@inproceedings{cai-etal-2026-ievoagent,
title = "{IE}vo{A}gent: Evolving Conversational Agent based on User Implicit Feedback",
author = "Cai, Yichen and
Li, Jiayang and
Qiu, Junyuan and
Guo, Jingya and
You, Weitao and
Yang, Changyuan and
Sun, Lingyun and
Chen, Pei",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.441/",
pages = "9725--9743",
ISBN = "979-8-89176-390-6",
abstract = "Current conversational agents often follow static learning paradigms and miss the implicit, evolving feedback embedded in users' follow-up behaviors. We propose IEvoAgent, an evolving conversational agent framework that leverages the structured dependency between agent responses and user reactions. We construct an annotated dataset from LMSYS-Chat-1M and WildChat and find consistent response-conditioned feedback patterns. Based on this finding, IEvoAgent uses a conditional feedback distribution matrix to estimate expected feedback rewards, combining offline KTO alignment with an inference-time prompt-evolution mechanism driven by a dynamic matrix. Experiments on MT-Bench-101, WildBench, and FB-Bench show improvements over open-source baselines, indicating that mining implicit feedback supports better multi-turn alignment under evolving user preferences. Our code and dataset are available at https://github.com/Hualeez/IEvoAgent."
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<abstract>Current conversational agents often follow static learning paradigms and miss the implicit, evolving feedback embedded in users’ follow-up behaviors. We propose IEvoAgent, an evolving conversational agent framework that leverages the structured dependency between agent responses and user reactions. We construct an annotated dataset from LMSYS-Chat-1M and WildChat and find consistent response-conditioned feedback patterns. Based on this finding, IEvoAgent uses a conditional feedback distribution matrix to estimate expected feedback rewards, combining offline KTO alignment with an inference-time prompt-evolution mechanism driven by a dynamic matrix. Experiments on MT-Bench-101, WildBench, and FB-Bench show improvements over open-source baselines, indicating that mining implicit feedback supports better multi-turn alignment under evolving user preferences. Our code and dataset are available at https://github.com/Hualeez/IEvoAgent.</abstract>
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%0 Conference Proceedings
%T IEvoAgent: Evolving Conversational Agent based on User Implicit Feedback
%A Cai, Yichen
%A Li, Jiayang
%A Qiu, Junyuan
%A Guo, Jingya
%A You, Weitao
%A Yang, Changyuan
%A Sun, Lingyun
%A Chen, Pei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F cai-etal-2026-ievoagent
%X Current conversational agents often follow static learning paradigms and miss the implicit, evolving feedback embedded in users’ follow-up behaviors. We propose IEvoAgent, an evolving conversational agent framework that leverages the structured dependency between agent responses and user reactions. We construct an annotated dataset from LMSYS-Chat-1M and WildChat and find consistent response-conditioned feedback patterns. Based on this finding, IEvoAgent uses a conditional feedback distribution matrix to estimate expected feedback rewards, combining offline KTO alignment with an inference-time prompt-evolution mechanism driven by a dynamic matrix. Experiments on MT-Bench-101, WildBench, and FB-Bench show improvements over open-source baselines, indicating that mining implicit feedback supports better multi-turn alignment under evolving user preferences. Our code and dataset are available at https://github.com/Hualeez/IEvoAgent.
%U https://aclanthology.org/2026.acl-long.441/
%P 9725-9743
Markdown (Informal)
[IEvoAgent: Evolving Conversational Agent based on User Implicit Feedback](https://aclanthology.org/2026.acl-long.441/) (Cai et al., ACL 2026)
ACL
- Yichen Cai, Jiayang Li, Junyuan Qiu, Jingya Guo, Weitao You, Changyuan Yang, Lingyun Sun, and Pei Chen. 2026. IEvoAgent: Evolving Conversational Agent based on User Implicit Feedback. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9725–9743, San Diego, California, United States. Association for Computational Linguistics.