Large Language Models (LLMs) increasingly automate software engineering tasks. While recent studies highlight the accelerated adoption of ``AI as a teammate'' in Open Source Software (OSS), developer interaction patterns remain under-explored. In this work, we investigated project-level guidelines and developers' interactions with AI-assisted pull requests (PRs) by expanding the AIDev dataset to include finer-grained contributor code ownership and a comparative baseline of human-created PRs. We found that over 67.5\% of AI-co-authored PRs originate from contributors without prior code ownership. Despite this, the majority of repositories lack guidelines for AI-coding agent usage. Notably, we observed a distinct interaction pattern: AI-co-authored PRs are merged significantly faster with minimal feedback. In contrast to human-created PRs where non-owner developers receive the most feedback, AI-co-authored PRs from non-owners receive the least, with approximately 80\% merged without any explicit review. Finally, we discuss implications for developers and researchers.

翻译：大型语言模型（LLM）正日益自动化软件工程任务。尽管近期研究强调了“AI作为协作者”在开源软件（OSS）中的加速应用，开发者交互模式仍待深入探索。本研究通过扩展AIDev数据集，纳入细粒度的贡献者代码所有权信息及人工创建拉取请求（PR）的对比基线，调查了项目级指导原则及开发者与AI辅助PR的交互情况。我们发现超过67.5%的AI协同创作PR来自无先前代码所有权的贡献者。尽管如此，大多数代码库仍缺乏AI编码代理使用规范。值得注意的是，我们观察到一种独特的交互模式：AI协同PR以极简反馈被显著更快地合并。与人工创建PR中非所有者开发者获得最多反馈的情况相反，来自非所有者的AI协同PR获得反馈最少，约80%在无明确审查的情况下被合并。最后，我们讨论了其对开发者和研究者的启示。