@inproceedings{li-etal-2026-cflowpsyd,
title = "{CF}low{P}sy{D}: An Analysis-Enhanced Dataset for Asynchronous Psychological Counseling through Self-Optimizing Multi-Agent Framework",
author = "Li, Donghao and
Deng, Yifan and
Weng, Jinta and
Zhang, Xingsheng and
Niu, Chenxu and
Tian, Jingyuan and
Huang, Heyan and
Hu, Yue",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.328/",
pages = "6578--6602",
ISBN = "979-8-89176-395-1",
abstract = "Asynchronous psychological counseling (APC) represents a crucial mental health service modality that transcends temporal and spatial constraints. However, its development faces significant data scarcity challenges: due to stringent privacy protection requirements and professional ethical considerations, direct collection of conversational data from authentic APC scenarios is virtually impossible. To address this challenge, we design a self-optimizing multi-agent framework for counseling dialogue generation, \textbf{CFlowPsy}, which utilizes a small amount of real anonymized counseling cases as seed data to synthesize diverse problem-solving-oriented APC conversations through large language models. Specifically, the framework employs a Persona-Flow module to continuously track and update clients' basic information, real-time emotions, and counseling progress, providing dynamic personalized analytical support for counselors and enabling self-optimization of generated dialogues. Simultaneously, the framework ensures that generated interventions contain explicit reasoning processes, demonstrating clear psychological analysis and logic, thereby enhancing the accuracy and consistency of responses. Under this framework, we develop the first Chinese APC dataset, \textbf{CFlowPsyD}, comprising 1,700 high-quality extended conversations. Extensive experiments and human evaluations confirm that the proposed CFlowPsyD dataset successfully simulates human-like APC processes."
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<abstract>Asynchronous psychological counseling (APC) represents a crucial mental health service modality that transcends temporal and spatial constraints. However, its development faces significant data scarcity challenges: due to stringent privacy protection requirements and professional ethical considerations, direct collection of conversational data from authentic APC scenarios is virtually impossible. To address this challenge, we design a self-optimizing multi-agent framework for counseling dialogue generation, CFlowPsy, which utilizes a small amount of real anonymized counseling cases as seed data to synthesize diverse problem-solving-oriented APC conversations through large language models. Specifically, the framework employs a Persona-Flow module to continuously track and update clients’ basic information, real-time emotions, and counseling progress, providing dynamic personalized analytical support for counselors and enabling self-optimization of generated dialogues. Simultaneously, the framework ensures that generated interventions contain explicit reasoning processes, demonstrating clear psychological analysis and logic, thereby enhancing the accuracy and consistency of responses. Under this framework, we develop the first Chinese APC dataset, CFlowPsyD, comprising 1,700 high-quality extended conversations. Extensive experiments and human evaluations confirm that the proposed CFlowPsyD dataset successfully simulates human-like APC processes.</abstract>
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%0 Conference Proceedings
%T CFlowPsyD: An Analysis-Enhanced Dataset for Asynchronous Psychological Counseling through Self-Optimizing Multi-Agent Framework
%A Li, Donghao
%A Deng, Yifan
%A Weng, Jinta
%A Zhang, Xingsheng
%A Niu, Chenxu
%A Tian, Jingyuan
%A Huang, Heyan
%A Hu, Yue
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-cflowpsyd
%X Asynchronous psychological counseling (APC) represents a crucial mental health service modality that transcends temporal and spatial constraints. However, its development faces significant data scarcity challenges: due to stringent privacy protection requirements and professional ethical considerations, direct collection of conversational data from authentic APC scenarios is virtually impossible. To address this challenge, we design a self-optimizing multi-agent framework for counseling dialogue generation, CFlowPsy, which utilizes a small amount of real anonymized counseling cases as seed data to synthesize diverse problem-solving-oriented APC conversations through large language models. Specifically, the framework employs a Persona-Flow module to continuously track and update clients’ basic information, real-time emotions, and counseling progress, providing dynamic personalized analytical support for counselors and enabling self-optimization of generated dialogues. Simultaneously, the framework ensures that generated interventions contain explicit reasoning processes, demonstrating clear psychological analysis and logic, thereby enhancing the accuracy and consistency of responses. Under this framework, we develop the first Chinese APC dataset, CFlowPsyD, comprising 1,700 high-quality extended conversations. Extensive experiments and human evaluations confirm that the proposed CFlowPsyD dataset successfully simulates human-like APC processes.
%U https://aclanthology.org/2026.findings-acl.328/
%P 6578-6602
Markdown (Informal)
[CFlowPsyD: An Analysis-Enhanced Dataset for Asynchronous Psychological Counseling through Self-Optimizing Multi-Agent Framework](https://aclanthology.org/2026.findings-acl.328/) (Li et al., Findings 2026)
ACL
- Donghao Li, Yifan Deng, Jinta Weng, Xingsheng Zhang, Chenxu Niu, Jingyuan Tian, Heyan Huang, and Yue Hu. 2026. CFlowPsyD: An Analysis-Enhanced Dataset for Asynchronous Psychological Counseling through Self-Optimizing Multi-Agent Framework. In Findings of the Association for Computational Linguistics: ACL 2026, pages 6578–6602, San Diego, California, United States. Association for Computational Linguistics.