| name | webswarm-recursive-multi-agent-orchestration-for-deep-and |
| description | WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search. 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 ... Activation: agent, multi-agent, agentic, llm, orchestration |
| metadata | {"arxiv_id":"2607.08662","published":"2026-07-09","authors":"Xiaoshuai Song, Liancheng Zhang, Kangzhi Zhao, Yutao Zhu, Zhongyuan Wang et al.","tags":["agent","multi-agent","agentic","llm","orchestration","inference","framework","text"]} |
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
Core Concept
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.
Key Innovations
1. Problem Formulation
- Addresses the challenge of agent with a novel approach
- Proposes a systematic framework for evaluation and analysis
- Demonstrates significant improvements over existing methods
2. Methodology
- Introduces new techniques for multi-agent
- Leverages agentic for improved performance
- Provides comprehensive evaluation across multiple settings
3. Practical Impact
- Applicable to real-world scenarios involving llm
- Provides actionable insights for practitioners
- Open-source implementation available for reproducibility
Technical Details
Approach
The paper presents a method that combines agent, multi-agent, agentic to address the core problem. The framework is designed to be generalizable and applicable across different settings.
Key Results
- Demonstrates state-of-the-art performance on benchmark tasks
- Provides comprehensive ablation studies
- Shows robustness across different experimental conditions
Applications
Primary Use Cases
- Research and development in agent
- Benchmark evaluation and comparison
- Practical deployment scenarios
Integration Considerations
- Compatible with existing multi-agent pipelines
- Can be adapted for domain-specific applications
- Supports reproducible research practices
Implementation Notes
Data Requirements
- Requires appropriate training/evaluation data
- Supports standard data formats
- Includes preprocessing recommendations
Training and Evaluation
- Follows standard evaluation protocols
- Provides reproducible experimental settings
- Includes statistical significance analysis
Related Work
- Builds upon recent advances in agent, multi-agent, agentic
- Extends existing frameworks with novel contributions
- Provides comprehensive comparison with prior methods
References
- Paper: arXiv:2607.08662 (2026-07-09)
- Authors: Xiaoshuai Song, Liancheng Zhang, Kangzhi Zhao, Yutao Zhu, Zhongyuan Wang et al.
- Categories: cs.CL, cs.AI, cs.MA
