Papers for

mathematical data analysts

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

AI system solves open algebraic combinatorics problems and raises questions

AI Discovery in Combinatorics: From Problems to Solutions and Back to Bigger Problems

Abstract: We introduce $qt$Bench, a benchmark of 29 open problems in algebraic combinatorics. These problems ask for a simple combinatorial algorithm that explains given polynomial coefficients or identities. To help AI agents search for such algorithms, we provide a scaffold for playing with combinatorial objects, countering cheating attempts, and testing candidate solutions. To explore how far frontier AI systems can go on these problems, we collaborated with OpenAI, whose researchers obtained proposed solutions to several of them. This highlights bigger meta-problems: is AI discovery at scale ethical? How should these results be released? How should proof digestion be organized? We discuss these questions and give our views.

Mon 12 OctArtificial IntelligenceMachine Learning

The gist

Algebraic combinatorics involves finding simple rules that explain complex math formulas. The authors created a set of 29 challenging problems that ask for such simple rules. They worked with OpenAI to see how well AI could find solutions, and the AI managed to propose answers to several problems. This also led to bigger questions about how to share AI-made discoveries ethically and how to check if the proofs are correct.

Open → 2610.16555v1