# Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling

arXiv: https://arxiv.org/abs/2505.15715 · HTML: https://mlsanity.com/paper/2505.15715/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 21 May 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning](https://mlsanity.com/paper/2503.05379/) (2025)
- [MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning](https://mlsanity.com/paper/2505.10557/) (2025)
- [ChatCounselor: A Large Language Models for Mental Health Support](https://mlsanity.com/paper/2309.15461/) (2023)
- [EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models](https://mlsanity.com/paper/2502.04424/) (2025)
- [A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions](https://mlsanity.com/paper/2502.11095/) (2025)
- [AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling](https://mlsanity.com/paper/2501.09426/) (2025)
- [Leveraging Chain of Thought towards Empathetic Spoken Dialogue without Corresponding Question-Answering Data](https://mlsanity.com/paper/2501.10937/) (2025)
- [DSADF: Thinking Fast and Slow for Decision Making](https://mlsanity.com/paper/2505.08189/) (2025)
- [Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agents](https://mlsanity.com/paper/2504.02800/) (2025)
- [Emotions in the Loop: A Survey of Affective Computing for Emotional Support](https://mlsanity.com/paper/2505.01542/) (2025)
- [Psy-Copilot: Visual Chain of Thought for Counseling](https://mlsanity.com/paper/2503.03645/) (2025)
- [DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs](https://mlsanity.com/paper/2505.07049/) (2025)

## Built on by (most cited first, 18 of 18)

- [Kardia-R1: Unleashing LLMs to Reason toward Understanding and Empathy for Emotional Support via Rubric-as-Judge Reinforcement Learning](https://mlsanity.com/paper/2512.01282/) (2025)
- [TheraMind: A Strategic and Adaptive Agent for Longitudinal Psychological Counseling](https://mlsanity.com/paper/2510.25758/) (2025)
- [PsychEval: A Multi-Session and Multi-Therapy Benchmark for High-Realism AI Psychological Counselor](https://mlsanity.com/paper/2601.01802/) (2026)
- [CAREBench: Evaluating LLMs' Emotion Understanding by Assessing Cognitive Appraisal Reasoning](https://mlsanity.com/paper/2605.17176/) (2026)
- [Mental-R1: Aligning LLM Reasoning for Mental Health Assessment](https://mlsanity.com/paper/2606.13176/) (2026)
- [CIMR: Contextualized Iterative Multimodal Reasoning for Robust Instruction Following in LVLMs](https://mlsanity.com/paper/2507.22074/) (2025)
- [CAP-LLM: Context-Augmented Personalized Large Language Models for News Headline Generation](https://mlsanity.com/paper/2508.03935/) (2025)
- [XDR-LVLM: An Explainable Vision-Language Large Model for Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2508.15168/) (2025)
- [Unlocking Compositional Control: Self-Supervision for LVLM-Based Image Generation](https://mlsanity.com/paper/2507.04151/) (2025)
- [Systematizing LLM Persona Design: A Four-Quadrant Technical Taxonomy for AI Companion Applications](https://mlsanity.com/paper/2511.02979/) (2025)
- [DELTA: Deliberative Multi-Agent Reasoning with Reinforcement Learning for Multimodal Psychological Counseling](https://mlsanity.com/paper/2602.04112/) (2026)
- [Psy-Chronicle:A Structured Pipeline for Synthesizing Long-Horizon Campus Psychological Counseling Dialogues](https://mlsanity.com/paper/2605.22140/) (2026)
- [MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling](https://mlsanity.com/paper/2606.29265/) (2026)
- [OneEmo: A Unified Multimodal Reasoning Model for Emotion Perception, Understanding, and Interaction](https://mlsanity.com/paper/2608.06013/) (2026)
- [ESCRAG-R1: Retrieval-Augmented Reinforcement Learning for Emotional Support Conversation](https://mlsanity.com/paper/2608.21925/) (2026)
- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)
- [BiGraph-Diffuse: A Bidirectional Diffusion Language Model with Graph-Structured Retrieval For Mental Health Counseling](https://mlsanity.com/paper/2609.29519/) (2026)
- [Usage-Modulated Sentiment Representations in Large Language Models](https://mlsanity.com/paper/2610.05069/) (2026)

## Abstract (arXiv)

Large language models (LLMs) hold significant potential for mental health support, capable of generating empathetic responses and simulating therapeutic conversations. However, existing LLM-based approaches often lack the clinical grounding necessary for real-world psychological counseling, particularly in explicit diagnostic reasoning aligned with standards like the DSM/ICD and incorporating diverse therapeutic modalities beyond basic empathy or single strategies. To address these critical limitations, we propose PsyLLM, the first large language model designed to systematically integrate both diagnostic and therapeutic reasoning for mental health counseling. To develop PsyLLM, we design a novel automated data synthesis pipeline that processes real-world mental health posts collected from Reddit, where users frequently share psychological distress and seek community support. This pipeline processes real-world mental health posts, generates multi-turn dialogue structures, and leverages LLMs guided by international diagnostic standards (e.g., DSM/ICD) and multiple therapeutic frameworks (e.g., CBT, ACT, psychodynamic) to simulate detailed clinical reasoning processes. Rigorous multi-dimensional filtering ensures the generation of high-quality, clinically aligned dialogue data. In addition, we introduce a new benchmark and evaluation protocol, assessing counseling quality across four key dimensions. Our experiments demonstrate that PsyLLM significantly outperforms state-of-the-art baseline models on this benchmark. The model weights and dataset have been publicly released at https://github.com/Emo-gml/PsyLLM.