arXiv AI
What is mathematics now, and what should it be?
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
Hugging Face Trending Papers
Aug 24
What is mathematics now, and what should it be?
Advances in neural theorem provers have been impressive, but the successes obscure a broader vision of what AI can do for mathematics and how mathematicians can engage with AI. This essay advances a m...
arXiv AI
Oct 9
Machine learning and information theory concepts towards an AI Mathematician
The paper argues that current AI excels at intuitive, system‑1 tasks but falls short on system‑2 reasoning needed for mathematics. It proposes using information theory to define what makes a mathematical statement interesting, aiming to guide the creation of an AI mathematician that can generate novel conjectures rather than merely prove existing theorems. The central hypothesis is that a concise, descriptive set of theorems can efficiently capture the space of all provable statements by minimizing description length while remaining close to many derivable results.
DeepMind Blog
Oct 29, 2025
Accelerating discovery with the AI for Math Initiative
The initiative brings together some of the world's most prestigious research institutions to pioneer the use of AI in mathematical research.
DeepMind Blog
Oct 24, 2025
Discovering new solutions to century-old problems in fluid dynamics
Our new method could help mathematicians leverage AI techniques to tackle long-standing challenges in mathematics, physics and engineering.
arXiv AI
Jul 10
From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier
arXiv:2607. 07779v1 Announce Type: cross Abstract: Recent developments in AI for Mathematics (AI4Math), especially Large Language Model (LLM)-driven theorem provers, has achieved remarkable success in formal proof generation for well-defined mathematical problems through Interactive Theorem Proving (ITP) languages.
arXiv AI
Sep 15
Math for AI safety: an invitation for mathematicians
The article "Math for AI safety: an invitation for mathematicians" calls for new mathematical tools to ensure AI remains understandable, controllable, and cooperative. It outlines specific mathematical fields—logic, game theory, probability, algebra, representation theory, analysis, and geometry—each paired with an open problem tailored for mathematicians without AI safety background. The piece invites researchers to contribute to designing AI that is legible, steerable, and aligned with human values.