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

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

URL: http://arxiv.org/abs/2604.02971v1
Date: Fri, 03 Apr 2026 11:19:17 GMT
Title: InfoSeeker: A Scalable Hierarchical Parallel Agent Framework for Web Information Seeking
Abstract summary: We present framework, a hierarchical framework based on principle of near-decomposability, containing a strategic textitHost, multiple textitManagers and parallel textitWorkers.<n>By leveraging aggregation and reflection mechanisms at the Manager layer, our framework enforces strict context isolation to prevent saturation and error propagation.<n>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
Score: 39.22757710468795
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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 https://github.com/agent-on-the-fly/InfoSeeker

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