@inproceedings{dai-etal-2026-sead,
title = "{SEAD}: Self-Evolving Agent for Multi-Turn Service Dialogue",
author = "Dai, Yuqin and
Gao, Ning and
Zhang, Wei and
Wang, Jie and
Luozichen and
Wu, Ruiyuan and
Wang, Jinpeng and
Wang, Chaozheng",
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.180/",
pages = "3674--3684",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models (LLMs) have demonstrated remarkable capabilities in open-domain dialogues. However, their performance in service dialogues remains suboptimal, as these require agents to guide users toward specific business objectives while dynamically tracking states and adapting strategies. This gap stems from the scarcity of high-quality training data and the difficulty in simulating authentic, goal-oriented user behaviors. We propose SEAD (Self-Evolving Agent for Service Dialogue), a framework that enables agents to learn effective strategies without large-scale human annotations. SEAD decouples user modeling into two components: a Profile Controller that generates diverse user states to manage training curriculum, and a User Simulator that focuses on realistic role-playing. This design ensures the environment provides adaptive training scenarios rather than acting as an unfair adversary."
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<abstract>Large Language Models (LLMs) have demonstrated remarkable capabilities in open-domain dialogues. However, their performance in service dialogues remains suboptimal, as these require agents to guide users toward specific business objectives while dynamically tracking states and adapting strategies. This gap stems from the scarcity of high-quality training data and the difficulty in simulating authentic, goal-oriented user behaviors. We propose SEAD (Self-Evolving Agent for Service Dialogue), a framework that enables agents to learn effective strategies without large-scale human annotations. SEAD decouples user modeling into two components: a Profile Controller that generates diverse user states to manage training curriculum, and a User Simulator that focuses on realistic role-playing. This design ensures the environment provides adaptive training scenarios rather than acting as an unfair adversary.</abstract>
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%0 Conference Proceedings
%T SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
%A Dai, Yuqin
%A Gao, Ning
%A Zhang, Wei
%A Wang, Jie
%A Wu, Ruiyuan
%A Wang, Jinpeng
%A Wang, Chaozheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%A Luozichen
%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 dai-etal-2026-sead
%X Large Language Models (LLMs) have demonstrated remarkable capabilities in open-domain dialogues. However, their performance in service dialogues remains suboptimal, as these require agents to guide users toward specific business objectives while dynamically tracking states and adapting strategies. This gap stems from the scarcity of high-quality training data and the difficulty in simulating authentic, goal-oriented user behaviors. We propose SEAD (Self-Evolving Agent for Service Dialogue), a framework that enables agents to learn effective strategies without large-scale human annotations. SEAD decouples user modeling into two components: a Profile Controller that generates diverse user states to manage training curriculum, and a User Simulator that focuses on realistic role-playing. This design ensures the environment provides adaptive training scenarios rather than acting as an unfair adversary.
%U https://aclanthology.org/2026.findings-acl.180/
%P 3674-3684
Markdown (Informal)
[SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue](https://aclanthology.org/2026.findings-acl.180/) (Dai et al., Findings 2026)
ACL
- Yuqin Dai, Ning Gao, Wei Zhang, Jie Wang, Luozichen, Ruiyuan Wu, Jinpeng Wang, and Chaozheng Wang. 2026. SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue. In Findings of the Association for Computational Linguistics: ACL 2026, pages 3674–3684, San Diego, California, United States. Association for Computational Linguistics.