M3Kang: Evaluating Multilingual Multimodal Mathematical Reasoning in Vision-Language Models

Aleix Torres-Camps, Nathaniel Mitrani Hadida, Victor Conchello Vendrell, Àlex Batlle Casellas, Arnau Padrés Masdemont, Jordi Ros-Giralt

Track: long paper (up to 10 pages)

Keywords: Benchmark, Dataset, Vision-Language Models, VLM, Mathematics, Reasoning, Multilingual

TL;DR: We introduce M3Kang, the first highly multilingual, multimodal, mathematical reasoning dataset for VLMs, based on the Kangaroo Math Competition, and conduct extensive benchmarking on SOTA models.

Abstract: Despite state-of-the-art vision-language models (VLMs) have demonstrated strong reasoning capabilities, their performance in multilingual mathematical reasoning remains underexplored, particularly when compared to human performance. To bridge this gap, we introduce M3Kang, the first massively multilingual, multimodal mathematical reasoning dataset for VLMs. It is derived from the Kangaroo Math Competition, the world’s largest mathematics contest, which annually engages over six million participants under the age of 18 across more than 90 countries. M3Kang includes 1,747 unique multiple-choice problems organized by grade-level difficulty, with translations into 108 culturally diverse languages, some of them including diagrams essential for solving them. Using this dataset, we conduct extensive benchmarking on both closed- and open-source SOTA models. We observe that, despite recent advances, models still struggle with basic math and diagram-based reasoning, with performance scaling with language presence and model size, but not with grade level. We also find that multilingual techniques can be effectively extended to the multimodal setting, resulting in significant improvements over baseline approaches. Our analysis also incorporates performance data from over 68,000 students, enabling direct comparison with human performance. We are open-sourcing M3Kang, including the English-only subset M2Kang, along with the framework and codebase used to construct the dataset.

Presenter: ~Aleix_Torres-Camps1

Format: Maybe: the presenting author will attend in person, contingent on other factors that still need to be determined (e.g., visa, funding).

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 30