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Web2BigTable system enhances LLM web search with bi-level agent architecture

Researchers have developed Web2BigTable, a novel bi-level multi-agent system designed for large-scale internet information search and extraction. This framework features an orchestrator that breaks down tasks for parallel processing by lower-level worker agents. Through a continuous run-verify-reflect cycle and a shared workspace, Web2BigTable enhances decomposition and execution, leading to improved data consistency and coverage. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Sets new SOTA on web search benchmarks, potentially improving data aggregation and reasoning capabilities for AI systems.

RANK_REASON This is a research paper detailing a new system and its benchmark performance.

Read on arXiv cs.AI →

COVERAGE [1]

  1. arXiv cs.AI TIER_1 · Yuxuan Huang, Yihang Chen, Zhiyuan He, Yuxiang Chen, Ka Yiu Lee, Huichi Zhou, Weilin Luo, Meng Fang, Jun Wang ·

    Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction

    arXiv:2604.27221v1 Announce Type: new Abstract: Agentic web search increasingly faces two distinct demands: deep reasoning over a single target, and structured aggregation across many entities and heterogeneous sources. Current systems struggle on both fronts. Breadth-oriented ta…