@inproceedings{wang-etal-2026-webxskill,
title = "{W}eb{XS}kill: Skill Learning for Autonomous Web Agents",
author = "Wang, Zhaoyang and
Wu, Qianhui and
Zhang, Xuchao and
Zhang, Chaoyun and
Yao, Wenlin and
Faisal, Fazle Elahi and
Peng, Baolin and
Qin, Si and
Nath, Suman and
Lin, Qingwei and
Bansal, Chetan and
Zhang, Dongmei and
Rajmohan, Saravan and
Gao, Jianfeng and
Yao, Huaxiu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.561/",
pages = "11837--11852",
ISBN = "979-8-89176-462-0",
abstract = "Autonomous web agents powered by large language models (LLMs) remain brittle on long-horizon browser workflows. A key bottleneck is a grounding gap in existing skill formulations: textual workflow skills provide natural language guidance but cannot be directly executed, while code-based skills execute without giving the agent step-level guidance for adaptation or recovery. We introduce WebXSkill, a framework that bridges this gap with executable skills, each pairing a parameterized action program with step-level natural-language guidance. WebXSkill operates in three stages: skill extraction mines reusable action subsequences from readily available synthetic agent trajectories and abstracts them into parameterized skills, skill organization indexes them into a URL-based graph for context-aware retrieval, and skill deployment exposes two complementary modes, grounded mode for fully automated execution and guided mode where skills serve as step-by-step instructions the agent follows with its native planning. WebXSkill demonstrates consistent improvements on WebArena, WebVoyager, and Online-Mind2Web. We further find that better skill deployment mode depends on a model{'}s plan and execution capability. The code is available at https://github.com/aiming-lab/WebXSkill."
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<abstract>Autonomous web agents powered by large language models (LLMs) remain brittle on long-horizon browser workflows. A key bottleneck is a grounding gap in existing skill formulations: textual workflow skills provide natural language guidance but cannot be directly executed, while code-based skills execute without giving the agent step-level guidance for adaptation or recovery. We introduce WebXSkill, a framework that bridges this gap with executable skills, each pairing a parameterized action program with step-level natural-language guidance. WebXSkill operates in three stages: skill extraction mines reusable action subsequences from readily available synthetic agent trajectories and abstracts them into parameterized skills, skill organization indexes them into a URL-based graph for context-aware retrieval, and skill deployment exposes two complementary modes, grounded mode for fully automated execution and guided mode where skills serve as step-by-step instructions the agent follows with its native planning. WebXSkill demonstrates consistent improvements on WebArena, WebVoyager, and Online-Mind2Web. We further find that better skill deployment mode depends on a model’s plan and execution capability. The code is available at https://github.com/aiming-lab/WebXSkill.</abstract>
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%0 Conference Proceedings
%T WebXSkill: Skill Learning for Autonomous Web Agents
%A Wang, Zhaoyang
%A Wu, Qianhui
%A Zhang, Xuchao
%A Zhang, Chaoyun
%A Yao, Wenlin
%A Faisal, Fazle Elahi
%A Peng, Baolin
%A Qin, Si
%A Nath, Suman
%A Lin, Qingwei
%A Bansal, Chetan
%A Zhang, Dongmei
%A Rajmohan, Saravan
%A Gao, Jianfeng
%A Yao, Huaxiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-webxskill
%X Autonomous web agents powered by large language models (LLMs) remain brittle on long-horizon browser workflows. A key bottleneck is a grounding gap in existing skill formulations: textual workflow skills provide natural language guidance but cannot be directly executed, while code-based skills execute without giving the agent step-level guidance for adaptation or recovery. We introduce WebXSkill, a framework that bridges this gap with executable skills, each pairing a parameterized action program with step-level natural-language guidance. WebXSkill operates in three stages: skill extraction mines reusable action subsequences from readily available synthetic agent trajectories and abstracts them into parameterized skills, skill organization indexes them into a URL-based graph for context-aware retrieval, and skill deployment exposes two complementary modes, grounded mode for fully automated execution and guided mode where skills serve as step-by-step instructions the agent follows with its native planning. WebXSkill demonstrates consistent improvements on WebArena, WebVoyager, and Online-Mind2Web. We further find that better skill deployment mode depends on a model’s plan and execution capability. The code is available at https://github.com/aiming-lab/WebXSkill.
%U https://aclanthology.org/2026.findings-emnlp.561/
%P 11837-11852
Markdown (Informal)
[WebXSkill: Skill Learning for Autonomous Web Agents](https://aclanthology.org/2026.findings-emnlp.561/) (Wang et al., Findings 2026)
ACL
- Zhaoyang Wang, Qianhui Wu, Xuchao Zhang, Chaoyun Zhang, Wenlin Yao, Fazle Elahi Faisal, Baolin Peng, Si Qin, Suman Nath, Qingwei Lin, Chetan Bansal, Dongmei Zhang, Saravan Rajmohan, Jianfeng Gao, and Huaxiu Yao. 2026. WebXSkill: Skill Learning for Autonomous Web Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 11837–11852, Budapest, Hungary. Association for Computational Linguistics.