InfoSeeker: A Scalable Hierarchical Parallel Agent Framework for Web Information Seeking

Computer Science > Artificial Intelligence [Submitted on 3 Apr 2026] InfoSeeker: A Scalable Hierarchical Parallel Agent Framework for Web Information Seeking Ka Yiu Lee, Yuxuan Huang, Zhiyuan He, Huichi Zhou, Weilin Luo, Kun Shao, Meng Fang, Jun Wang Recent agentic search systems have made substantial progress by emphasising deep, multi-step reasoning. However, this focus often overlooks the challenges of wide-scale information synthesis, where agents must aggregate large volumes of heterogeneous evidence across many sources. As a result, most existing large language model agent systems face severe limitations in data-intensive settings, including context saturation, cascading error propagation, and high end-to-end latency. To address these challenges, we present \framework, a hierarchical framework based on principle of near-decomposability, containing a strategic \textit{Host}, multiple \textit{Managers} and parallel \textit{Workers}. By leveraging aggregation and reflection mechanisms at the Manager layer, our framework enforces strict context isolation to prevent saturation and error propagation. Simultaneously, the parallelism in worker layer accelerates the speed of overall task execution, mitigating the significant latency. Our evaluation on two complementary benchmarks demonstrates both efficiency ( 3-5 \times speed-up) and effectiveness, achieving a 8.4\% success rate on WideSearch-en and 52.9\% accuracy on BrowseComp-zh. The code is released at this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.02971 [cs.AI]   (or arXiv:2604.02971v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.02971 Focus to learn more Submission history From: Yuxuan Huang [view email] [v1] Fri, 3 Apr 2026 11:19:17 UTC (546 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)