license: other
license_name: license
license_link: LICENSE
configs:
- config_name: default
data_files:
- split: standard
path: data/standard-*
- split: high_quality
path: data/high_quality-*
dataset_info:
features:
- name: level
dtype: int32
- name: language
dtype: string
- name: text_only
dtype: bool
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: standard
num_bytes: 4548122261.668258
num_examples: 108677
- name: high_quality
num_bytes: 1699405334.9488075
num_examples: 40617
download_size: 5773349370
dataset_size: 6247527596.617065
M3Kang: A Multilingual Multimodal Mathematical Reasoning Dataset with Kangaroo Problems
Introduction
Despite state-of-the-art vision-language models (VLMs) have demonstrated strong reasoning capabilities, their performance in multilingual mathematical reasoning remains underexplored. 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. The dataset consists of two splits: standard, used in the paper for benchmarking and containing 108,677 problems, and high-quality, a smaller subset of problems with higher-quality translations, totaling 40,617 problems.
This dataset is published as part of our paper M3Kang: Evaluating Multilingual Multimodal Mathematical Reasoning in Vision-Language Models.
Description
The M3Kang dataset is built from problems in the Kangaroo Math Competition, the largest annual international contest for primary and secondary students. Problems are organized by grade level and sometimes accompanied by figures (see “Sample Images” below for an example). We sourced data from the original PDFs of the 2007–2024 editions organized by the Catalan Math Society in Catalonia, Spain, and processed it through a pipeline described below. In addition to processing and formatting the problems into a proper data set structure compatible with Hugging Face, our processing pipeline automatically translates the problems to many other languages and performs quality checks on the translations, to obtain the final multilingual data set. The final dataset includes each problem in two formats: a text version of the question and an image containing the full problem (question, multi-choice answers, and optional figure, and the correct answer) with translations into more than 100 languages.
Sample Images
Example of problem found in the M3Kang dataset, with translations to English, Catalan, Spanish, and German:
Dataset Details
| Type of problems | Math, logic, reasoning |
| Number of English problems | 1747 |
| Multimodal? | Yes, text + image |
| Multilingual? | Yes, it contains tranlsations to 108 languages |
| Number of levels | 8 levels: from school grade 5 through grade 12 |
| Total # of problems | 108,677 |
Note that because our translation pipeline includes a quality assurance stage that discards low quality translations, the number of problems in non-English languages is in general smaller than 1747. See the following table for the exact number of problems in each language for both splits.
Data Format
A sample from our dataset consists in a string of “text”, containing the problem statement, an “image”, containing a snapshot of the problem (which includes the problem statement, the answer key and any accompanying figures), an “answer” field, containing the correct answer, a “language” field, containing the language of the problem, and a “level”, containing the level (school grade) of the problem. For example:
{
"id: "lvl-6_2007_10",
“level”: 6,
“language”: “eng”,
"text_only": false,
“text”: “In the figure on the right, you can
see a triangle ABC in which two segments have
been drawn from vertices A and B to points on
the opposite sides, thereby dividing the triangle
into nine non-overlapping regions. If we draw a
total of eight segments to the opposite sides,
four from A and four from B, into how many
non-overlapping regions will the triangle be
divided?”,
"label": "B",
“image”:
}
Dataset Collection Process
We sourced data from the original PDFs of the 2007–2024 editions organized by the Catalan Math Society (SCM) in Catalonia, Spain. We gained access to this dataset following formal approval from the SCM, who authorized our use of the data for research purposes via an official letter. The original data was available in the form of PDF files, each file containing a test of math multi-choice Kangaroo problems (e.g., a test with 24 problems, for a given year and a given school level). The input data also included the correct answer to each problem.
We developed a pipeline to process the set of input PDFs and automatically (with some human-in-the-loop support) generate the data set in a format compatible with Hugging Face. The pipeline used to generate the dataset includes only software that has meticulously approved by the legal team. This pipeline is also described in the paper M3Kang: Evaluating Multilingual Multimodal Mathematical Reasoning in Vision-Language Models.
Dataset license
This dataset is intended for research purposes only. See Data License Agreement - Research Use
Dataset Citation Instructions
Please cite our paper if you use this dataset in your research.
@inproceedings{
anonymous2025mkang,
title={M3Kang: Evaluating Multilingual Multimodal Mathematical Reasoning in Vision-Language Models},
author={Anonymous},
booktitle={Submitted to The Fourteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=txURffKsPo},
note={under review}
}
Acknowledgements
We gratefully acknowledge the Societat Catalana de Matemàtiques (SCM) for authorizing access to the Catalan Kangaroo problems for non-commercial use, which served as the foundation for this dataset.
Qualcomm AI Research
At Qualcomm AI Research, we are advancing AI to make its core capabilities – perception, reasoning, and action – ubiquitous across devices. Our mission is to make breakthroughs in fundamental AI research and scale them across industries. By bringing together some of the best minds in the field, we’re pushing the boundaries of what’s possible and shaping the future of AI.
Qualcomm AI Research continues to invest in and support deep-learning research in computer vision. The publication of this dataset for use by the AI research community is one of our many initiatives. Find out more about Qualcomm AI Research. For any questions or technical support, please contact us at research.datasets@qti.qualcomm.com
Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc.