@inproceedings{zou-etal-2026-emores,
title = "{E}mo{R}es: Toward Adaptive Psychological Support via User-Agnostic Benchmark and Topic-Mining Agent",
author = "Zou, Zhengwei and
Jiang, Xuanming and
An, Baoyi and
Nie, Dingyu and
Fang, Zhengxing and
Liu, Qingyu and
Qian, Xueming and
Zhao, Guoshuai and
Yang, Zhongyu",
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.416/",
pages = "8575--8586",
ISBN = "979-8-89176-395-1",
abstract = "Large language models exhibit significant potential for psychological support, yet they often generate fragmented and emotionally inconsistent dialogues that lack the therapeutic structure necessary for reliable assessment.To address these issues, we introduce **VeilEval**, a clinically grounded and privacy-preserving benchmark equipped with interpretable metrics for evaluating multi-turn psychological dialogues.Furthermore, we propose Emotion-Resonance (**EmoRes**), a multi-agent framework that boosts psychological reasoning via a Topic-Mining Emotional Agent and a multi-perspective Self-Reflection Agent, thereby jointly improving topic continuity, emotional coherence, and clinical interpretability.Experiments demonstrate that EmoRes achieves up to $\sim 3\times$ improvement over strong baselines on VeilEval, with its effectiveness further validated by ablation studies and human evaluations."
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<abstract>Large language models exhibit significant potential for psychological support, yet they often generate fragmented and emotionally inconsistent dialogues that lack the therapeutic structure necessary for reliable assessment.To address these issues, we introduce **VeilEval**, a clinically grounded and privacy-preserving benchmark equipped with interpretable metrics for evaluating multi-turn psychological dialogues.Furthermore, we propose Emotion-Resonance (**EmoRes**), a multi-agent framework that boosts psychological reasoning via a Topic-Mining Emotional Agent and a multi-perspective Self-Reflection Agent, thereby jointly improving topic continuity, emotional coherence, and clinical interpretability.Experiments demonstrate that EmoRes achieves up to \sim 3\times improvement over strong baselines on VeilEval, with its effectiveness further validated by ablation studies and human evaluations.</abstract>
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%0 Conference Proceedings
%T EmoRes: Toward Adaptive Psychological Support via User-Agnostic Benchmark and Topic-Mining Agent
%A Zou, Zhengwei
%A Jiang, Xuanming
%A An, Baoyi
%A Nie, Dingyu
%A Fang, Zhengxing
%A Liu, Qingyu
%A Qian, Xueming
%A Zhao, Guoshuai
%A Yang, Zhongyu
%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 zou-etal-2026-emores
%X Large language models exhibit significant potential for psychological support, yet they often generate fragmented and emotionally inconsistent dialogues that lack the therapeutic structure necessary for reliable assessment.To address these issues, we introduce **VeilEval**, a clinically grounded and privacy-preserving benchmark equipped with interpretable metrics for evaluating multi-turn psychological dialogues.Furthermore, we propose Emotion-Resonance (**EmoRes**), a multi-agent framework that boosts psychological reasoning via a Topic-Mining Emotional Agent and a multi-perspective Self-Reflection Agent, thereby jointly improving topic continuity, emotional coherence, and clinical interpretability.Experiments demonstrate that EmoRes achieves up to \sim 3\times improvement over strong baselines on VeilEval, with its effectiveness further validated by ablation studies and human evaluations.
%U https://aclanthology.org/2026.findings-acl.416/
%P 8575-8586
Markdown (Informal)
[EmoRes: Toward Adaptive Psychological Support via User-Agnostic Benchmark and Topic-Mining Agent](https://aclanthology.org/2026.findings-acl.416/) (Zou et al., Findings 2026)
ACL
- Zhengwei Zou, Xuanming Jiang, Baoyi An, Dingyu Nie, Zhengxing Fang, Qingyu Liu, Xueming Qian, Guoshuai Zhao, and Zhongyu Yang. 2026. EmoRes: Toward Adaptive Psychological Support via User-Agnostic Benchmark and Topic-Mining Agent. In Findings of the Association for Computational Linguistics: ACL 2026, pages 8575–8586, San Diego, California, United States. Association for Computational Linguistics.