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

Mental health disorders affect a substantial proportion of the global population, yet a persistent shortage of trained practitioners leaves the majority without adequate care. Large language model (LLM)-based counselors present a promising direction for delivering scalable conversational psychological support. Offline model training alone leaves limited room to adapt to individual clients or to learn from ongoing therapeutic interaction at test time. We introduce PsyEvo, an LLM-based counseling framework that enables both client-specific personalization and response-policy improvement at test time through three components: Hierarchical Bayesian Skill Policy (HBSP) personalizes what intervention to apply by maintaining a per-client skill posterior updated from session feedback; Inter-session Listwise Preference Optimization (LiPO) improves how the selected skill is expressed by updating a shared response adapter from cross-client preference evidence; and State-conditioned Ordinal Credit Assignment (SOCA) supplies candidate preferences and trajectory credit to the two components through consistency-checked comparisons and ordinal projection. In simulated-client evaluation with shared online cohort adaptation, PsyEvo obtains 7.684 Overall on PsychEval and exceeds every component variant in each of three matched runs. Removing individual components lowers mean overall score by 0.138--0.171 under the shared configuration, supporting conditional contributions within the complete scaffold. Our code is available at https://github.com/Lingxi-mental-health/PsyEvo

The growing demand for scalable psychological counseling highlights the need for high-quality, privacy-compliant data, yet such data remains scarce. Here we introduce MAGneT, a novel multi-agent framework for synthetic psychological counseling session generation that decomposes counselor response generation into coordinated sub-tasks handled by specialized LLM agents, each modeling a key psychological technique. Unlike prior single-agent approaches, MAGneT better captures the structure and nuance of real counseling. We further propose a unified evaluation framework that consolidates diverse automatic metrics and expands expert assessment from four to nine counseling aspects, thus addressing inconsistencies in prior evaluation protocols. Empirically, MAGneT substantially outperforms existing methods: experts prefer MAGneT-generated sessions in 77.2% of cases on average across the nine aspects over the strongest baseline, and sessions generated by MAGneT using Llama3-8B-Instruct backbone yield 3.2% higher general counseling skills and 4.3% higher CBT-specific skills on cognitive therapy rating scale (CTRS). An open source Llama3-8B-Instruct model fine-tuned on MAGneT-generated data also outperforms models fine-tuned using baseline synthetic datasets by 6.9% on average on CTRS. We make our code, data and fine-tuned model public.

Psychological counseling unfolds over many sessions, and what a client brings to each session depends on what has happened in their life since the last one. Existing synthetic counseling corpora are mostly single-session, and multi-session ones advance along a therapy plan without modeling the client's life between sessions. We present Psy-Chronicle, an event-driven framework that grounds multi-session counseling synthesis in a student's semester timeline: each simulated student receives a profile and a semester-long temporal stress event graph, and counseling sessions are generated along this timeline with memory carried across them. Applied to campus counseling, it yields CPCD, an open Chinese dataset of 100 students and 1{,}248 sessions spanning a full semester. The dialogues follow their timelines: dependencies between events carry across sessions, and references to earlier sessions rarely introduce content absent from the trajectory. Under a published evaluation protocol, in basic quality and exceeds all single-session corpora in working alliance. A model fine-tuned on CPCD connects each week's events to earlier ones more often than a model trained on the same data without the dependencies between events, while matching it when the client's situation does not change.