🤖 AI Summary
This study addresses the lack of reliable evaluation of artificial intelligence systems on genuine mathematical research, despite their strong performance on Olympiad-style problems. To bridge this gap, the authors introduce a novel private benchmark comprising 25 expert-level problems, meticulously designed by leading mathematicians and validated through double-blind review, emphasizing deep theoretical understanding over procedural problem-solving tricks. Evaluation employs a programmatic verifier to check unique closed-form solutions and an unbiased statistical estimator based on 100 independent runs per problem, permitting models to leverage coding, search, and open-ended reasoning. Results reveal that all state-of-the-art models achieve success rates below 10%, underscoring a substantial disparity between current AI capabilities and human expertise in research-grade mathematical reasoning.
📝 Abstract
Recent AI systems have achieved gold-medal-level performance on the International Mathematical Olympiad, demonstrating remarkable proficiency at competition-style problem solving. However, competition mathematics represents only a narrow slice of mathematical reasoning: problems are drawn from limited domains, require minimal advanced machinery, and can often reward insightful tricks over deep theoretical knowledge. We introduce \bench{}, a private benchmark of 25 expert-curated problems designed to evaluate AI systems on research-level mathematics that goes far beyond the olympiad frontier. Problems are authored by Ivy League mathematics professors, graduate students, and PhD-holding IMO medalists, and routinely took their authors weeks to solve independently. Each problem undergoes double-blind verification by two independent domain experts who must solve the problem from scratch, and yields a unique, closed-form solution assessed by programmatic verifiers. We evaluate frontier models as unconstrained research agents, with full access to coding tools, search, and open-ended reasoning, using an unbiased statistical estimator computed over 100 independent runs per problem. Our results reveal that all frontier models currently score below 10\%, exposing a substantial gap between olympiad-level problem solving and genuine research-level mathematical reasoning. By keeping the benchmark fully private, we ensure that measured performance reflects authentic mathematical capability rather than memorization of training data.