Web2BigTable: Multi-Agent LLM System for Web-to-Table Search
A novel multi-agent system named Web2BigTable has been unveiled for web-to-table searches, catering to both breadth-focused tasks that require schema-aligned outputs with extensive coverage and depth-focused tasks that necessitate coherent reasoning across lengthy search paths. This framework utilizes a bi-level structure, where a top-level orchestrator breaks down tasks into sub-problems, which are then tackled in parallel by lower-level worker agents. Employing a closed-loop run-verify-reflect methodology, the system enhances both task decomposition and execution over time, utilizing persistent, human-readable external memory that evolves through self-updating mechanisms. The comprehensive details of this system can be found in arXiv paper 2604.27221.
Key facts
- Web2BigTable is a multi-agent LLM system for web-to-table search.
- It handles both breadth-oriented and depth-oriented search tasks.
- The architecture is bi-level with an orchestrator and worker agents.
- It uses a closed-loop run-verify-reflect process for improvement.
- The system employs persistent, human-readable external memory.
- Updates are self-evolving for each single agent.
- The paper is available on arXiv with ID 2604.27221.
- The announcement type is new.
Entities
Institutions
- arXiv