PsychAgent: An Experience-Driven Lifelong Learning Agent for Self-Evolving Psychological Counselor

TL;DR

PsychAgent is a lifelong learning AI for psychological counseling that continuously improves through experience, memory, and skill evolution, outperforming existing models in multi-session therapy scenarios.

Contribution

It introduces a memory-augmented planning engine, a skill evolution engine, and a reinforced internalization engine for self-evolving counseling AI.

Findings

Outperforms GPT-5.4 and Gemini-3 in evaluation metrics.

Achieves higher scores than domain-specific baselines.

Enhances consistency and quality in multi-session responses.

Abstract

Existing methods for AI psychological counselors predominantly rely on supervised fine-tuning using static dialogue datasets. However, this contrasts with human experts, who continuously refine their proficiency through clinical practice and accumulated experience. To bridge this gap, we propose an Experience-Driven Lifelong Learning Agent (\texttt{PsychAgent}) for psychological counseling. First, we establish a Memory-Augmented Planning Engine tailored for longitudinal multi-session interactions, which ensures therapeutic continuity through persistent memory and strategic planning. Second, to support self-evolution, we design a Skill Evolution Engine that extracts new practice-grounded skills from historical counseling trajectories. Finally, we introduce a Reinforced Internalization Engine that integrates the evolved skills into the model via rejection fine-tuning, aiming to improve performance across diverse scenarios. Comparative analysis shows that our approach achieves higher scores than strong general LLMs (e.g., GPT-5.4, Gemini-3) and domain-specific baselines across all reported evaluation dimensions. These results suggest that lifelong learning can improve the consistency and overall quality of multi-session counseling responses.