用群体智能探索代码优化，突破单个代理的思维局限。
SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery
- 构建多代理协同系统，由主控代理全局引导搜索
- 在15项任务中13项优于或相当当前顶尖方法
- 适合需要深度探索的自动化代码优化场景
长期运行的编程代理（如autoresearch）能持续发现开放问题的优化方案，但容易收敛到单一高层策略，后续仅进行低层修改，遗漏更优解。我们提出两个关键设计缺陷：单一代理累积上下文、仅暴露单一程序状态供编辑。为此引入SwarmResearch，一种由主控代理协调的多代理框架，多个搜索代理各自在独立git分支中以局部上下文运行。在开放式优化任务中，SwarmResearch在15项任务中的13项表现优于或等同于当前最优的LLM引导演化与多代理技术，得益于更高层次的探索能力。相比固定串行/并行规模，其动态调整并行度的调度策略在不同搜索深度上均能发现性能更优的解。
原文摘要 · Abstract (English)
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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