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arXiv · 2604.02988

Self-Optimizing Multi-Agent Systems for Deep Research

Arthur Câmara·Vincent Slot·Jakub Zavrel
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Abstract

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 expert-crafted prompts.

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Arthur Câmara, Vincent Slot, Jakub Zavrel. 2026-04-03. Self-Optimizing Multi-Agent Systems for Deep Research. https://arxiv.org/abs/2604.02988

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