arXiv Bans Researchers for One Year Over Unchecked AI-Generated Content and Hallucinated References

Researchers who use hallucinated references to face arXiv ban

The physical-sciences repository arXiv is banning researchers from posting their manuscripts on the platform for one year if a submission is found to contain references that have been hallucinated by artificial-intelligence tools. The ban also applies to authors who submit manuscripts containing other "incontrovertible" signs of generative AI usage that demonstrate the AI results haven't been carefully checked. What's more, after a researcher's one-year penalty is over, they will not be able to post any manuscripts to arXiv unless the work has already been accepted at a "reputable peer-reviewed venue", according to Thomas Dietterich, a computer scientist at Oregon State University in Corvallis and chair of arXiv's computer science section. ArXiv's new policy, which has triggered a torrent of both positive and negative comments from researchers on social media, is one of the latest and most far-reaching examples of how preprint servers are grappling with the rising tide of AI 'slop' -- low-quality or meaningless content made using generative AI. Some, such as arXiv, are imposing bans on authors who do not follow their guidelines. Others have ruled out entire categories of submissions that raise concerns about generative AI use. Scientists increasingly use large language models (LLMs) for a variety of legitimate tasks, such as literature reviews, but arXiv's announcement drew approval from many researchers. "Great move and I fully support it! The only question I have is: why only AI hallucinations, folks? Let's fight the slop in general", Valeri Kremnev, co-founder of the AI startup sci2sci in Berlin, posted on social media. But not everyone is convinced that such measures are the right approach. Natalie Khalil, the founder of Reviewer 3, a platform run from in San Francisco, California, that uses AI to help researchers to conduct peer review, argues that arXiv is treating the symptom, not the root cause. "If a researcher is banned from arXiv, they will still do research, just elsewhere," she notes. In response, Dietterich says that various platforms need to work together to cull faulty references and other questionable output from LLMs. "The fact that an irresponsible researcher can publish irresponsible research elsewhere is not a justification for allowing them to post it on arXiv." In Dietterich's announcement on social media, he wrote that arXiv "can't trust anything" in a submission that contains strong evidence "that the authors did not check the results of LLM generation". This includes hallucinated references and LLM comments such as "here is a 200-word summary; would you like me to make any changes?" In an interview with Nature, Dietterich said that although arXiv had already been issuing penalties for various violations of its code of conduct, the server didn't have standardized sanctions for inappropriate generative AI use until recently. It is now publicizing the sanctions to deter such behaviour, he said, noting that the site's moderators will consider authors' appeals. Dietterich thinks that researchers put too much trust in outputs from LLMs and are not spending enough time analysing the models' results. "The trouble is, if they're not checking for these simple things, what else are they not checking for?" He also notes that some of this AI-generated content originates from paper mills -- companies that sell authorship slots and citations on manuscripts that have already been accepted for publication in journals. AI slop is most prevalent in arXiv's computer science section, which posts around half of all papers submitted to the preprint server, Dietterich says. "The authors there are the early adopters of LLM technology, and the earlier abusers of it." ArXiv's new policy is "interesting", says Brian Nosek, a social-cognitive psychologist at the University of Virginia in Charlottesville and executive director of the Center for Open Science (COS), also in Charlottesville. He notes that he has "informal evidence that a small number of people generate a substantial portion of the AI slop". In that case, a policy such as arXiv's that targets an individual's pattern of behaviour might be more effective than a policy that targets individual papers, Nosek says. Khalil, however, questions how the policy will be implemented, because hallucinated references are "not always objective evidence of fraud". For example, the metadata and link for a preliminary work can change when the work is finalised, she notes. Other preprint servers are also inundated with AI slop. The COS closed its OSF Preprints platform in October last year, attributing the move to a deluge of low-quality submissions, many of them AI slop. Psychology preprint server PsyArXiv issues permanent bans for authors who fail to disclose substantial use of generative AI, says Dermot Lynott, a psychologist at Maynooth University in Ireland, who chairs the repository's scientific advisory board. Sociology repository SocArXiv also bans authors permanently for, among other reasons, submitting AI-generated gobbledygook. "We are aware that any paper we accept will be scraped up and fed to the next generation of LLMs, and we don't want to contribute to a death spiral of nonsense and hallucination," says Philip Cohen, a sociologist and a demographer at the University of Maryland in College Park, and director of SocArXiv. The biology and medical preprint repositories bioRxiv and medRxiv have banned manuscripts that are the first submissions of a single author, because such manuscripts are often created entirely with generative AI. But Richard Sever, who co-founded bioRxiv and medRxiv and is head of openRxiv in New York City, which operates the two preprint servers, says the databases have not yet considered imposing bans on authors whose submissions include hallucinated references. Instead, managers are exploring technologies to help spot such references. "I am more concerned by AI slop with legitimate references, which may be harder to detect," he says. Dietterich thinks that generative AI will eventually stop producing hallucinated references, making it harder to distinguish the papers it produces from genuine research. "At some point," he says, "we might need ways that authors can prove that they actually carried out the experiments that they're reporting."

Research repository ArXiv will ban authors for a year if they let AI do all the work | TechCrunch

ArXiv, a widely used open repository for preprint research, is doing more to crack down on the careless use of large language models in scientific papers. Although papers are posted to the site before they are peer-reviewed, arXiv (pronounced "archive") has become one of the main ways that research circulates in fields like computer science and math, and the site itself has become a source of data on trends in scientific research. ArXiv has already taken steps to combat a growing number of low-quality, AI-generated papers, for example by requiring first-time posters to get an endorsement from an established author. And after being hosted by Cornell for more than 20 years, the organization is becoming an independent nonprofit, which should allow it to raise more money to address issues like AI slop. In its latest move, Thomas Dietterich -- the chair of arXiv's computer science section -- posted Thursday that "if a submission contains incontrovertible evidence that the authors did not check the results of LLM generation, this means we can't trust anything in the paper." That incontrovertible evidence could include things like "hallucinated references" and comments to or from the LLM, Dietterich said. If such evidence is found, a paper's authors will face "a 1-year ban from arXiv followed by the requirement that subsequent arXiv submissions must first be accepted by a reputable peer-reviewed venue." Note that this isn't an outright prohibition on using LLMs, but rather an insistence that, as Dietterich put it, authors take "full responsibility" for the content, "irrespective of how the contents are generated." So if researchers copy-paste "inappropriate language, plagiarized content, biased content, errors, mistakes, incorrect references, or misleading content" directly from an LLM, then they're still responsible for it. Dietterich told 404 Media that this will be a "one-strike" rule, but moderators must flag the issue and section chairs must confirm the evidence before imposing the penalty. Authors will also be able to appeal the decision. Recent peer-reviewed research has found that fabricated citations are on the rise in biomedical research, likely due to LLMs -- though to be fair, scientists aren't the only ones getting caught using citations that were made up by AI.

ArXiv will ban researchers who upload papers full of AI slop

Last year, ArXiv also updated its policies to reduce AI slop by only allowing computer science review articles and position papers to be published if they have been peer reviewed and have been accepted at a conference or a journal. "The advent of large language models have made this type of content relatively easy to churn out on demand, and the majority of the review articles we receive are little more than annotated bibliographies, with no substantial discussion of open research issues," ArXiv said at the time.

Major Scientific Repository arXiv Cracks Down on AI-Generated Papers

If the platform finds 'incontrovertible evidence' that a submission's large language model (LLM) used wasn't checked, they could face a one-year ban, as well as long-term scrutiny of what they submit. ArXiv.org, one of the world's most popular repositories of free scientific research, has cracked down on AI-generated content in a recent shake-up to its rules. Thomas G. Dietterich, current chair of the Computer Science Section of arXiv, clarified the penalties that scientists using generative AI improperly could face. In future, if "a submission contains incontrovertible evidence that the authors did not check the results of LLM generation, this means we can't trust anything in the paper," said Dietterich. "The penalty is a 1-year ban from arXiv followed by the requirement that subsequent arXiv submissions must first be accepted at a reputable peer-reviewed venue." Dietterich, who is a professor at Oregon State University, said examples could include "hallucinated references," as well as what he called "meta-comments from the LLM." For example, this could be a section that reads: "Here is a 200-word summary; would you like me to make any changes?" "Our Code of Conduct states that by signing your name as an author of a paper, each author takes full responsibility for all its contents, irrespective of how the contents were generated," he clarified. AI-generated content isn't just a big problem in your social media newsfeed; it's now a major issue in the world of serious academia. In the run-up to one of the the world's most popular AI conferences, the 2026 International Conference on Learning Representations (ICLR), 21% of ICLR peer reviews were allegedly fully AI-generated, and more than half showed signs of AI use. The issue was less extreme for the papers themselves, but still very serious. 199 manuscripts, or 1%, were fully AI-generated, while 9% contained more than 50% AI-generated text. Reactions on social media were broadly positive. Lucas Beyer, a Meta researcher formerly of UK AI firm DeepMind, dubbed the move "very good" and called for the restrictions to be "strongly enforced." Enforcing these types of measures could be a big lift as ArXiv.org deals with a huge amount of content. According to its own statistics, it reached two million submissions by the end of 2021, with roughly 24,000 articles being submitted monthly as of November 2024. Dietterich did later clarify that appeals will be possible if bans are issued, telling 404 Media that the rules are only set to apply in cases with "incontrovertible evidence," with the support of both a moderator and a section chair from the platform.

A key science publishing platform is cracking down on AI slop

The pre-print website arXiv has announced that researchers who put their names to papers which included errors clearly generated by artificial intelligence (AI) will face a year-long ban and ongoing restrictions. The move is a response to a growing influx of AI-generated papers faced by scholarly journals as well as sites such as arXiv, which serve as unofficial platforms for research publication ahead of peer review. However, not everyone agrees that arXiv's response to the problem is appropriate - and the solution to the flood of AI slop research may involve more AI, not less. The rise of bot-assisted writing AI-generated text is on the rise everywhere. A study released last week suggests half of new articles published online are now "primarily AI-generated". Science is not immune to this trend. Last month, the journal Organization Science published a study of how the rise of AI has affected submissions and peer reviews since the release of ChatGPT in 2022. Reporting a dramatic rise in submitted papers and a drop in quality, the authors conclude that "the current state of AI tools, amplified by existing publish-or-perish incentives, appears to be pushing the system toward an equilibrium of more rather than better research". A common problem in AI-generated research writing is hallucinated citations: references to other research that does not exist. The traditional safeguard against poor quality in scholarly publishing is peer review: another expert in the subject at hand reads the research paper and interrogates the work behind it before it can be published. However, the peer review system was already struggling before AI. Pressured researchers often have little time or incentive to do the unpaid work of peer review. And on arXiv, which publishes preprints - articles which have most often not been peer-reviewed - even this system is not available. Last year, flooded with AI-generated submissions, the site stopped accepting certain types of article. A study published in January (itself a preprint) estimated around 1 in 8 papers in biomedical science now contain AI-generated text. Most researchers would agree that AI-generated text is not a problem in itself. The problem is the lower-quality work that AI can make easy to produce. Does the punishment fit the crime? The ArXiv announcement doesn't come out against AI use, but rather says If a submission contains incontrovertible evidence that the authors did not check the results of LLM generation, this means we can't trust anything in the paper. This may be true as far as it goes. But the penalty - a year-long ban for all authors listed on a paper - may be out of keeping with current research practices. In the past, research was often carried out by people working alone or in groups of two or three. In these circumstances, it seems reasonable to expect each author to take responsibility for the whole. But research is now more collaborative than ever before. Many papers have four or five authors, and in a growing number of extreme cases papers may be credited to groups of hundreds of scientists working together, each working on their own speciality and trusting their colleagues to be doing the same. In a case where one author of dozens or hundreds included an AI-hallucinated reference in their part of the paper, banning the lot seems harsh. And there are no equivalent sanctions for publishing other problematic material. There's no ban for pushing fringe or discredited theories, or using poor quality evidence and illogical arguments, for example. Can AI help fight slop? The rise of AI produces problems for publishers and quality assurance. And the idea of some kind of sanctions for reckless use of AI, such as included hallucinated references, is a good one. But ArXiv's particular choice seems drastic. If the goal is to improve peer review and quality assurance, AI systems themselves can play a role. Modern AI systems are quite capable of taking a list of references and checking everything on it is a real paper available on the internet. Any references flagged as suspect can then be checked by a human. AI can even be useful for carrying out quick sense-checks of things like a paper's statistical analysis. Perhaps this is the way forward, rather than harsh sanctions for relatively minor AI-related infractions.

ArXiv will ban researchers for a year if they submit papers with AI slop

Serving tech enthusiasts for over 25 years. TechSpot means tech analysis and advice you can trust. Science Slop: Large language models and chatbots are growing in popularity for both recreational use and, in some cases, serious research projects. However, one of the internet's most popular research platforms is looking to curb misuse by users who rely on low-quality chatbot prompts without properly verifying the final output. The arXiv (pronounced "archive") team recently announced a significant update to its official code of conduct. The popular open-access repository of research papers awaiting peer review will now seek to deter AI-generated "slop" by enforcing stricter accountability rules, including a one-year ban for violations. The team said that using LLMs and generative AI tools is generally acceptable, but only if the final output is carefully reviewed and corrected to remove hallucinated or inaccurate content. Thomas G. Dietterich, a distinguished professor at Oregon State University and member of arXiv's moderation team, recently outlined the changes on social media. He explained that the updated code of conduct "states that by signing your name as an author of a paper, each author takes full responsibility for all its contents, irrespective of how the contents were generated." Dietterich emphasized the implications for generative AI tools in particular. If authors use LLMs or chatbots to draft or enhance a paper, they must proofread the final version for inappropriate language, plagiarism, bias, and other unacceptable content. Ultimately, each author is responsible for any errors or hallucinations produced by AI tools. After confirming the presence of clear evidence that a paper contains AI-generated content that was not properly removed prior to submission, arXiv's moderation team will impose a one-year ban from the platform. According to Dietterich, if a paper contains such AI-generated material, arXiv may question the reliability of the work as a whole. In addition, a banned author will be required to publish through a reputable peer-reviewed venue before being allowed to return to arXiv. Dietterich provided examples of AI-generated artifacts that could trigger a ban, including LLM "meta-comments" such as "here is a summary," "would you like to make any changes?", and similar boilerplate text. He also explained that the "one-strike" ban policy will go through a formal moderation process. Moderators must first flag and verify evidence of AI-generated content before any penalty is imposed. Authors will also have the opportunity to appeal decisions before a ban is finalized. arXiv is a free open-access repository hosting more than 2.4 million papers across physics, mathematics, computer science, biology, statistics, engineering, and other fields. In a landscape increasingly shaped by AI-generated content, the platform remains a critical hub for early-stage research dissemination. It remains to be seen whether the new code of conduct will be sufficient to deter misuse of generative AI and reduce low-quality submissions that could undermine the integrity of open-access research platforms.

ArXiv introduces one-year ban for researchers who submit papers with unchecked AI-generated content

ArXiv, the open-access repository that has served as the primary distribution channel for preprint research in computer science, mathematics, and physics for more than three decades, will ban authors for one year if they submit papers containing obvious signs of unchecked AI generation. Thomas Dietterich, chair of arXiv's computer science section, announced the policy on Thursday, writing that submissions with "incontrovertible evidence" of unvetted large language model output mean "we can't trust anything in the paper." The rule is not a blanket prohibition on using AI tools. Researchers can still use language models for drafting, editing, or analysis. What triggers the penalty is evidence that an author pasted LLM output into a paper without checking it, the kind of carelessness that produces hallucinated references, placeholder instructions from the chatbot, or fabricated data tables with notes reading "fill in with the real numbers from your experiments." If moderators find such evidence and a section chair confirms it, the author faces a one-year ban from arXiv, after which all subsequent submissions must first be accepted by a peer-reviewed journal before they can appear on the platform. ArXiv is not a journal. It does not peer-review papers. But it has become the de facto way that research circulates in several of the fastest-moving fields in science, particularly machine learning and artificial intelligence. Papers posted to arXiv are read, cited, and built upon long before they appear in formal publications, if they ever do. That makes the platform's quality standards unusually consequential: a hallucinated citation on arXiv can propagate through the research literature just as effectively as one in a peer-reviewed journal, and often faster. The scale of the problem is significant. A study published in The Lancet in May 2026 by researchers at Columbia University audited 2.5 million biomedical papers and 126 million references indexed on PubMed Central. It found that fabricated citations have risen twelvefold since 2023. In that year, roughly one in 2,828 papers contained at least one fake reference. By 2025, the rate had climbed to one in 458. In the first seven weeks of 2026, it was one in 277. The researchers attributed the surge to the proliferation of AI writing tools, noting that previous studies estimate 30 to 69 per cent of LLM-generated references in biomedical contexts are fabricated. ArXiv has reason to take the threat seriously. The platform receives thousands of submissions each month, and its volunteer moderation system was not designed to screen for machine-generated content at scale. Dietterich's announcement described the new penalty as a "one-strike" rule, though decisions are subject to appeal and require confirmation by a section chair before being imposed. The policy is deliberately narrow in what it targets. Dietterich listed specific examples of "incontrovertible evidence": hallucinated references that do not correspond to any real publication, meta-comments from the language model left in the text (such as "here is a 200-word summary; would you like me to make any changes?"), and placeholder data with instructions to the author that were never removed. These are not subtle quality failures. They are signs that the author did not read the paper before submitting it. The distinction matters because it avoids the far more difficult question of whether AI-assisted writing should be permitted at all. ArXiv's existing policy already states that authors bear "full responsibility" for their content "irrespective of how the contents are generated." The new penalty enforces that principle by targeting the most egregious violations, cases where the author's failure to exercise any oversight is provable from the text itself. That approach has practical advantages. Detecting whether a well-edited paper was drafted with the help of an LLM is unreliable with current detection tools, and attempting to enforce a broader ban would be both technically difficult and potentially punitive toward researchers who use AI tools responsibly. By focusing on obvious slop, arXiv can enforce the rule without needing to build or buy an AI-detection system, a technology that remains prone to its own errors. ArXiv is not the only institution struggling with the issue. Academic conferences in computer science, including NeurIPS and ICML, have reported surges in submissions that appear to be generated with minimal human oversight. Nature published a feature in late 2025 describing how AI slop is creating a crisis in computer science, where the volume of low-quality submissions is overwhelming reviewers and diluting the signal-to-noise ratio of the field's output. Peer-reviewed journals face the same problem. The Lancet study found that fabricated citations appeared in papers that had already passed peer review, suggesting that reviewers are either not checking references or are unable to identify fabrications at the rate they are now appearing. Lead author Maxim Topaz, of Columbia University's School of Nursing, warned that clinicians and guideline developers have no way of knowing when the evidence they rely on does not exist, a gap that efforts to reduce AI hallucinations in scientific research have not yet closed. ArXiv itself is undergoing structural changes that may help it address the challenge. After more than 20 years as a project hosted by Cornell University, the platform is becoming an independent nonprofit, a move that should give it greater autonomy over its moderation policies and the ability to raise funds specifically to combat quality problems. It has also introduced a requirement for first-time submitters to obtain an endorsement from an established author, a gatekeeping measure aimed at reducing the volume of submissions from accounts created solely to publish AI-generated material. The new rule will catch the most careless offenders, researchers who submit papers they have not read. It will not catch researchers who use language models to generate plausible but incorrect claims, fabricate data, or produce papers that are fluent but scientifically vacuous. Those problems require peer review, institutional oversight, and a willingness within the research community to treat AI-assisted misconduct with the same seriousness as traditional forms of fabrication. What arXiv's policy does establish is a principle: if you submit a paper, you are responsible for every word in it. That has always been true in theory. The difference now is that language models have made it trivially easy to produce text that reads like science but contains nothing of substance. ArXiv's one-year ban is a modest penalty for a serious offence, but it is also the first formal acknowledgement by a major research platform that the problem is no longer one of occasional carelessness. It is structural, it is growing, and it requires dedicated infrastructure to combat.

ArXiv to Ban Researchers for a Year if They Submit AI Slop

The change comes as arXiv and others struggle to manage an influx of AI-generated materials masquerading as rigorous science. ArXiv, the open-access repository of preprint academic research, will ban authors of papers for a year if they submit obviously AI-generated work. Late Thursday evening, Thomas Dietterich, chair of the computer science section of ArXiv, wrote on X: "If generative AI tools generate inappropriate language, plagiarized content, biased content, errors, mistakes, incorrect references, or misleading content, and that output is included in scientific works, it is the responsibility of the author(s). We have recently clarified our penalties for this. If a submission contains incontrovertible evidence that the authors did not check the results of LLM generation, this means we can't trust anything in the paper." Examples of incontrovertible evidence, he wrote, include "hallucinated references, meta-comments from the LLM ('here is a 200 word summary; would you like me to make any changes?'; 'the data in this table is illustrative, fill it in with the real numbers from your experiments'." "The penalty is a 1-year ban from arXiv followed by the requirement that subsequent arXiv submissions must first be accepted at a reputable peer-reviewed venue," Dietterich wrote. Dietterich told me in an email on Friday morning that this is a one-strike rule -- meaning authors caught just once including AI slop in submissions will be banned -- but that decisions will be open to appeal. "I want to emphasize that we only apply this to cases of incontrovertible evidence," he said. "I should also add that our internal process requires first a moderator to document the problem and then for the Section Chair to confirm before imposing the penalty." In November 2025, arXiv announced it would no longer accept computer science review articles and position papers because it was being "flooded" with AI slop. "Generative AI/large language models have added to this flood by making papers -- especially papers not introducing new research results -- fast and easy to write. While categories across arXiv have all seen a major increase in submissions, it's particularly pronounced in arXiv's CS category," arXiv wrote in a press release about the change at the time. And in January, it announced first-time submitters would need an endorsement from an established author due to a rise in fraudulent submissions. AI-generated, fabricated citations are a huge problem in research. A recent study by Columbia University researchers examined 2.5 million biomedical papers across three years, and found that one in 277 papers published in the first seven weeks of 2026 contained fabricated references; In 2023, it was one in 2,828, and in 2025, one in 458. AI-generated citations and papers are already straining the peer-review process, and more and more papers are making it through the pipeline with those meta-comments and hallucinated data intact. ArXiv is managed by Cornell Tech, but this July, it will become an independent nonprofit corporation. Greg Morrisett, dean and vice provost of Cornell Tech, told Science.org that this change will help arXiv raise more money from a wider range of donors, which Morrisett said is needed to deal with the emergence of "AI slop."

A popular academic journal is coming down hard on AI-generated submissions

We're still in the early stages of the AI revolution, but there's already plenty of evidence that it won't be purely a blessing. Generative AI has made writing exponentially faster, if not necessarily better, and the result has been a massive increase in submissions of novels, newspaper pieces, and even academic journals, with one publication even warning of a coming "swamp of slop." Now, however, the journals are fighting back. ArXiv, one of the largest open-access repositories of preprint academic research, is issuing a one-year ban on all authors who submit "obviously AI-generated work," according to 404media. Moreover, if the offending author wishes to return to the good graces of ArXiv, they will have to first submit to a "reputable peer-reviewed review venue," according to Thomas Dietterich, chair of the publication's computer science division. He recently took to X to not only clarify the new rules but also place the onus on authors to use LLMs responsibly: "If generative AI tools generate inappropriate language, plagiarized content, biased content, errors, mistakes, incorrect references, or misleading content, and that output is included in scientific works, it is the responsibility of the author(s). We have recently clarified our penalties for this. If a submission contains incontrovertible evidence that the authors did not check the results of LLM generation, this means we can't trust anything in the paper." Faulty, misleading references, plagiarism, and invented citations are not the only issues with AI; however, there are others. In November of 2025, ArXiv was forced to shut down its entire computer science review section due to the overwhelming volume of AI-generated submissions, most of which did not even introduce new research results, according to a press statement. A funny, counterintuitive consequence of AI-enabled hyperefficiency is the evaluation bottleneck. If, in any given month, there are 100 academic papers submitted for review, it's not too hard to find and publish the best work, but if there are a thousand submissions, even the best-funded journals can't keep up. Expect the backlash to grow even fiercer as the power of AI increases and the costs of using it decrease.

ArXiv Warns Researchers Against Submitting Papers With Unchecked AI Content

ArXiv has moved to tighten its rules on careless use of large language models in research papers. The open-access preprint platform said authors must check all content before submitting work, even when they during writing or research. The policy does not block researchers from using large language models. However, it makes authors fully responsible for errors, fake citations, copied text, or unchecked AI output. The move comes as research platforms face rising pressure to control low-quality papers linked to automated writing tools.