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.
By Aishik Mandal, Hiba Arnaout, Clarissa W. Ong, Juliet Bockhorst, Kate Sheehan, Rachael Moldow, Tanmoy Chakraborty, Iryna Gurevych
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.
By Mizanur Rahman, Abeer Badawi, Elahe Rahimi, Laleh Seyyed-Kalantari, Frank Rudzicz, Enamul Hoque, Elham Dolatabadi
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...
By Doan Nam Long Vu, Rui Tan, Lena Moench, Svenja Jule Francke, Daniel Woiwod, Florian Thomas-Odenthal, Sanna Stroth, Tilo Kircher, Christiane Hermann, Udo Dannlowski, Hamidreza Jamalabadi, Simone Balloccu, Shaoxiong Ji
arXiv:2608.22615v1 Announce Type: new
Abstract: Large Language Model (LLM)-based counseling agents can generate fluent and supportive responses, but they often lack the structured, goal-directed prog...
By Qi Zhang, Heajun An, Prakriti Dumaru, Sang Won Lee, Lifu Huang, Pamela J. Wisniewski, Jin-Hee Cho
arXiv:2606. 16307v1 Announce Type: new Abstract: Training tool-augmented LLM agents requires large corpora of multi-turn, tool-grounded conversational data that is expensive to annotate, privacy-constrained in production settings, and largely absent from public datasets.
By Rahul Khedar, Eshita, Sneha Teja Sree Reddy Thondapu, Mayank Malhotra, Arup Das, Jitesh Chandra, Yun-Shiuan Chuang, Chaitanya Kulkarni, Arun Menon, Linsey Pang, Avinash Karn, Mouli V, Prakhar Mehrotra
arXiv:2608.22251v1 Announce Type: cross
Abstract: Rising global demand for mental health support creates significant service delivery challenges, with asynchronous email counselling serving as a cruc...
By Philipp Steigerwald, Nico Bienlein, Jennifer Burghardt, Mara Stieler, Robert Lehmann, Jens Albrecht
The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.
By Yinzhu Chen, Abdine Maiga, Hossein A. Rahmani, Emine Yilmaz
The paper introduces a fine‑tuned Mistral‑7B‑instruct model as an automated "Supervisor‑in‑the‑Loop" for mental health care, using a tri‑stream analysis of therapeutic alliance, latent risk, and supervisory triage. Leveraging 106 DAIC‑WOZ sessions, the VAL (Visual‑Acoustic‑Linguistic) framework achieves high accuracy in technique identification, alliance assessment, and risk prediction, while cutting supervisory triage latency from 72 hours to about 10 seconds per session. The system also addresses cold‑start challenges with Bayesian priors and synchronizes modalities via timestamps for robust fusion.
By Shreeya Sharma, Ravish Gupta, Saket Kumar, Abhishek Aggarwal
arXiv:2607. 25681v1 Announce Type: new Abstract: Cognitive distortion amplifies negative emotions and contributes to mental health disorders.
By Qi Chen, Siria Xiyueyao Luo, Jian Wang, Yuan Shi, Haocong Rao, Xuejiao Zhao
arXiv:2608. 04524v1 Announce Type: cross Abstract: Synthetic generation of Cognitive Behavioral Therapy (CBT) sessions is challenged by two competing demands: adhering to strict therapeutic structure while modeling the resistant, unpredictable behavior of real patients.
By Javier Rodriguez-Juan, Hiba Arnaout, Jose Garcia-Rodriguez, David Tom\'as, Iryna Gurevych
arXiv:2608.21868v1 Announce Type: new
Abstract: Depression assessment from multimodal clinical interviews requires integrating dispersed evidence from multiple symptoms into a coherent PHQ-8 profile....
By Ao Chen, Xiaojiang Peng
arXiv:2607. 25667v1 Announce Type: cross Abstract: Psychotherapists need repeated training and supervision by experts; however, scalability is problematic.
By Rodolfo Rizzi, Alessandro Grecucci, Massimo Stella