WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Xiaoshuai Song, Liancheng Zhang, Kangzhi Zhao, Yutao Zhu, Zhongyuan Wang, Guanting Dong, Jinghan Yang, Han Li, Kun Gai, Ji-Rong Wen, Zhicheng Dou
• Venue: arXiv: cs.CL
• Published: 2026-07-09
• Collected: 2026-07-10
• URL: http://arxiv.org/abs/2607.08662v1
• PDF: https://arxiv.org/pdf/2607.08662v1
• DOI: UNKNOWN

Plain-Language Brief

WebSwarm is a framework that uses multiple language model-based search agents to perform complex, deep-and-wide web searches. It aims to overcome the limitations of single ReAct-style agents by dynamically instantiating agentic search nodes and enabling collaboration among them. The system outperforms single-agent and multi-agent baselines on various tasks, as shown in experiments.

What the Document Says

• WebSwarm is a framework for web search using multiple language model-based agents.
• It addresses the limitations of single ReAct-style agents in handling depth and coverage simultaneously.
• The system constructs task decomposition, recursive expansion, and agent collaboration during inference.
• Each node can solve its objective itself or delegate child nodes.
• Nodes return evidence and results to parent nodes for further expansion, revision, or aggregation.
• WebSwarm grounds subsequent node expansion by probing how task-relevant information is organized on the web.
• It outperforms single-agent and multi-agent baselines on deep, wide, and interleaved deep-and-wide tasks, as shown in experiments on BrowseComp-Plus, WideSearch, DeepWideSearch, and GISA.

Key Concepts Mentioned

• WebSwarm
• language model-based search agents
• ReAct-style agent
• task decomposition
• recursive expansion
• agent collaboration
• BrowseComp-Plus
• WideSearch
• DeepWideSearch
• GISA

Claims Made by the Document

• WebSwarm outperforms single-agent and multi-agent baselines on deep, wide, and interleaved deep-and-wide tasks.

Evidence Used

• Experiments on BrowseComp-Plus, WideSearch, DeepWideSearch, and GISA show that WebSwarm consistently outperforms single-agent and multi-agent baselines on the specified tasks.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Large language model (LLM)-based web search agents are transforming information seeking from simple factoid question answering into complex, deep-and-wide search and research-oriented tasks. A single ReAct-style agent is constrained by one long trajectory and limited context, making it difficult to handle depth and coverage simultaneously. Existing multi-agent systems improve search coverage through parallel execution and aggregation, but still exhibit clear limitations in recursive depth, collaboration adaptability, and evidence-grounded expansion. We propose WebSwarm, a progressive recursive delegation framework that jointly constructs task decomposition, recursive expansion, and agent collaboration during inference. WebSwarm dynamically instantiates agentic search nodes, each coupling a local objective with a search mode that specifies how the node should organize search and collaboration. Each node can either solve its objective itself or further delegate child nodes; after solving, it returns evidence and results upward, enabling parent nodes to further expand, revise, or aggregate the search process. To guide this process, WebSwarm first probes how task-relevant information is organized on the web to ground subsequent node expansion, and reuses process-level experience across homogeneous sibling nodes. Experiments on BrowseComp-Plus, WideSearch, DeepWideSearch, and GISA show that WebSwarm consistently outperforms single-agent and multi-agent baselines on deep, wide, and interleaved deep-and-wide tasks. Further analyses of ablation, task difficulty, web tool efficiency, and model generalization explain WebSwarm's effectiveness and provide insights for multi-agent search systems.