Xun Wei, Pukai Zhou, Zeyu Wang,
• Abstract summary: This paper proposes MoPHES, a framework that integrates mental state evaluation, conversational support, and professional treatment recommendations.<n>One agent is fine-tuned on mental health conditions datasets to assess users' mental states and predict the severity of anxiety and depression; the other is fine-tuned on multi-turn dialogues to handle conversations with users.<n>Both models are also deployed directly on mobile devices to enhance user convenience and protect user privacy.
• Score: 4.633878208731596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The 2022 World Mental Health Report calls for global mental health care reform, amid rising prevalence of issues like anxiety and depression that affect nearly one billion people worldwide. Traditional in-person therapy fails to meet this demand, and the situation is worsened by stigma. While general-purpose large language models (LLMs) offer efficiency for AI-driven mental health solutions, they underperform because they lack specialized fine-tuning. Existing LLM-based mental health chatbots can engage in empathetic conversations, but they overlook real-time user mental state assessment which is critical for professional counseling. This paper proposes MoPHES, a framework that integrates mental state evaluation, conversational support, and professional treatment recommendations. The agent developed under this framework uses two fine-tuned MiniCPM4-0.5B LLMs: one is fine-tuned on mental health conditions datasets to assess users' mental states and predict the severity of anxiety and depression; the other is fine-tuned on multi-turn dialogues to handle conversations with users. By leveraging insights into users' mental states, our agent provides more tailored support and professional treatment recommendations. Both models are also deployed directly on mobile devices to enhance user convenience and protect user privacy. Additionally, to evaluate the performance of MoPHES with other LLMs, we develop a benchmark for the automatic evaluation of mental state prediction and multi-turn counseling dialogues, which includes comprehensive evaluation metrics, datasets, and methods.

Related papers

• Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models [72.36715571932696]
  Narrative therapy helps individuals transform problematic life stories into empowering alternatives.<n>Current approaches lack realism in specialized psychotherapy and fail to capture therapeutic progression over time.<n>Int (Interactive Narrative Therapist) simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate expert-like responses.
  arXiv  Detail & Related papers  (2025-07-27T11:52:09Z)
• Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations [13.064927179032756]
  We introduce MedAgent, a novel framework for synthetically generating realistic, multi-turn mental health sensemaking conversations.<n>We present MultiSenseEval, a holistic framework to evaluate the multi-turn conversation abilities of LLMs in healthcare settings.
  arXiv  Detail & Related papers  (2025-05-26T16:42:02Z)
• Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling [50.83055329849865]
  PsyLLM is a large language model designed to integrate diagnostic and therapeutic reasoning for mental health counseling.<n>It processes real-world mental health posts from Reddit and generates multi-turn dialogue structures.<n>Our experiments demonstrate that PsyLLM significantly outperforms state-of-the-art baseline models.
  arXiv  Detail & Related papers  (2025-05-21T16:24:49Z)
• Ψ-Arena: Interactive Assessment and Optimization of LLM-based Psychological Counselors with Tripartite Feedback [51.26493826461026]
  We propose Psi-Arena, an interactive framework for comprehensive assessment and optimization of large language models (LLMs)<n>Arena features realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients.<n>Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives.
  arXiv  Detail & Related papers  (2025-05-06T08:22:51Z)
• MAGI: Multi-Agent Guided Interview for Psychiatric Assessment [50.6150986786028]
  We present MAGI, the first framework that transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational navigation.<n>We show that MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning.
  arXiv  Detail & Related papers  (2025-04-25T11:08:27Z)
• "It Listens Better Than My Therapist": Exploring Social Media Discourse on LLMs as Mental Health Tool [1.223779595809275]
  Large language models (LLMs) offer new capabilities in conversational fluency, empathy simulation, and availability.<n>This study explores how users engage with LLMs as mental health tools by analyzing over 10,000 TikTok comments.<n>Results show that nearly 20% of comments reflect personal use, with these users expressing overwhelmingly positive attitudes.
  arXiv  Detail & Related papers  (2025-04-14T17:37:32Z)
• AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling [57.054489290192535]
  Traditional in-person psychological counseling remains primarily niche, often chosen by individuals with psychological issues.<n>Online automated counseling offers a potential solution for those hesitant to seek help due to feelings of shame.
  arXiv  Detail & Related papers  (2025-01-16T09:57:12Z)
• MentalAgora: A Gateway to Advanced Personalized Care in Mental Health through Multi-Agent Debating and Attribute Control [40.21489535255022]
  MentalAgora is a novel framework employing large language models enhanced by interaction between multiple agents for tailored mental health support. This framework operates through three stages: strategic debating, tailored counselor creation, and response generation. Our evaluations, including experiments and user studies, demonstrate that MentalAgora aligns with professional standards and effectively meets user preferences.
  arXiv  Detail & Related papers  (2024-07-03T01:19:38Z)
• LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
  We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
  arXiv  Detail & Related papers  (2024-06-09T09:03:11Z)
• Can AI Relate: Testing Large Language Model Response for Mental Health Support [23.97212082563385]
  Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS. We develop an evaluation framework for determining whether LLM response is a viable and ethical path forward for the automation of mental health treatment.
  arXiv  Detail & Related papers  (2024-05-20T13:42:27Z)
• Psy-LLM: Scaling up Global Mental Health Psychological Services with AI-based Large Language Models [3.650517404744655]
  Psy-LLM framework is an AI-based tool leveraging Large Language Models for question-answering in psychological consultation settings. Our framework combines pre-trained LLMs with real-world professional Q&A from psychologists and extensively crawled psychological articles. It serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress.
  arXiv  Detail & Related papers  (2023-07-22T06:21:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.