Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction
📰 ArXiv cs.AI
Learn how Web2BigTable, a bi-level multi-agent LLM system, enables internet-scale information search and extraction with deep reasoning and structured aggregation
Action Steps
- Design a bi-level architecture using LLMs to handle breadth-oriented and depth-oriented tasks
- Implement a multi-agent system to enable structured aggregation across entities and sources
- Use schema-aligned outputs to ensure wide coverage and cross-entity consistency
- Develop a system to perform coherent reasoning over long search trajectories
- Evaluate the system's performance on internet-scale information search and extraction tasks
Who Needs to Know This
Researchers and developers in AI and information retrieval can benefit from this system, as it addresses the challenges of agentic web search and provides a novel approach to handling breadth-oriented and depth-oriented tasks
Key Insight
💡 Web2BigTable's bi-level architecture and multi-agent system enable deep reasoning and structured aggregation for effective internet-scale information search and extraction
Share This
🚀 Web2BigTable: A bi-level multi-agent LLM system for internet-scale info search & extraction! 🤖
Key Takeaways
Learn how Web2BigTable, a bi-level multi-agent LLM system, enables internet-scale information search and extraction with deep reasoning and structured aggregation
Full Article
Title: Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction
Abstract:
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 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 mul
Abstract:
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 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 mul
DeepCamp AI