Such artifacts strongly suggest the authors did not review the generated content before submission. When moderators encounter this kind of evidence, they may conclude that the reliability of the entire paper is questionable.
If moderators determine that a submission contains clear evidence of unchecked LLM output, several consequences can follow:
The policy applies to every author on the paper, reflecting arXiv’s rule that authors share responsibility for the content they sign.
Importantly, arXiv has not banned the use of generative AI in research writing. The platform’s stance is that AI tools can be used—but authors must carefully verify and take responsibility for the output.
This mirrors broader academic norms: software may assist with drafting or analysis, but the authors remain accountable for accuracy, citations, and claims.
Moderation decisions on arXiv are not final without recourse. Authors who believe a decision was incorrect can appeal through arXiv’s moderation appeals process, asking moderators to reconsider the submission or classification.
In some situations, arXiv may require that a paper be accepted by a conventional journal before considering an appeal related to a declined submission.
arXiv is one of the most important infrastructure platforms for early scientific communication. The new enforcement approach reflects a growing concern across academia: generative AI can accelerate writing, but it also increases the risk of fabricated citations and subtle factual errors.
Rather than banning AI outright, arXiv’s approach sends a clear signal: AI assistance is acceptable, but unchecked AI output is not. The responsibility still rests squarely with the researchers whose names appear on the paper.
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