SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery

• URL: http://arxiv.org/abs/2607.02807v1
• Date: Thu, 02 Jul 2026 22:47:35 GMT
• Title: SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery
• Abstract summary: Long-running coding agents such as autoresearch can persistently discover optimizations for open-ended problems.<n>We introduce SwarmResearch, an orchestrator-subagent harness in which a Shepherd Agent uses global context to steer a population of Search Agents.<n>On open-ended optimization tasks, SwarmResearch discovers better or comparable solutions to state-of-the-art LLM-guided evolution and multi-agent techniques.
• Score: 11.64410415121195
• License: http://creativecommons.org/licenses/by/4.0/
• 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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