WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
Score
78提出递归委托机制和过程经验重用，填补了多智能体搜索在递归深度与协作自适应方面的空白。
论文结构完整，方法描述清晰，实验设计全面，消融和分析充分。
对构建复杂搜索助手和知识发现工具具有直接指导意义，但落地仍需工程适配。
2025年7月的最新论文，紧跟多智能体搜索前沿。
来自arXiv预印本，作者机构未明示但实验复现性较好。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper presents a novel method with thorough experiments, addressing key challenges of recursive depth and collaborative adaptability in multi-agent search.
AI commentary
「This paper is worth attention as it tackles key bottlenecks in existing multi-agent search systems regarding recursive depth and dynamic collaboration. For researchers and developers working on AI agents, knowledge retrieval, and complex reasoning, its ideas of process-level experience reuse and structured delegation are highly insightful.」
Quick overview
WebSwarm is a progressive recursive delegation framework that dynamically instantiates search nodes coupling local objectives with search modes, enabling task decomposition, recursive expansion, and agent collaboration. Experiments on multiple deep-and-wide search benchmarks show WebSwarm consistently outperforms single-agent and existing multi-agent baselines, effectively addressing the trade-off between depth and coverage.
- Proposes WebSwarm framework that enhances both search depth and coverage via recursive delegation and collaboration
- Dynamically instantiates search nodes, each can solve locally or delegate to child nodes
- First probes web information organization to guide node expansion and reuse process-level experience
- Achieves state-of-the-art performance on BrowseComp-Plus, WideSearch, and other benchmarks
Full text
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