X-raying the arXiv: A Large-Scale Analysis of arXiv Submissions'Source Files

2026-01-16

AI Summary

This study addresses the widespread presence of redundant content unrelated to PDF generation in arXiv source submissions, which poses risks of storage inefficiency and inadvertent disclosure of sensitive information. Through a longitudinal analysis of approximately 600,000 submissions from 2015 to 2025, this work presents the first large-scale characterization of the issue, revealing that submissions contain an average of 27% redundant data—amounting to over 580 GB—and identifying instances of inappropriate text and potential research security breaches. We developed an automated detection pipeline integrating large-scale parsing, content classification, and qualitative analysis to assess and categorize such redundancies. Based on these findings, we propose concrete data hygiene recommendations and have issued alerts to both arXiv administrators and contributing authors, establishing a new paradigm for safeguarding academic infrastructure.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

arXiv is the largest open-access repository for scientific literature. When submitting a paper, authors upload the manuscript's source files, from which the final PDF is compiled. These source files are also publicly downloadable, potentially exposing data unrelated to the published paper -- such as figures, documents, or comments -- that may unintentionally reveal confidential information or simply waste storage space. We thus ask ourselves:"What can be found within the source files of arXiv submissions?"We present a longitudinal analysis of ~600,000 submissions appeared on arXiv between 2015--2025. For each submission, we examine the uploaded source files to quantify and characterize data not required for producing the respective PDF. On average, 27% of the data in each submission are unnecessary, totaling>580 GB of redundant content across our dataset. Qualitative inspection reveals the presence of offensive/inappropriate text (e.g.,"WTF does this mean?") and experimental details that could disclose ongoing research. We have contacted arXiv's leadership team, as well as the authors of affected papers to alert them of these issues. Finally, we propose recommendations and an automated tool to detect and analyze arXiv submissions residual data at scale, aiming to improve data hygiene in the arXiv's ecosystem.

Problem

• arXiv
• source files
• data leakage
• redundant content
• scientific publishing

Innovation

• arXiv
• source file analysis
• data hygiene
• redundant content detection
• scientific reproducibility