AI 中文总结
本文提出多模态框架AgenTag，基于PR的文本、行为等模态，结合监督对比学习实现开放世界AI编码智能体归因，在AIDev数据集上取得良好效果，且发现归因主要依赖PR描述和提交消息等行为指纹。
AI 中文摘要
AI编码智能体越来越多地以开发者的账户提交代码合并请求（PR），掩盖了实际生成变更的主体，可靠的归因对于代码仓库治理、AI辅助软件开发的实证研究以及衡量AI编码智能体的影响十分重要。现有研究聚焦于已知智能体的闭集识别，尚未充分探索开放世界下AI编码智能体归因的实际局限。本文提出AgenTag，一种用于开放世界AI编码智能体归因的多模态框架，在AIDev数据集上进行评估，该数据集包含来自5个AI编码智能体的33580个PR和6618个人类作者的PR。我们用文本、行为和代码三类模态表征每个PR，对比传统分类与监督对比学习，用于开放集识别和新智能体的少样本注册。AgenTag以加权F1值0.96（宏F1值0.84）识别作者智能体，以平衡F1值0.89区分AI与人类作者的PR，以AUC值0.84检测新智能体。我们进一步发现，PR描述和提交消息提供了几乎所有归因信号，而代码差异在多种表征中贡献极小，表明编码智能体的区分主要依据其变更的沟通方式而非生成的代码；且这些行为指纹在移除明确的自我披露标记后仍存在，说明归因主要依赖潜在风格特征。这些发现表明，AI编码智能体的可靠归因是可行的，并明确了归因精度与实现所需信息之间的实际权衡。
英文摘要
AI coding agents increasingly author pull requests (PRs), often under developers' own accounts, obscuring who actually produced a change. Reliable attribution is important for repository governance, empirical studies of AI-assisted software development, and measuring the impact of AI coding agents. Existing work focuses on closed-set identification of known agents, leaving the practical limits of open-world AI coding agent attribution largely unexplored. In this paper, we present AgenTag, a multimodal framework for open-world AI coding agent attribution, evaluated on AIDev, comprising 33,580 PRs from five AI coding agents and 6,618 human-authored PRs. We represent each PR using textual, behavioral, and code-based modalities, and compare conventional classification with supervised contrastive learning for open-set recognition and few-shot enrollment of previously unseen agents. AgenTag identifies authoring agents with a weighted F1 of 0.96 (macro F1 of 0.84), distinguishes AI- from human-authored PRs with a balanced F1 of 0.89, and detects previously unseen agents with an AUC of 0.84. We further show that PR descriptions and commit messages provide nearly all of the attribution signal, whereas code diffs contribute little across multiple representations, indicating that coding agents are distinguished primarily by how they communicate changes rather than by the code they generate. Moreover, these behavioral fingerprints persist after removing explicit self-disclosed markers, demonstrating that attribution relies largely on latent stylistic characteristics. These findings show that reliable attribution of AI coding agents is feasible and clarify the practical trade-offs between attribution accuracy and the information required to achieve it.
