SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery
SwarmResearch is an orchestrator-subagent harness that uses global context to steer a population of search agents, achieving better or comparable results on open-ended optimization tasks compared to state-of-the-art methods.
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[Submitted on 2 Jul 2026]
Title:SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery
View a PDF of the paper titled SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery, by Yuvraj Virk and 3 other authors
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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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.02807 [cs.AI]
(or arXiv:2607.02807v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.02807
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yuvraj Virk [view email] [v1] Thu, 2 Jul 2026 22:47:35 UTC (350 KB)
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