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...
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.
The initiative brings together some of the world's most prestigious research institutions to pioneer the use of AI in mathematical research.
Our new method could help mathematicians leverage AI techniques to tackle long-standing challenges in mathematics, physics and engineering.
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.
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.
We built a neural theorem prover for Lean that learned to solve a variety of challenging high-school olympiad problems, including problems from the AMC12 and AIME competitions, as well as two problems adapted from the IMO.
The paper introduces the AI Mathematician (AIM) framework, which leverages Large Reasoning Models (LRMs) to tackle frontier mathematical research. AIM addresses the complexity and procedural rigor of research problems through an exploration mechanism for longer solution paths and a pessimistic reasonable verification method for reliability. Early experiments show AIM can autonomously construct significant proof components and uncover non‑trivial insights across real‑world mathematical topics.
Artificial intelligence (AI) systems have ushered in a new age of proof abundance, but helping humans understand these proofs requires new attention to the differences in how AI agents and humans purs...
arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.
arXiv:2509. 13570v2 Announce Type: replace Abstract: With the rapid rise of generative AI in higher education, understanding how students use AI is increasingly important.
The paper investigates how autonomous AI agents differ from humans in proof formalization tasks. Using a method called "simulated collaboration," the authors analyze nine major Lean collaborations involving 4.7 million definitions. They find that AI agents focus more on explicitly marked gaps, avoid densely connected proof centers and complex or surprising goals, yet are attracted to surprising proof bodies, which they tend to regularize toward a baseline.
