发表机构
HSBC Business School, Peking University; Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology（北京大学汇丰商学院; 深圳先进技术大学人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用arXiv数据与匿名化作者面板，通过合成控制法等分析，识别出2026年数学领域因生成式AI引发的异常生产增长，明确了研究治理的核心约束。
AI 中文摘要
生成式AI正在改变理论与计算研究的生产条件，但其系统层面的效应需要将平台增长、领域特定分化与生产结构分离开来的衡量方法。我们收集了2018年1月至2026年8月期间arXiv的20个档案库的2080条月度观测数据，以及一个独立的匿名化数学作者面板。通过拟合至2025年12月的正则化凸合成控制法，我们识别出2026年1月至8月的异常情况，同时采用空间安慰剂、前一年伪保留样本、捐赠者重新拟合及替代前周期等方法评估比较稳健性。数学领域记录了47127条列表条目，较2025年高出33.5%，较42113条的合成反事实高出11.9%。合格的捐赠者与前周期设计得出的结果介于9.6%至14.9%之间，且数学领域在15个符合条件的安慰剂档案库中拥有最大的RMSPE比率。子领域增长广泛，30个主要math.*类别中有29个实现扩张。作者面板显示重复产出尾部明显增厚：至少提交5次的活跃作者单元占比从2.45%升至3.80%，而提交10次的尾部占比从0.21%升至0.49%。这些结果记录了2026年数学领域出现的新的、异常巨大的生产制度转变，其时间与生产结构，结合AI扩散和可验证研究任务的独立证据，与延迟扩散和能力阈值机制一致。比较设计识别出该异常，且独立三角测量法评估了与AI相关的解释。研究结果将验证、选择与注意力定位为研究治理的核心约束。
英文摘要
Generative AI is changing the production conditions of theoretical and computational research, but its sys tem level effects require measures that separate plat form growth, field specific divergence, and production structure. We assemble 2,080 monthly observations for twenty arXiv archives from January 2018 through Au gust 2026 and a separate pseudonymized Mathematics author panel. A regularized convex synthetic control fitted through December 2025 identifies the January August 2026 anomaly, while spatial placebos, prior year pseudo holdouts, donor refits, and alternative preperiods assess comparative robustness. Mathematics recorded 47,127 list entries, 33.5% above 2025 and 11.9% above a synthetic counterfactual of 42,113 entries. Qualified donor and preperiod designs yield 9.6% to 14.9%, and Mathematics has the largest RMSPE ratio among fifteen eligible placebo archives. Subfield growth is broad, with 29 of 30 primary math.* categories expanding. The author panel shows a marked thickening of the repeated output tail. The share of active author units produc ing at least five submissions rose from 2.45% to 3.80%, while the ten submission tail rose from 0.21% to 0.49%. These results document a new and unusually large 2026 Mathematics production regime shift. Its timing and production structure, combined with independent evi dence on AI diffusion and verifiable research tasks, are consistent with delayed diffusion and capability thresh old mechanisms. The comparative design identifies the anomaly, and separate triangulation evaluates AI related explanations. The findings locate verification, selection, and attention as central constraints for research gover nance.
