2026· Annual Meeting of the Association for Computational Linguistics· pp. 36757-36791· 0 citations· 49 references
Computer Science
TL;DR
The proposed Psy chain-of-agents framework is a chain-of-agents framework that evolves static counseling corpora into high-fidelity dialogues through collaborative simulation which explicitly models client personality, stage progression, safety monitoring, and expert supervision.
Abstract
Existing psychological counseling datasets often suffer from monolithic client personas, insufficient therapeutic depth, and a lack of process controllability. To address these critical limitations, we propose PsyChain , a chain-of-agents framework that evolves static counseling corpora into high-fidelity dialogues through collaborative simulation which explicitly models client personality, stage progression, safety monitoring, and expert supervision. PsyChain involves a Client Profiler that extracts life scenarios and pairs them with psychological personality archetypes to synthesize diverse profiles. To simulate the complete counseling process, five specialized agents—Process Monitor, Client Speaker, Safety Monitor, Counselor Su-pervisor, and Counselor Speaker—collaborate and interact autonomously at each dialogue turn to ensure therapeutic professionalism and safety. We apply this to construct PsyChainD , a Chinese dataset of 10,456 dialogues featuring systematically diverse client profiles. Extensive evaluation across client side , counselor side and overall quality shows substantial improvements. The model trained on PsyChainD achieves 61-91% win rates against domain-specific baselines in pairwise evaluation and the highest average score in human evaluation, indicating potential for real-world counseling.
EmoTrace, a multi-turn dialogue corpus generation framework centered on modeling seekers' emotional trajectories, is proposed, which outperforms existing approaches in terms of emotional richness and empathy quality.
Kai-Tong Weng, Lixiong Liu, Zihao Liu et al.· arXiv.org· 0 citations
DeepSAGE (Strategic AI Guidance Engine), a hybrid LLM--Deep Reinforcement Learning (DRL) framework for stage-aware counseling dialogue grounded in the first session of Cognitive Behavioral Therapy (CBT), suggests that combining stage-structured dialogue with learned strategy selection is a promising approach for AI cou...
Qi Zhang, Heajun An, P. Dumaru et al.· 0 citations
DeepSupport, a multi-persona PS system trained with OrthoTune, a PS-tailored framework with style-specific adapters and a style-consistency regularizer is built, unifying the five DeepSupport personas into Ekova, a persistent personality-support agent with a unified cross-session memory layer, supporting both adaptive...
A dual-loop self-evolution framework driven by verifiable emotion feedback is introduced, which raises Qwen3-8B Overall from 53.87 to 79.24 and outperforms protocol-matched uniform emotion-reward reinforcement learning by 7.23 points.
Yingfei Wei, Shuo Jiang, Huai-Xia Dou et al.· 0 citations
PALATE (Person-Aligned LLM-Simulated-User Assessment with Tailored Evaluation with Tailored Evaluation), a scalable RPA benchmark built on user simulators, produces interpretable evaluations of specific user-RPA pairs rather than compressing systems into a single user-independent ranking.
Yuhan Zhu, Ming-Xuan Du, Benfeng Xu et al.· arXiv.org· 0 citations
AffAdapt is presented, a seamless interaction design framework for AI-personas, which coordinates streaming speech recognition, proactive turn-management, persona-grounded response generation, a persistent emotional state, and synchronized embodied output into a single interaction loop.
Nishanth Chidambaram, K. Paliwal, Kayla Hom et al.· 1 citation· ⚡1
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