Yue Xu, Xiyang Wang, Hui Liu, Bolei He · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27863 · open-access PDF
越来越多的语言模型用于代码生成需要方法来区分纯人类编写的代码和AI参与的代码:其中任何部分,无论小,是由模型生成的或实质上修改的文件。 除了标记这些文件,我们旨在本地化可归因于AI的行或块。 现有的方法将文件压缩成一个单一的分数,从表面风格特征或全球代码代表性,这平均了当只有少数块AI参与时的证据。
Machine translation (Meta M2M100); the English original follows.
The increasing use of large language models for code generation calls for methods that distinguish purely human-written code from AI-involved code: files in which any part, however small, was generated or substantially modified by a model. Beyond flagging such files, we aim to localize the lines or blocks attributable to AI. Existing approaches compress a file into a single score, from surface stylistic features or a global code representation, which averages away the evidence when only a few blocks are AI-involved. We propose a framework that models AI authorship at the token level and predic
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