Advancing geometry with AI: Multi-agent generation of polytopes

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Authors: Grzegorz Świrszcz, Ascher Wagner, Geordie Williamson, Sam Blackwell, Bogdan Georgiev, Alex Davies, Ali Eslami, Sébastien Racanière, Théophane Weber, Pushmeet Kohli
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Abstract

Polytopes are one of the most primitive concepts underlying geometry. They are defined as intersections of a finite number of half-spaces, i.e. the subsets of Euclidean spaces which can be defined by a finite set of linear inequalities. Discovery and study of polytopes with complex structures provides a means of advancing scientific knowledge. Construction of polytopes with specific extremal structure is very difficult and time-consuming. Having an automated tool for the generation of such extremal examples is therefore of great value. We present an Artificial Intelligence system capable of generating novel polytopes with very high complexity, whose abilities we demonstrate in three different and challenging scenarios: the Hirsch Conjecture, the k-neighbourly problem and the longest monotone paths problem. For each of these three problems the system was able to generate novel examples, which match or surpass the best previously known bounds. Our main focus was the Hirsch Conjecture, which had remained an open problem for over 50 years. The highly parallel A.I. system presented in this paper was able to generate millions of examples, with many of them surpassing best known previous results and possessing properties not present in the earlier human-constructed examples. For comparison, it took top human experts over 50 years to handcraft the first example of a polytope exceeding the bound conjectured by Hirsch, and in the decade since humans were able to construct only a scarce few families of such counterexample polytopes. With the adoption of computer-aided methods, the creation of new examples of mathematical objects stops being a domain reserved only for human brilliance. Advances in A.I. provide mathematicians with yet another powerful tool in advancing mathematical knowledge. The results presented demonstrate that A.I. is capable of addressing problems in geometry recognized as extremely hard, and also to produce extremal examples different in nature from the ones constructed by humans.

Fields of Study & Subjects

Computational Geometry and Mesh Generation Advanced Numerical Analysis Techniques Manufacturing Process and Optimization

Publication details

Journal / source
Advances in Theoretical and Mathematical Physics
Publisher
International Press of Boston
Volume
30
Issue
1
Pages
41–63
Publication date
1 January 2026
Language
EN