NeurIPS leadership doesn’t think hallucinated references are necessarily disqualifying; see the full article from Fortune for a statement from them: https://archive.ph/yizHN

> When reached for comment, the NeurIPS board shared the following statement: “The usage of LLMs in papers at AI conferences is rapidly evolving, and NeurIPS is actively monitoring developments. In previous years, we piloted policies regarding the use of LLMs, and in 2025, reviewers were instructed to flag hallucinations. Regarding the findings of this specific work, we emphasize that significantly more effort is required to determine the implications. Even if 1.1% of the papers have one or more incorrect references due to the use of LLMs, the content of the papers themselves are not necessarily invalidated. For example, authors may have given an LLM a partial description of a citation and asked the LLM to produce bibtex (a formatted reference). As always, NeurIPS is committed to evolving the review and authorship process to best ensure scientific rigor and to identify ways that LLMs can be used to enhance author and reviewer capabilities.”

> the content of the papers themselves are not necessarily invalidated. For example, authors may have given an LLM a partial description of a citation and asked the LLM to produce bibtex (a formatted reference)

Maybe I'm overreacting, but this feels like an insanely biased response. They found the one potentially innocuous reason and latched onto that as a way to hand-wave the entire problem away.

Science already had a reproducibility problem, and it now has a hallucination problem. Considering the massive influence the private sector has on the both the work and the institutions themselves, the future of open science is looking bleak.

This will continue to happen as long as it is effectively unpunished. Even retracting the paper would do little good, as odds are it would not have been written if the author could not have used an LLM, so they are no worse off for having tried. Scientific publications are mostly a numbers game at this point. It is just one more example of a situation where behaving badly is much cheaper than policing bad behavior, and until incentives are changed to account for that, it will only get worse.

Why not run every submitted paper through GPTZero (before sending to reviewers) and summarily reject any paper with a hallucination?

> Even if 1.1% of the papers have one or more incorrect references due to the use of LLMs, the content of the papers themselves are not necessarily invalidated.

This statement isn’t wrong, as the rest of the paper could still be correct.

However, when I see a blatant falsification somewhere in a paper I’m immediately suspicious of everything else. Authors who take lazy shortcuts when convenient usually don’t just do it once, they do it wherever they think they can get away with it. It’s a slippery slope from letting an LLM handle citations to letting the LLM write things for you to letting the LLM interpret the data. The latter opens the door to hallucinated results and statistics, as anyone who has experimented with LLMs for data analysis will discover eventually.

I think a _single_ instance of an LLM hallucination should be enough to retract the whole paper and ban further submissions.

Kinda gives the whole game away, doesn’t it? “It doesn’t actually matter if the citations are hallucinated.”

In fairness, NeurIPS is just saying out loud what everyone already knows. Most citations in published science are useless junk: it’s either mutual back-scratching to juice h-index, or it’s the embedded and pointless practice of overcitation, like “Human beings need clean water to survive (Franz, 2002)”.

Really, hallucinated citations are just forcing a reckoning which has been overdue for a while now.