Hallucinated citations are polluting the scientific literature. What can be done?

Tens of thousands of publications from 2025 might include invalid references generated by AI, a Nature analysis suggests.

Hallucinated citations are polluting the scientific literature. What can be done?

Tens of thousands of publications from 2025 might include invalid references generated by AI, a Nature analysis suggests.

Hallucinated citations are polluting the scientific literature. What can be done?

Tens of thousands of publications from 2025 might include invalid references generated by AI, a Nature analysis suggests.

Researchers who use hallucinated references to face arXiv ban

The preprint server is the latest to impose stiff penalties on authors who contribute to AI ‘slop’ — but not everyone is convinced it’s the right approach.

Fraudulent citations, blamed on AI hallucinations, are becoming more common in research papers

“Fabricated” citations that do not reference real academic papers are spreading in the literature, polluting the public record of science, a new study found

Thomas G. Dietterich on Twitter / X

We are seeing a new trend in submissions to @arxiv (and presumably to conferences and journals): Authors submitting papers whose contents they likely do not understand. 1/— Thomas G. Dietterich (@tdietterich) September 13, 2026

Preprint site arXiv is banning computer-science reviews: here’s why

Nature - The repository is taking steps to tackle a surge in low quality, AI-generated content.

Fair Moderation, Equitable Access, and AI: arXiv’s Updated Rate Limit Policy

arXiv, and the scientific community at large, are facing a watershed moment. Scholarly publishing is currently changing at a rapid pace, and we are seeing a…

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.

Citing Less Critically: LLMs Reshape the Rhetoric and Reach of Scientific Citation

Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentioning). As large language models (LLMs) increasingly assist scientific writing, whether they reproduce citations with the same rhetorical intent as humans remains unclear. We introduce a masked-citation task to compare human and LLM-generated citation behavior. For each citation context, an LLM generates a replacement citation sentence, producing a counterfactual corpus directly comparable to human citation. We analyze what, whom, and how models cite, using an LLM-as-a-judge to classify citation intent and a 20-million-edge coauthorship network to measure social distance between cited authors. Across six popular LLMs and 1,746 top NLP conference papers (63k+ contexts, 132k+ citations), three patterns emerge: (1) Compared with human citation, LLMs cite significantly less critically; (2) LLMs over-cite popular and older papers, a tendency amplified for contrasting citations where human writing more often draws on recent, niche work; (3) Whereas humans often cite within their close social network, especially for supporting citations, LLMs tend to draw on more socially distant authors. Together, these differences are double-edged: LLM citation reaches beyond a scholar's close collaborators while being less critical and amplifying visibility bias, reshaping the rhetoric and reach of scientific citation.

Mikel Artetxe on Twitter / X

3/4 papers I reviewed and 2/3 reviews I got this ARR cycle were AI slop. Fraudsters are already outnumbering honest researchers. Unless drastic measures are urgently adopted, conference publications will have zero value or even become negative signal, and academia will be toast.— Mikel Artetxe (@artetxem) September 19, 2026

HalluCiteChecker: A Lightweight Toolkit for Hallucinated Citation Detection and Verification in the Era of AI Scientists

We introduce HalluCiteChecker, a toolkit for detecting and verifying hallucinated citations in scientific papers. While AI assistant technologies have transformed the academic writing process, including citation recommendation, they have also led to the emergence of hallucinated citations that do not correspond to any existing work. Such citations not only undermine the credibility of scientific papers but also impose an additional burden on reviewers and authors, who must manually verify their validity during the review process. In this study, we formalize hallucinated citation detection as an NLP task and provide a corresponding toolkit as a practical foundation for addressing this problem. Our package is lightweight and can perform verification in seconds on a standard laptop. It can also be executed entirely offline and runs efficiently using only CPUs. We hope that HalluCiteChecker will help reduce reviewer workload and support organizers by enabling systematic pre-review and publication checks. Our code is released under the Apache 2.0 license on GitHub and is distributed as an installable package via PyPI. A demonstration video is available on YouTube.

Ali Sina Önder on Twitter / X

Does your social media visibility affect your citations? Yes because social media visibility enhances your "expert" status. Here is the brand new paper with @econ_lessmann and Max Rose: https://t.co/KJaxgOaAYQ @davidstadelmann @MishaTeplitskiy @csugimoto @voxeu @AntonioFatas pic.twitter.com/kioHRJ0gYH— Ali Sina Önder (@asonder79) May 3, 2026

Q&A from the slop trenches – GeoSpatial ML

We reviewed 22 ML conference submissions this summer. Fifteen had fabricated citations, hallucinated authors, or clear LLM slop, so we complain about that a bit, and release the paper references audit we now run.