The core paper is here [1], but the SciAm article says:
"...around the 14th century, Indian mathematician Madhava of Sangamagrama provided the first exact formula, expressed as an infinite series—a sum of endlessly many terms that, if you could somehow add them all up, would yield pi exactly. The catch: his series converged agonizingly slowly, requiring hundreds of terms just to nail down a few decimal places. More than three hundred years later Leonhard Euler discovered another series that converged faster. And in the early 1900s, the mathematician Srinivasa Ramanujan produced formulas that are still revered for their efficiency today. ... Each equation seemed unrelated to the others. But in late 2025, a team of seven AI researchers at the Technion–Israel Institute of Technology found a previously unknown mathematical structure underlying hundreds of pi formulas, including those of Archimedes, Euler and Ramanujan. “It’s not every day that you get to cite Archimedes,” says Ph.D. student Michael Shalyt, part of the team. The structure, called a conservative matrix field, or CMF, acts as a kind of mathematical common ancestor, showing how formulas that look nothing alike turn out to be different expressions of the same underlying object."
From the abstract:
"Our system combines large language models (LLMs) for systematic formula harvesting, an LLM-code feedback loop for validation, and a novel symbolic algorithm for clustering andeventual unification. We demonstrate this methodology on the hallmark case of π, an ideal testing ground for symbolic unification. Applying this approach to 455,050 arXiv papers, we validate 385 distinct formulas for π and prove relations between 360 (94%) of them, of which 166 (43%) can be derived from a single mathematical object—linking canonical formulas by Euler, Gauss, Brouncker, and newer ones from algorithmic discoveries by the Ramanujan Machine. Our method generalizes to other constants, including e, ζ(3), and Catalan’s constant, demonstrating the potential of AI-assisted mathematics to uncover hidden structures and unify knowledge across domains"
The whole paper is waay over my head, but I find it elegant that there is a simpler underlying structure.
--- [1] https://arxiv.org/pdf/2502.17533