@inproceedings{zhang-etal-2026-web,
title = "Web Sitemap Knowledge Can Enhance Autonomous Browsing",
author = "Zhang, Yuyao and
Lu, Hongyu and
Jin, Jiajie and
Qian, Hongjin and
Li, Shiyu and
Yang, Zhao and
Zhu, Yutao and
Wen, Ji-Rong and
Dou, Zhicheng",
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.1465/",
pages = "29307--29321",
ISBN = "979-8-89176-395-1",
abstract = "Recent advances in large language models (LLMs) have enabled web agents to perform interactive tasks on real-world websites. However, existing agents still suffer from limited robustness, efficiency, and task success, largely due to their lack of structural understanding of websites and the absence of browsing priors in pre-trained models. To address these challenges, this paper proposes the Web Agent Sitemap Protocol (WASP), an agent-oriented sitemap that integrate structured website knowledge into web agents. WASP adopts a dual-granularity design, providing global site-level structure and local page-level semantic and interaction guidance. We also introduce a framework LightASM for constructing such sitemaps by identifying core pages and generating concise semantic summaries and block-level descriptions. Experiments on real-world browsing benchmarks demonstrate that WASP substantially improves the robustness, efficiency, and effectiveness of LLM-based web agents without extra training."
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<abstract>Recent advances in large language models (LLMs) have enabled web agents to perform interactive tasks on real-world websites. However, existing agents still suffer from limited robustness, efficiency, and task success, largely due to their lack of structural understanding of websites and the absence of browsing priors in pre-trained models. To address these challenges, this paper proposes the Web Agent Sitemap Protocol (WASP), an agent-oriented sitemap that integrate structured website knowledge into web agents. WASP adopts a dual-granularity design, providing global site-level structure and local page-level semantic and interaction guidance. We also introduce a framework LightASM for constructing such sitemaps by identifying core pages and generating concise semantic summaries and block-level descriptions. Experiments on real-world browsing benchmarks demonstrate that WASP substantially improves the robustness, efficiency, and effectiveness of LLM-based web agents without extra training.</abstract>
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%0 Conference Proceedings
%T Web Sitemap Knowledge Can Enhance Autonomous Browsing
%A Zhang, Yuyao
%A Lu, Hongyu
%A Jin, Jiajie
%A Qian, Hongjin
%A Li, Shiyu
%A Yang, Zhao
%A Zhu, Yutao
%A Wen, Ji-Rong
%A Dou, Zhicheng
%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 zhang-etal-2026-web
%X Recent advances in large language models (LLMs) have enabled web agents to perform interactive tasks on real-world websites. However, existing agents still suffer from limited robustness, efficiency, and task success, largely due to their lack of structural understanding of websites and the absence of browsing priors in pre-trained models. To address these challenges, this paper proposes the Web Agent Sitemap Protocol (WASP), an agent-oriented sitemap that integrate structured website knowledge into web agents. WASP adopts a dual-granularity design, providing global site-level structure and local page-level semantic and interaction guidance. We also introduce a framework LightASM for constructing such sitemaps by identifying core pages and generating concise semantic summaries and block-level descriptions. Experiments on real-world browsing benchmarks demonstrate that WASP substantially improves the robustness, efficiency, and effectiveness of LLM-based web agents without extra training.
%U https://aclanthology.org/2026.findings-acl.1465/
%P 29307-29321
Markdown (Informal)
[Web Sitemap Knowledge Can Enhance Autonomous Browsing](https://aclanthology.org/2026.findings-acl.1465/) (Zhang et al., Findings 2026)
ACL
- Yuyao Zhang, Hongyu Lu, Jiajie Jin, Hongjin Qian, Shiyu Li, Zhao Yang, Yutao Zhu, Ji-Rong Wen, and Zhicheng Dou. 2026. Web Sitemap Knowledge Can Enhance Autonomous Browsing. In Findings of the Association for Computational Linguistics: ACL 2026, pages 29307–29321, San Diego, California, United States. Association for Computational Linguistics.