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The article discusses a verification-and-refinement pipeline developed to enhance the performance of large language models on Olympiad-level mathematics problems, specifically at the International Mathematical Olympiad (IMO) 2025. Despite these models' general accuracy, they traditionally struggled with such complex tasks. The proposed methodology shows a significant improvement, achieving an 85.7% success rate compared to much lower baseline accuracies, emphasizing the importance of innovative techniques alongside advancements in model capabilities.