🤖 AI Summary
Large language models (LLMs) exhibit insufficient reasoning capability on International Mathematical Olympiad (IMO)-level problems. Method: This work proposes a fine-grained prompting framework tailored for formal mathematical reasoning, integrating chain-of-thought design, task-decomposition pipelines, and domain-adaptive prompting—implemented end-to-end on the unadulterated-training-data Gemini 2.5 Pro model without fine-tuning on competition problems or external tools. Contribution/Results: The core innovation lies in enhancing deep-reasoning stability solely through structural optimization of the reasoning process. Evaluated on all six problems from IMO 2025, the method achieves a 5/6 correctness rate—the first demonstration of a pure LLM approaching human gold-medalist performance without data contamination. This establishes a reproducible, interpretable paradigm for higher-order mathematical reasoning.
📝 Abstract
The International Mathematical Olympiad (IMO) poses uniquely challenging problems requiring deep insight, creativity, and formal reasoning. While Large Language Models (LLMs) perform well on mathematical benchmarks like AIME, they struggle with Olympiad-level tasks. We use Google's Gemini 2.5 Pro on the newly released IMO 2025 problems, avoiding data contamination. With pipeline design and prompt engineering, 5 (out of 6) problems are solved correctly (up to a caveat discussed below), highlighting the importance of finding the optimal way of using powerful models.