titleWeb2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction
authorsYuxuan Huang, Yihang Chen, Zhiyuan He, Yuxiang Chen, Ka Yiu Lee, Huichi Zhou, Weilin Luo, Meng Fang, Jun Wang
date2026-04-29
venuearXiv (preprint, not peer reviewed)
idarXiv:2604.27221
upvotes39 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractAgentic 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 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 38.50 (7.5times the second best at 5.10), Row F1 of 63.53 (+25.03 over the second best), and Item F1 of 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.

cite

@misc{huang2026web,
  title={Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction},
  author={Yuxuan Huang and Yihang Chen and Zhiyuan He and Yuxiang Chen and Ka Yiu Lee and Huichi Zhou and Weilin Luo and Meng Fang and Jun Wang},
  year={2026},
  eprint={2604.27221},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2604.27221}
}