We present the results of a large-scale computational analysis of mathematical papers from the ArXiv repository, demonstrating a comprehensive system that not only detects mathematical errors but provides complete referee reports with journal tier recommendations. Our automated analysis system processed over 37,000 papers across multiple mathematical categories, revealing significant error rates and quality distributions. Remarkably, the system identified errors in papers spanning three centuries of mathematics, including works by Leonhard Euler (1707-1783) and Peter Gustav Lejeune Dirichlet (1805-1859), as well as contemporary Fields medalists. In Numerical Analysis (math.NA), we observed an error rate of 9.6\% (2,271 errors in 23,761 papers), while Geometric Topology (math.GT) showed 6.5\% (862 errors in 13,209 papers). Strikingly, Category Theory (math.CT) showed 0\% errors in 93 papers analyzed, with evidence suggesting these results are ``easier'' for automated analysis. Beyond error detection, the system evaluated papers for journal suitability, recommending 0.4\% for top generalist journals, 15.5\% for top field-specific journals, and categorizing the remainder across specialist venues. These findings demonstrate both the universality of mathematical error across all eras and the feasibility of automated comprehensive mathematical peer review at scale. This work demonstrates that the methodology, while applied here to mathematics, is discipline-agnostic and could be readily extended to physics, computer science, and other fields represented in the ArXiv repository.

翻译：我们展示了对arXiv存储库中数学论文进行大规模计算分析的结果，该系统不仅能检测数学错误，还能提供完整的审稿报告及期刊级别推荐。我们的自动化分析系统处理了超过37,000篇涵盖多个数学领域的论文，揭示了显著的错误率与质量分布。值得注意的是，系统识别出的错误横跨三个世纪的数学文献，包括莱昂哈德·欧拉（1707-1783）和彼得·古斯塔夫·勒热纳·狄利克雷（1805-1859）的著作，以及当代菲尔兹奖得主的论文。在数值分析（math.NA）领域，我们观察到9.6%的错误率（23,761篇论文中出现2,271处错误），而几何拓扑学（math.GT）的错误率为6.5%（13,209篇论文中出现862处错误）。引人注目的是，范畴论（math.CT）在分析的93篇论文中显示出0%错误率，有证据表明这些结果对自动化分析而言“更易处理”。除错误检测外，系统还评估了论文的期刊适用性，推荐0.4%的论文投稿至顶尖综合期刊，15.5%投稿至顶尖领域专精期刊，其余则归类于专业期刊。这些发现既证明了数学错误在所有历史时期的普遍性，也验证了大规模自动化全面数学同行评审的可行性。本研究表明，该方法虽应用于数学领域，但具有学科无关性，可轻松扩展至物理学、计算机科学及arXiv存储库涵盖的其他学科领域。