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    <abstract>Mathematical reasoning remains one of the most challenging domains for large language models (LLMs), requiring not only linguistic understanding but also structured logical deduction and numerical precision. While recent LLMs demonstrate strong general-purpose reasoning abilities, their mathematical competence across diverse languages remains underexplored. Existing benchmarks primarily focus on English or a narrow subset of high-resource languages, leaving significant gaps in assessing multilingual and cross-lingual mathematical reasoning. To address this, we introduce MathMist, a parallel multilingual benchmark for mathematical problem solving and reasoning. MathMist encompasses 2,890 parallel Bangla-English gold standard artifacts, totaling \approx30K aligned question–answer pairs across thirteen languages, representing an extensive coverage of high-, medium-, and low-resource linguistic settings. The dataset captures linguistic variety, multiple types of problem settings, and solution synthesizing capabilities. We systematically evaluate a diverse suite of models, including open-source small and medium LLMs, proprietary systems, and multilingual-reasoning-focused models under zero-shot, chain-of-thought (CoT), perturbated reasoning, and code-switched reasoning paradigms. Our results reveal persistent deficiencies in LLMs’ ability to perform consistent and interpretable mathematical reasoning across languages, with pronounced degradation in low-resource settings. All the codes and data are available at GitHub: https://github.com/mahbubhimel/MathMist</abstract>
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%0 Conference Proceedings
%T MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning
%A E Sobhani, Mahbub
%A Sayeedi, Md. Faiyaz Abdullah
%A Mohiuddin, Tasnim
%A Islam, Md Mofijul
%A Shatabda, Swakkhar
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F e-sobhani-etal-2026-mathmist
%X Mathematical reasoning remains one of the most challenging domains for large language models (LLMs), requiring not only linguistic understanding but also structured logical deduction and numerical precision. While recent LLMs demonstrate strong general-purpose reasoning abilities, their mathematical competence across diverse languages remains underexplored. Existing benchmarks primarily focus on English or a narrow subset of high-resource languages, leaving significant gaps in assessing multilingual and cross-lingual mathematical reasoning. To address this, we introduce MathMist, a parallel multilingual benchmark for mathematical problem solving and reasoning. MathMist encompasses 2,890 parallel Bangla-English gold standard artifacts, totaling \approx30K aligned question–answer pairs across thirteen languages, representing an extensive coverage of high-, medium-, and low-resource linguistic settings. The dataset captures linguistic variety, multiple types of problem settings, and solution synthesizing capabilities. We systematically evaluate a diverse suite of models, including open-source small and medium LLMs, proprietary systems, and multilingual-reasoning-focused models under zero-shot, chain-of-thought (CoT), perturbated reasoning, and code-switched reasoning paradigms. Our results reveal persistent deficiencies in LLMs’ ability to perform consistent and interpretable mathematical reasoning across languages, with pronounced degradation in low-resource settings. All the codes and data are available at GitHub: https://github.com/mahbubhimel/MathMist
%U https://aclanthology.org/2026.findings-eacl.131/
%P 2524-2550

Markdown (Informal)

[MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning](https://aclanthology.org/2026.findings-eacl.131/) (E Sobhani et al., Findings 2026)

• MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning (E Sobhani et al., Findings 2026)

ACL

• Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Tasnim Mohiuddin, Md Mofijul Islam, and Swakkhar Shatabda. 2026. MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning. In Findings of the Association for Computational Linguistics: EACL 2026, pages 2524–2550, Rabat, Morocco. Association for Computational Linguistics.