Even with AI, Bijection Discovery is Still Hard: The Opportunities and Challenges of OpenEvolve for Novel Bijection Construction
Davis Brown, Jesse He, Helen Jenne, Henry Kvinge, and Max Vargas

TL;DR
This paper investigates the use of OpenEvolve, an AI-assisted program synthesis system, for discovering combinatorial bijections, highlighting its potential and current limitations in aiding mathematical research.
Contribution
The paper demonstrates the application of OpenEvolve to bijection problems, including an open problem, and discusses lessons learned about its capabilities and challenges.
Findings
OpenEvolve shows promise for combinatorial problem solving.
Current systems struggle with discovering novel, research-level bijections.
Human mathematicians remain essential in complex bijection discovery.
Abstract
Evolutionary program synthesis systems such as AlphaEvolve, OpenEvolve, and ShinkaEvolve offer a new approach to AI-assisted mathematical discovery. These systems utilize teams of large language models (LLMs) to generate candidate solutions to a problem as human readable code. These candidate solutions are then 'evolved' with the goal of improving them beyond what an LLM can produce in a single shot. While existing mathematical applications have mostly focused on problems of establishing bounds (e.g., sphere packing), the program synthesis approach is well suited to any problem where the solution takes the form of an explicit construction. With this in mind, in this paper we explore the use of OpenEvolve for combinatorial bijection discovery. We describe the results of applying OpenEvolve to three bijection construction problems involving Dyck paths, two of which are known and one of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEvolutionary Algorithms and Applications · Logic, programming, and type systems · Machine Learning in Materials Science
