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

• URL: http://arxiv.org/abs/2604.27221v1
• Date: Wed, 29 Apr 2026 21:43:16 GMT
• Title: Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction
• Abstract summary: textbfWeb2BigTable is a multi-agent framework for web-to-table search.<n>Web2BigTable sets a new state of the art on WideSearch, reaching an Avg@4 Success Rate of textbf38.50 ($7.5times$ the second best at 5.10), Row F1 of textbf63.53 (+25.03 over the second best) and Item F1 of textbf80.12 (+14.42 over the second best)<n>It also generalises to depth-oriented search on XBench
• Score: 38.55682016152131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 each single-agent. During execution, workers coordinate through a shared workspace that makes partial findings visible, allowing them to reduce redundant exploration, reconcile conflicting evidence, and adapt to emerging coverage gaps. Web2BigTable sets a new state of the art on WideSearch, reaching an Avg@4 Success Rate of \textbf{38.50} ($7.5\times$ the second best at 5.10), Row F1 of \textbf{63.53} (+25.03 over the second best), and Item F1 of \textbf{80.12} (+14.42 over the second best). It also generalises to depth-oriented search on XBench-DeepSearch, achieving 73.0 accuracy. Code is available at https://github.com/web2bigtable/web2bigtable.

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