AI companies must work with the research community to protect attribution

Nature

September 16, 2026

Editorial (No author named)

"Last Tuesday is likely to go down as the start of an epoch in the history of mathematics. On 8 September, an artificial-intelligence firm announced it had solved one of the Millennium Prize Problems — among the hardest, best known and most important in maths. The problem relates to the Navier–Stokes equations, 200-year-old differential equations that describe how fluids behave. The firm, OpenAI in San Francisco, California, says the solution shows the equations can break down under certain conditions, so are not reliable for real-world fluids.

Yet the circumstances of the claimed breakthrough and its communication in a press release rather spoiled the celebrations. OpenAI said that its breakthrough, which cost several million US dollars, has been validated using an automated verification method that is becoming the standard for rigour in mathematics. However, there are renewed concerns among mathematicians around how AI models learn from interacting with their users — and whether the models, together with the developers and researchers using them, are giving due credit to previous work.

Information to verify those concerns has not been published, but last week’s events should be a wake-up call for researchers and institutions. The integrity of the scientific process is at risk if studies from researchers in and outside tech companies are not appropriately crediting those who have been helping to make AI models so smart..."

Researchers presented 6000 papers at a major meeting. Could AI reproduce their findings?

Science

August 25, 2026

Celia Zhao

"By his own admission, Jansen Tang is not an artificial intelligence (AI) researcher. Instead, he manages cybersecurity risk at a bank.

This month, however, Tang won a competition that challenged participants to use AI to reproduce results from thousands of papers presented at one of the world’s top computer science conferences, the International Conference on Machine Learning (ICML). In just 19 days, he reproduced a whopping 363 cutting-edge papers, the most among all 1200-plus contestants. The key to his success? His team of AI agents—AI models designed to autonomously perform certain tasks—that he calls his “agentic minions on KRACK (Knowledge, Reasoning, Agency & Coherence Kernel).”

The hackathon, organized by the computing and research community platforms Hugging Face and alphaXiv, posed an ambitious question: Can AI agents make it possible to check scientific work at a scale that’s increasingly impossible for human reviewers? In other words, if provided a paper’s data, code, and methods, can AI independently reproduce the experiments and reach the same results?..."