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

By PulseAugur Editorial · [1 sources] ·

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

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · 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…

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