Fifty-four seconds. That’s how long it took Raphael Wimmer to write up an experiment that he did not actually perform, using a new artificial-intelligence tool called Prism, released by OpenAI last month. “Writing a paper has never been easier. Clogging the scientific publishing pipeline has never been easier,” wrote Wimmer, a researcher in human–computer action at the University of Regensburg in Germany, on Bluesky.
Large language models (LLMs) can suggest hypotheses, write code and draft papers, and AI agents are automating parts of the research process. Although this can accelerate science, it also makes it easy to create fake or low-quality papers, known as AI slop.
Computer science was a growing field before the advent of LLMs, but it is now at breaking point. The 2026 International Conference on Machine Learning (ICML) has received more than 24,000 submissions — more than double that of the 2025 meeting. One reason for the boom is that LLM adoption has increased researcher productivity, by as much as 89.3%, according to research published in Science in December1.
“It’s a volume far beyond what the current review system was designed to handle,” and makes “thorough and careful evaluation increasingly infeasible”, says Seulki Lee, a computer scientist at the Korea Advanced Institute of Science and Technology in Daejeon, South Korea.
Volume is not the only problem. Many authors fail to properly validate or verify AI-generated contents, says Lee. Analyses of submissions to prominent AI conferences show that some papers are entirely AI-generated and dozens contain AI fabrications, known as hallucinations. Since the advent of ChatGPT in November 2022, the number of monthly submissions to the arXiv preprint repository has risen by more than 50% and the number of articles rejected each month has risen fivefold to more than 2,400 (see ‘Rejection rates climb’).
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