# MultiNRC: A Challenging and Native Multilingual Reasoning Evaluation Benchmark for LLMs

arXiv: https://arxiv.org/abs/2507.17476 · HTML: https://mlsanity.com/paper/2507.17476/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 9 of 9)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [ARC Prize 2024: Technical Report](https://mlsanity.com/paper/2412.04604/) (2024)
- [MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation](https://mlsanity.com/paper/2503.10497/) (2025)
- [MultiLoKo: a multilingual local knowledge benchmark for LLMs spanning 31 languages](https://mlsanity.com/paper/2504.10356/) (2025)
- [BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models](https://mlsanity.com/paper/2502.07346/) (2025)
- [M4U: Evaluating Multilingual Understanding and Reasoning for Large Multimodal Models](https://mlsanity.com/paper/2405.15638/) (2024)

## Built on by (most cited first, 16 of 16)

- [MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages](https://mlsanity.com/paper/2509.26601/) (2025)
- [The Riddle of Reflection: Evaluating Reasoning and Self-Awareness in Multilingual LLMs using Indian Riddles](https://mlsanity.com/paper/2511.00960/) (2025)
- [MobiFlow: Real-World Mobile Agent Benchmarking through Trajectory Fusion](https://mlsanity.com/paper/2604.09587/) (2026)
- [The Culture Funnel: You Can't Align What isn't in the Data](https://mlsanity.com/paper/2606.13808/) (2026)
- [CulturALL: Benchmarking Multilingual and Multicultural Competence of LLMs on Grounded Tasks](https://mlsanity.com/paper/2604.19262/) (2026)
- [Beyond Confidence: Rethinking Self-Assessments for Performance Prediction in LLMs](https://mlsanity.com/paper/2605.07806/) (2026)
- [CultureForest: Understanding and Evaluating Cultural Norm Grounded Reasoning in LLMs](https://mlsanity.com/paper/2606.01879/) (2026)
- [Eye-Q: A Multilingual Benchmark for Visual Word Puzzle Solving and Image-to-Phrase Reasoning](https://mlsanity.com/paper/2601.03400/) (2026)
- [Macaron: Controlled, Human-Written Benchmark for Multilingual and Multicultural Reasoning via Template-Filling](https://mlsanity.com/paper/2602.10732/) (2026)
- [CroCo: Cross-Lingual Contrastive Preference Tuning on Self-Generations](https://mlsanity.com/paper/2605.26293/) (2026)
- [The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes](https://mlsanity.com/paper/2606.11470/) (2026)
- [ChLogic: Evaluating Robustness of Logical Reasoning in Chinese Expressions](https://mlsanity.com/paper/2606.17905/) (2026)
- [SFBench: The SciFy Scientific Feasibility Benchmark](https://mlsanity.com/paper/2606.29630/) (2026)
- [ADAGE: A Language-Agnostic Pipeline for Analogical Reasoning Evaluation](https://mlsanity.com/paper/2607.23058/) (2026)
- [CulturalMenuBench: Probing the Knowledge-Application Gap in Multimodal Culinary Reasoning](https://mlsanity.com/paper/2609.03526/) (2026)
- [Language, Language Models, and What We're Talking About](https://mlsanity.com/paper/2609.03577/) (2026)

## Abstract (arXiv)

Although recent Large Language Models (LLMs) have shown rapid improvement on reasoning benchmarks in English, the evaluation of such LLMs' multilingual reasoning capability across diverse languages and cultural contexts remains limited. Existing multilingual reasoning benchmarks are typically constructed by translating existing English reasoning benchmarks, biasing these benchmarks towards reasoning problems with context in English language/cultures. In this work, we introduce the Multilingual Native Reasoning Challenge (MultiNRC), a benchmark designed to assess LLMs on more than 1,000 native, linguistic and culturally grounded reasoning questions written by native speakers in French, Spanish, and Chinese. MultiNRC covers four core reasoning categories: language-specific linguistic reasoning, wordplay & riddles, cultural/tradition reasoning, and math reasoning with cultural relevance. For cultural/tradition reasoning and math reasoning with cultural relevance, we also provide English equivalent translations of the multilingual questions by manual translation from native speakers fluent in English. This set of English equivalents can provide a direct comparison of LLM reasoning capacity in other languages vs. English on the same reasoning questions. We systematically evaluate current 14 leading LLMs covering most LLM families on MultiNRC and its English equivalent set. The results show that (1) current LLMs are still not good at native multilingual reasoning, with none scoring above 50% on MultiNRC; (2) LLMs exhibit distinct strengths and weaknesses in handling linguistic, cultural, and logical reasoning tasks; (3) Most models perform substantially better in math reasoning in English compared to in original languages (+10%), indicating persistent challenges with culturally grounded knowledge.