LLMs for Mathematical Reasoning

We study the reasoning capabilities of large language models in the context of mathematical problem solving. We develop and maintain several datasets and benchmarks to more effectively evaluate this capability in state-of-the-art models.

Blog Posts (on matharena.ai)

MathArena Apex: Unconquered Final-Answer Problems

With Flying Colors: Language Models Ace IMC 2025

Not Even Bronze: Evaluating LLMs on IMO 2025

Publications

2025

MathArena: Evaluating LLMs on Uncontaminated Math Competitions

Mislav Balunović, Jasper Dekoninck, Nikola Jovanović, Ivo Petrov, Martin Vechev

NeurIPS Datasets and Benchmarks 2025

Website
Paper
Code

Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad

Ivo Petrov, Jasper Dekoninck, Lyuben Baltadzhiev, Maria Drencheva, Kristian Minchev, Mislav Balunović, Nikola Jovanović, Martin Vechev

AI4Math@ICML 2025

Website
Paper
Code

The Open Proof Corpus: A Large-Scale Study of LLM-Generated Mathematical Proofs

Jasper Dekoninck, Ivo Petrov, Kristian Minchev, Mislav Balunovic, Martin Vechev, Miroslav Marinov, Maria Drencheva, Lyuba Konova, Milen Milenov Shumanov, Kaloyan Tsvetkov, Nikolay Drenchev, Lazar D. Todorov, Kalina Nikolova, Nikolay Georgiev, Vanesa Kalinkova, Margulan Ismoldayev

AI4Math@ICML 2025

Website
Paper
Code

MathConstruct: Challenging LLM Reasoning with Constructive Proofs

Mislav Balunović*, Jasper Dekoninck*, Nikola Jovanović, Ivo Petrov, Martin Vechev

ICML 2025 * Equal contribution

Paper
Code

2024

Constraint-Based Synthetic Data Generation for LLM Mathematical Reasoning

Timofey Fedoseev, Dimitar I. Dimitrov, Timon Gehr, Martin Vechev

Workshop on Mathematical Reasoning, NeurIPS 2024

Poster
Paper