导读
potential incompleteness of disclosure;
【编辑部】这对你意味着什么
该研究提出的 ProofTrace 框架能够识别数学论文中 AI 参与证明的特征，且在 AI 润色后仍保持检测稳定性，而现有基于文本的检测器性能大幅下降。对于在美国从事数学、计算机科学或相关领域研究的华人学者和学生，这意味着期刊和会议可能采用更有效的技术手段审查论文中 AI 的实质性贡献，影响论文撰写与投稿策略。
英文标题：The Gold Rush in AI4Math, II: What are signatures of AI-assisted Proofs?
arXiv编号：2611.08412
作者：Yuanchuan Guo, Jiashun Jin, Zheng Tracy Ke
来源：arXiv
原文链接：https://arxiv.org/abs/2611.08412
中文摘要：
The growing use of AI in mathematics raises a question: can we detect AI involvement in proofs, separately from AI assistance with writing? Historical arXiv papers, recent papers disclosing substantive AI use, and manuscripts released by OpenAI provide a rich empirical basis for studying this question. However, several challenges remain: lack of matched case-control pairs; potential incompleteness of disclosure; and lack of ready-to-use features designed to capture signals of AI involvement in proofs. To address these challenges, we construct five cohorts comprising 8,281 papers by combining existing manuscripts with those we produced using generative AI. These cohorts span different levels of AI involvement and include matched pairs of papers addressing the same research problems. We then introduce ProofTrace, a framework that represents each paper as a graph of immediate proof support (GIPS) and extracts interpretable features of mathematical organization. Our study yields three discoveries. First, principal-component projections of our features reveal a qualitative gradient of AI involvement, where AI-reworded papers remain close to human papers, the two AI-generated cohorts depart in broadly shared directions, and papers disclosing substantive AI use occupy intermediate positions which shifts toward AI-generated cohorts over time. Second, we identify recurring signatures of AI-generated proofs, such as fewer externally cited results, greater reliance on intermediate conclusions, and altered graph connectivity; many of these signatures persist under controls for research field, manuscript length, or graph size. Finally, in a separate detection experiment, ProofTrace distinguishes human-written papers from those disclosing substantive AI use. Its performance remains stable under AI rewording, whereas the performance of state-of-the-art text-based detectors declines sharply.
本文由华人空间 AI 论文助手自动抓取翻译，仅供学术交流。
【编辑部】今日讨论
你在撰写或审稿数学论文时，是否遇到过需要界定 AI 对证明实质性贡献的情况？
