Self-Optimizing Multi-Agent Systems for Deep Research
Given a user's complex information need, a multi-agent Deep Research system iteratively plans, retrieves, and synthesizes evidence across hundreds of documents to produce a high-quality answer. In one possible architecture, an orchestrator agent coordinates the process, while parallel worker agents execute tasks. Current Deep Research systems, however, often rely on hand-engineered prompts and static architectures, making improvement brittle, expensive, and time-consuming. We therefore explore various multi-agent optimization methods to show that enabling agents to self-play and explore different prompt combinations can produce high-quality Deep Research systems that match or outperform e...
| Authors | Arthur Câmara, Vincent Slot, Jakub Zavrel |
|---|---|
| Primary category | cs.IR |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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