@inproceedings{wang-etal-2026-websync,
title = "{W}eb{S}ync: A Self-Evolving Web Agent Framework for Co-Evolving Strategic Advice and Executable Skills",
author = "Wang, Yuanlei and
Luo, Haohao and
Yi, Zihao and
Shen, Ying and
Lin, Liang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2238/",
pages = "50932--50952",
ISBN = "979-8-89176-459-0",
abstract = "With the advent of large language models, autonomous web agents have achieved more sophisticated capabilities in planning, reflecting, and tool using, enabling automating navigation in complex and dynamic web environments. Despite this, existing efforts to instill experiential learning through memory-centric retrieval or skill-centric synthesis are respectively limited by computational inefficiency and a lack of robustness, preventing web agents from truly self-evolving. To tackle this problem, we introduce a novel web agent framework called \textbf{WebSync}, which is designed to unify strategic advice and skill synthesis through a structured skill recipe to compile experience into executable skills more efficiently and robustly. Furthermore, it enables continuous self-evolving by using runtime feedback to trigger iterative recipe and skill refinement, thus achieving a robust transition from trial-and-error reasoning to proven execution. Experimental results on WebArena and WebVoyager benchmarks demonstrate the superiority and versatility of \textbf{WebSync} across multiple backbone large language models."
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<abstract>With the advent of large language models, autonomous web agents have achieved more sophisticated capabilities in planning, reflecting, and tool using, enabling automating navigation in complex and dynamic web environments. Despite this, existing efforts to instill experiential learning through memory-centric retrieval or skill-centric synthesis are respectively limited by computational inefficiency and a lack of robustness, preventing web agents from truly self-evolving. To tackle this problem, we introduce a novel web agent framework called WebSync, which is designed to unify strategic advice and skill synthesis through a structured skill recipe to compile experience into executable skills more efficiently and robustly. Furthermore, it enables continuous self-evolving by using runtime feedback to trigger iterative recipe and skill refinement, thus achieving a robust transition from trial-and-error reasoning to proven execution. Experimental results on WebArena and WebVoyager benchmarks demonstrate the superiority and versatility of WebSync across multiple backbone large language models.</abstract>
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%0 Conference Proceedings
%T WebSync: A Self-Evolving Web Agent Framework for Co-Evolving Strategic Advice and Executable Skills
%A Wang, Yuanlei
%A Luo, Haohao
%A Yi, Zihao
%A Shen, Ying
%A Lin, Liang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-websync
%X With the advent of large language models, autonomous web agents have achieved more sophisticated capabilities in planning, reflecting, and tool using, enabling automating navigation in complex and dynamic web environments. Despite this, existing efforts to instill experiential learning through memory-centric retrieval or skill-centric synthesis are respectively limited by computational inefficiency and a lack of robustness, preventing web agents from truly self-evolving. To tackle this problem, we introduce a novel web agent framework called WebSync, which is designed to unify strategic advice and skill synthesis through a structured skill recipe to compile experience into executable skills more efficiently and robustly. Furthermore, it enables continuous self-evolving by using runtime feedback to trigger iterative recipe and skill refinement, thus achieving a robust transition from trial-and-error reasoning to proven execution. Experimental results on WebArena and WebVoyager benchmarks demonstrate the superiority and versatility of WebSync across multiple backbone large language models.
%U https://aclanthology.org/2026.emnlp-main.2238/
%P 50932-50952
Markdown (Informal)
[WebSync: A Self-Evolving Web Agent Framework for Co-Evolving Strategic Advice and Executable Skills](https://aclanthology.org/2026.emnlp-main.2238/) (Wang et al., EMNLP 2026)
ACL