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arXiv · 2607.02807

SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery

Yuvraj Virk·Zack Edds·Chunqiu Steven Xia·Lingming Zhang
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Abstract

Long-running coding agents such as autoresearch can persistently discover optimizations for open-ended problems. However, they tend to converge onto a single high-level approach, then proceed with low-level edits while missing other superior approaches to the problem. We hypothesize two harness-level design choices contribute to this behavior: accumulating context in a single long-running agent and only exposing a single program state to edit. We introduce SwarmResearch, an orchestrator-subagent harness in which a Shepherd Agent uses global context to steer a population of Search Agents, each operating with local context in their respective git branch. On open-ended optimization tasks, SwarmResearch discovers better or comparable solutions to state-of-the-art LLM-guided evolution and multi-agent techniques on 13/15 tasks, driven by higher-level exploration. Compared with fixed scaling of serial and parallel agents, SwarmResearch's orchestrator-guided scaling discovers better-performing solutions by adapting parallelism at different search depths.

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Yuvraj Virk, Zack Edds, Chunqiu Steven Xia, Lingming Zhang. 2026-07-02. SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery. https://arxiv.org/abs/2607.02807

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