Yue Xu, Xiyang Wang, Hui Liu, Bolei He · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27863 · open-access PDF
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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