Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction
From Daily Paper Cast by Jingwen Liang, Gengyu Wang
May 5, 2026 · 24 min · Episode 1830
About this episode
This episode discusses Web2BigTable, a bi-level multi-agent system designed for efficient internet-scale information search and extraction.
🤗 Upvotes: 27 | cs.AI Authors: Yuxuan Huang, Yihang Chen, Zhiyuan He, Yuxiang Chen, Ka Yiu Lee, Huichi Zhou, Weilin Luo, Meng Fang, Jun Wang Title: Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction Arxiv: http://arxiv.org/abs/2604.27221v1 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 tasks demand schema-aligned outputs with wide coverage and cross-entity consistency, while depth-oriented tasks require coherent reasoning over long, branching search trajectories. We introduce \textbf{Web2BigTable}, a multi-agent framework for web-to-table search that supports both regimes. Web2BigTable adopts a bi-level architecture in which an upper-level orchestrator decomposes the task into sub-problems and lower-level worker agents solve them in parallel. Through a closed-loop run--verify--reflect process, the framework jointly improves decomposition and execution over time via persistent, human-readable external memory, with self-evolving updates to…
People in this episode
Hosts: Jingwen Liang, Gengyu Wang
Topics covered
- multi-agent systems
- information search
- data extraction
- web search
- artificial intelligence
- structured data
Keywords
- Web2BigTable
- multi-agent framework
- information extraction
- web-to-table search
- deep reasoning
- schema-aligned outputs
- cross-entity consistency
- closed-loop process
Mentioned in this episode
Organizations: Web2BigTable, Arxiv
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