Psych\=eChat: An Empathic Framework Focused on Emotion Shift Tracking and Safety Risk Analysis in Psychological Counseling

2026-01-18

AI Summary

This work addresses the limitations of current large language models in psychological counseling, which often lack explicit modeling of clients’ emotional dynamics and struggle to balance empathetic responses with safety risk mitigation. To this end, we propose an empathetic framework that integrates emotion trajectory tracking with proactive risk assessment, explicitly modeling emotional transitions for the first time and introducing a dual-module协同 mechanism for emotion regulation and risk control. Methodologically, our approach leverages interactive role-playing data synthesis, a multi-agent collaborative architecture, and a unified chain-of-thought reasoning paradigm to enhance inferential capabilities while maintaining computational efficiency. Experimental results demonstrate that the proposed framework significantly outperforms existing methods in both automatic and human evaluations, exhibiting particular strengths in emotional insight and safety assurance.

Technology Category

• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Humans and AI: Emotional Intelligence

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Large language models (LLMs) have demonstrated notable advancements in psychological counseling. However, existing models generally do not explicitly model seekers'emotion shifts across counseling sessions, a core focus in classical psychological schools. Moreover, how to align counselor models'responses with these emotion shifts while proactively mitigating safety risks remains underexplored. To bridge these gaps, we propose Psych\=eChat, which explicitly integrates emotion shift tracking and safety risk analysis for psychological counseling. Specifically, we employ interactive role-playing to synthesize counselor--seeker dialogues, incorporating two modules: Emotion Management Module, to capture seekers'current emotions and emotion shifts; and Risk Control Module, to anticipate seekers'subsequent reactions and identify potential risks. Furthermore, we introduce two modeling paradigms. The Agent Mode structures emotion management, risk control, and counselor responses into a collaborative multi-agent pipeline. The LLM Mode integrates these stages into a unified chain-of-thought for end-to-end inference, balancing efficiency and performance. Extensive experiments, including interactive scoring, dialogue-level evaluation, and human assessment, demonstrate that Psych\=eChat outperforms existing methods for emotional insight and safety control.

Problem

• emotion shift tracking
• safety risk analysis
• psychological counseling
• large language models
• empathic framework

Innovation

• emotion shift tracking
• safety risk analysis
• multi-agent counseling framework
• role-playing dialogue synthesis
• chain-of-thought reasoning