PsyEvo: A Personalized Counseling Agent That Self-Evolves at Test Time
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
arXiv:2509.04183v3 Announce Type: replace-cross Abstract: The growing demand for scalable psychological counseling highlights the need for high-quality, privacy-compliant data, yet such data remains...
Graph2Counsel is a framework that generates synthetic counseling dialogues by leveraging Client Psychological Graphs (CPGs) to encode the relationships among a client’s thoughts, emotions, and behaviors. The system uses a structured prompting pipeline guided by counselor strategies and explores techniques such as Chain‑of‑Thought and Multi‑Agent Feedback to produce 760 realistic sessions from 76 CPGs. Expert evaluation shows the dataset surpasses previous ones in specificity, counselor competence, authenticity, conversational flow, and safety, and fine‑tuning an open‑source model on it improves performance on several counseling benchmarks.
MACBT is a clinician‑facing AI decision‑support system that models the five‑stage CBT workflow with five collaborative agents and incorporates a CBT‑specific longitudinal memory module (CD Memory) to track cognitive distortions across sessions. The system generates pre‑session pathology reports and prioritises interventions, and was trained on a Chinese CBT dialogue corpus using a Qwen3‑14B backbone with supervised fine‑tuning and preference optimisation. Evaluation by GPT‑4 judges shows MACBT outperforms several existing chatbots in professionalism and clinical authenticity, and the memory‑augmented version improves session quality by 12.6% and achieves a longitudinal mean of 2.29 on continuity, progression, and personalization.
The paper introduces StratCBT, a new dataset of 9,688 psychological counseling sessions with 256K utterances, each counselor response aligned to one of eight Cognitive Behavioral Therapy (CBT) strategies. It was created by modeling clients’ negative thoughts and generating high‑quality conversations through self‑chat, using realistic sessions for guidance. Experiments show that strategy‑aligned generation improves professional and effective counseling when evaluated with large language model‑simulated clients.
arXiv:2606. 30887v1 Announce Type: cross Abstract: Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive metric.
arXiv:2510.25384v2 Announce Type: replace Abstract: Large Language Models (LLMs) are promising tools for synthetic data generation in mental health. However, privacy policies and restrictions forced...