# MATH-PT: A Math Reasoning Benchmark for European and Brazilian Portuguese

arXiv: https://arxiv.org/abs/2604.25926 · HTML: https://mlsanity.com/paper/2604.25926/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Mathematical reasoning, one of 17 directions in LLM reasoning.
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Placed in

- LLM reasoning / [Mathematical reasoning](https://mlsanity.com/lr/mathematical-reasoning/)

## Builds on (most cited first, 2 of 2)

- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Kimi K2: Open Agentic Intelligence](https://mlsanity.com/paper/2507.20534/) (2025)

## Built on by (most cited first, 2 of 2)

- [Multilingual Verifier Bias in RLVR: Benchmark, Rollout Diagnosis, and the Cross-Lingual Selection Bottleneck](https://mlsanity.com/paper/2608.20362/) (2026)
- [Does CoT-Pass@k Really Check the CoT? A Multilingual Mathematical Audit](https://mlsanity.com/paper/2609.32622/) (2026)

## Abstract (arXiv)

The use of large language models (LLMs) for complex mathematical reasoning is an emergent area of research, with fast progress in methods, models, and benchmark datasets. However, most mathematical reasoning evaluations exhibit a significant linguistic bias, with the vast majority of benchmark datasets being exclusively in English or (at best) translated from English. We address this limitation by introducing sc Math-PT, a novel dataset comprising 1,729 mathematical problems written in European and Brazilian Portuguese. sc Math-PT is curated from a variety of high-quality native sources, including mathematical Olympiads, competitions, and exams from Portugal and Brazil. We present a comprehensive benchmark of current state-of-the-art LLMs on sc Math-PT, revealing that frontier reasoning models achieve strong performance in multiple choice questions compared to open weight models, but that their performance decreases for questions with figures or open-ended questions. To facilitate future research, we release the benchmark dataset and model outputs.