On 6 October, James Maynard was nervously checking his phone under the table throughout dinner. He’d heard rumors of a tsunami heading for the shores of math. When he checked in one last time from bed, the swell struck: OpenAI had released more than 700 math papers, generated with an advanced version of its AI model. They appeared to topple hundreds of long-standing problems, forcing researchers to face the reality that no problem in math is off-limits for machines. At first, Maynard, a mathematician at the University of Oxford and recipient of the Fields Medal, math’s most prestigious prize, feared for colleagues whose work would be upended. But then he spotted a paper that offered progress on one of his favorite pastimes: the Riemann hypothesis, a puzzle about prime numbers and one of seven famed Millennium Prize Problems, often considered the most formidable in all of math. “I was like a kid at Christmas,” Maynard says. “Even if it was some scary monster giving me a gift, I was just so excited.” The release comes less than 1 month after OpenAI announced a solution to the Navier-Stokes equations, another Millennium Prize Problem. OpenAI’s latest proof solves only a weaker version of the Riemann hypothesis. But Maynard, who made the most recent notable Riemann advance in 2024, says it is “groundbreaking” and likely to have payoffs for other problems across math. “Within analytic number theory, it’s obviously the best headline result for the past 100 years in the field.” Learn more: scim.ag/4s9F8Ip

Borrowing an idea from one research discipline to solve a thorny problem in another is an age-old scientific trick. Charles Darwin’s theory of natural selection, for example, famously inspired algorithms in computer science that “evolve” solutions to complex problems. But such clever leaps are fleetingly rare. For #AI, however, hopping between fields can be a breeze. Researchers highlighted that ability with the release of 36 preprints produced with a single suite of AI tools called BootLoops. The so-called harness can be used with any large language model, such as Anthropic’s Claude or OpenAI’s ChatGPT. And in just 3 months, researchers used Claude outfitted with BootLoops to cross-pollinate at a massive scale, spotting—and precisely solving—problems in seemingly unrelated fields that share the same mathematical structures. The resulting papers range widely, from figuring out the age of sunspots, to analyzing government health policies, to attempting to decipher a mysterious medieval manuscript. Learn more: scim.ag/4ybX3zw