---
id: 20260502-T0-03
title: "Web2BigTable：双层多智能体系统实现互联网级信息搜索"
title_en: "Web2BigTable: Bi-Level Multi-Agent System for Web-Scale Search"
url: https://ai.daily.yangsir.net/daily/20260502-T0-03
issue_date: 2026-05-02
publish_date: 2026-05-01T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2604.27221
---

# Web2BigTable：双层多智能体系统实现互联网级信息搜索

康奈尔大学提出Web2BigTable，一种双层多智能体LLM系统，可同时处理深度推理和大规模结构化信息提取。该系统解决了当前网络搜索的两个核心痛点：针对单一目标的深度推理和跨多源异构实体的结构化聚合。研究显示，该系统在20个测试任务中的性能超越现有方法，平均提升37%。该技术可应用于搜索引擎、知识图谱构建和大规模数据分析领域，显著提升信息处理效率。

## English Version

**Web2BigTable: Bi-Level Multi-Agent System for Web-Scale Search**

Cornell researchers developed Web2BigTable, a bi-level multi-agent LLM system that simultaneously handles deep reasoning on single targets and structured aggregation across multiple heterogeneous sources. It addresses two critical challenges in web search: deep reasoning and broad information extraction. The system outperformed existing methods by an average of 37% across 20 test tasks. This technology can enhance search engines, knowledge graph construction, and large-scale data analysis.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2604.27221)

**详情页**：https://ai.daily.yangsir.net/daily/20260502-T0-03

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