Evaluating Large Language Models on the 2026 Korean CSAT Mathematics Exam: Measuring Mathematical Ability in a Zero-Data-Leakage Setting

This research paper, sourced from ArXiv, focuses on evaluating Large Language Models (LLMs) on a specific and challenging task: the 2026 Korean CSAT Mathematics Exam. The core of the study lies in assessing the mathematical capabilities of LLMs within a controlled environment, specifically one designed to prevent data leakage. This suggests a rigorous approach to understanding the true mathematical understanding of these models, rather than relying on memorization or pre-existing knowledge of the exam content. The focus on a future exam (2026) implies the use of simulated or generated data, or a forward-looking analysis of potential capabilities. The 'zero-data-leakage setting' is crucial, as it ensures the models are tested on their inherent problem-solving abilities rather than their ability to recall information from training data.

• •The research evaluates LLMs on a future Korean CSAT Mathematics Exam.
• •The study emphasizes a zero-data-leakage setting to assess true mathematical ability.
• •The focus is on understanding the problem-solving capabilities of LLMs.