跳到正文
arXiv cs.AI· Sunghwan Kim, Deogyong Kim, Sangam Lee, Donguk Kwon, Dongha Lee·· 6 天前AI 评分38

Web$hell:面向智能体网页访问的统一接口

Web$hell: A Unified Interface for Agentic Web Access

AI 导读

Web$hell 是一个将网页访问统一到单一访问模型的接口,通过 WebContract 定义的通用 schema,由 WebCompiler 把实时网页编译为可寻址的信息与动作对象,存入 WebCache 并通过共享操作集处理,为智能体提供一致、可操作且持久的网页访问。在网页搜索、网页导航与深度研究基准上的实验显示,Web$hell 以更低成本取得较强任务表现。

正文 · 原文

View PDF HTML (experimental)

Abstract:Large language model (LLM) agents are becoming direct users of the open web. As they take on increasingly complex and information-intensive tasks, effective web access is becoming a core capability for agentic problem solving. Existing approaches have advanced agentic web access through different access models, including search-augmented agents, browser-based agents, and, more recently, filesystem-based agents. Nevertheless, as users expect agents to complete long-horizon, end-to-end tasks, they increasingly need to conduct broad information gathering, perform browser actions, and revisit acquired evidence within a single trajectory. Yet our preliminary analyses reveal that existing access models struggle to meet these demands. They primarily support either information gathering or browser interaction, while combining their capabilities can involve substantial costs or performance trade-offs. To this end, we present Web\$hell, an interface that unifies web access under a single access model. Using a common schema defined by WebContract, WebCompiler compiles live webpages into addressable information and action objects. These objects are maintained in WebCache and handled through a shared set of operations, providing agents with consistent, operable, and persistent web access. Through extensive experiments across web search, web navigation, and deep research benchmarks, we show that Web\$hell achieves strong task performance at lower cost. Our results further indicate the importance of interface design for effective web access, with Web\$hell opening new possibilities for more capable and efficient LLM agents on the web.
Comments: Work in progress
Subjects: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
Cite as: arXiv:2610.13416 [cs.AI]
  (or arXiv:2610.13416v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2610.13416

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: SungHwan Kim [view email]
[v1] Fri, 9 Oct 2026 11:23:40 UTC (7,892 KB)

来源:arXiv cs.AI · arxiv.org