What it is
Large language models (LLMs) increasingly excel at mathematics tasks, but their unreliability limits their use in mathematics research. One mitigation is to have LLMs generate formal proofs in languages such as Lean, in which a compiler verifies every step of a proof. The authors present what they describe as the first demonstration of this method's value in solving open problems at scale. They built an artificial intelligence agent for formal proof search that autonomously resolved 9 of 353 open Erdős problems (from a collection of problems posed by the mathematician Paul Erdős) and proved 44 of 492 conjectures from the On-Line Encyclopedia of Integer Sequences (OEIS), a database of number sequences. The agent is being deployed in research in combinatorics, optimization, graph theory, algebraic geometry and quantum optics.
Why it matters
An AI agent that writes formal proofs, which a compiler checks step by step, autonomously resolved 9 of 353 open Erdős problems and proved 44 of 492 conjectures from the OEIS. Language models are increasingly good at mathematics tasks but their unreliability limits their use in research; formal proof search is a mitigation, and the authors call this the first demonstration of its value in solving open problems at scale. Even a basic agent that alternated generation by a language model with verification in Lean replicated the Erdős successes, and the authors conclude that formal proof search is a powerful enabler of autonomous mathematical discovery.
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Filed underMathematics, Computing, and Information Processing, Artificial Intelligence Applications, Logic, programming, and type systems