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Artificial intelligence research has a slop problem, academics say
07th December, 2025 07:53 AM
A single person claims to have authored 113 academic papers on artificial intelligence this year, 89 of which will be presented this week at one of the world’s leading conferences on AI and machine learning, which has raised questions among computer scientists about the state of AI research.
The author, Kevin Zhu, recently finished a bachelor’s degree in computer science at the University of California, Berkeley, and now runs Algoverse, an AI research and mentoring company for high schoolers, many of whom are his co-authors on the papers. Zhu himself graduated from high school in 2018.
Papers he has put out in the past two years cover subjects like using AI to locate nomadic pastoralists in sub-Saharan Africa, to evaluate skin lesions, and to translate Indonesian dialects. On his LinkedIn, he touts publishing “100+ top conference papers in the past year”, which have been “cited by OpenAI, Microsoft, Google, Stanford, MIT, Oxford and more”.
Zhu’s papers are a “disaster”, said Hany Farid, a professor of computer science at Berkeley, in an interview. “I’m fairly convinced that the whole thing, top to bottom, is just vibe coding,” he said, referring to the practice of using AI to create software.
Farid called attention to Zhu’s prolific publications in a recent LinkedIn post, which provoked discussion of other, similar cases among AI researchers, who said their newly popular discipline faces a deluge of low-quality research papers, fueled by academic pressures and, in some cases, AI tools.
In response to a query from the Guardian, Zhu said that he had supervised the 131 papers, which were “team endeavors” run by his company, Algoverse. The company charges Ksh429,900 to high-school students and undergraduates for a selective 12-week online mentoring experience, which involves help submitting work to conferences.
“At a minimum, I help review methodology and experimental design in proposals, and I read and comment on full paper drafts before submission,” he said, adding that projects on subjects such as linguistics, healthcare or education involved “principal investigators or mentors with relevant expertise”.
The teams used “standard productivity tools such as reference managers, spellcheck, and sometimes language models for copy-editing or improving clarity”, he said in response to a query about whether the papers were written with AI.
Bot watchers in turmoil
The review standards for AI research differ from most other scientific fields. Most work in AI and machine learning does not go undergo the stringent peer-review processes of fields such as chemistry and biology – instead, papers are often presented less formally at major conferences such as NeurIPS, one of the world’s top machine learning and AI gatherings, where Zhu is slated to present.
Zhu’s case points to a larger issue in AI research, said Farid. Conferences, including NeurIPS, are being overwhelmed with increasing numbers of submissions: NeurIPS fielded 21,575 papers this year, up from under 10,000 in 2020. Another top AI conference, the International Conference on Learning Representations (ICLR), reported a 70 per cent increase in its yearly submissions for 2026’s conference, nearly 20,000 papers, up from just over 11,000 for the 2025 conference.
“Reviewers are complaining about the poor quality of the papers, even suspecting that some are AI-generated. Why has this academic feast lost its flavour?” asked the Chinese tech blog 36Kr in a November 2025 post about ICLR, noting that the average score reviewers had awarded papers had declined year-over-year.
Meanwhile, students and academics are facing mounting pressure to rack up publications and keep up with their peers. It is uncommon to produce a double-digit number, much less triple, of high-quality academic computer science papers in a year, academics said. Farid says that at times, his students have “vibe-coded” papers to up their publication counts.
