arXiv implements 1-year ban for papers containing incontrovertible evidence of unchecked LLM-generated errors, such as hallucinated references or results. [N]

2026-05-15 · Source: Machine Learning · Field: Science & Research — Research Methodology & Innovation, Mathematics & Computational Sciences · Depth: Intermediate, medium

Summary

arXiv, through its moderator Thomas G. Dietterich, has clarified new penalties for authors submitting papers with unchecked Large Language Model (LLM) generated errors. Effective immediately, submissions containing "incontrovertible evidence" of such errors, like hallucinated references or LLM meta-comments, will result in a 1-year ban from arXiv for all authors. Following this ban, subsequent submissions from these authors will only be accepted if they have already been accepted by a reputable peer-reviewed venue. This policy reinforces the arXiv Code of Conduct, which holds authors fully responsible for all content, regardless of generation method. The move addresses a growing concern within the scientific community regarding the integrity of preprints, with some commentators likening the influx of unchecked LLM-generated content to a "DDOS attack" on scientific publishing.

Key takeaway

For AI Scientists and Research Scientists submitting to arXiv, you must meticulously verify all content, especially if generated by LLMs. Failing to check for hallucinated references, placeholder text, or LLM meta-comments can lead to a 1-year ban and a significant hurdle for future preprints, as you will need prior peer-reviewed acceptance, which can be a challenging Catch-22 given typical publishing workflows.

Key insights

Authors are fully responsible for all content in their submissions, even if generated by AI.

Principles

• Author responsibility is paramount.
• Unchecked AI output undermines trust.

Method

arXiv implements a 1-year ban for papers with incontrovertible LLM errors, followed by a requirement for prior peer-reviewed acceptance for future submissions.

In practice

• Verify all LLM-generated content.
• Manually check all references and data.

Topics

• arXiv Policy
• LLM Errors
• Scientific Misconduct
• Academic Publishing
• Research Integrity

Best for: AI Scientist, Research Scientist, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.