OIBench: Benchmarking Strong Reasoning Models with Olympiad in Informatics

• URL: http://arxiv.org/abs/2506.10481v1
• Date: Thu, 12 Jun 2025 08:33:38 GMT
• Title: OIBench: Benchmarking Strong Reasoning Models with Olympiad in Informatics
• Authors: Yaoming Zhu, Junxin Wang, Yiyang Li, Lin Qiu, ZongYu Wang, Jun Xu, Xuezhi Cao, Yuhuai Wei, Mingshi Wang, Xunliang Cai, Rong Ma,
• Abstract summary: This paper introduces OIBench, a high-quality, private, and challenging olympiad-level informatics dataset comprising 250 carefully curated original problems.<n>We detail the construction methodology of the benchmark, ensuring a comprehensive assessment across various programming paradigms and complexities.<n>We propose Time/Space Completion Curves for finer-grained efficiency analysis and enable direct human-model comparisons.
• Score: 13.049841309304922
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As models become increasingly sophisticated, conventional algorithm benchmarks are increasingly saturated, underscoring the need for more challenging benchmarks to guide future improvements in algorithmic reasoning. This paper introduces OIBench, a high-quality, private, and challenging olympiad-level informatics dataset comprising 250 carefully curated original problems. We detail the construction methodology of the benchmark, ensuring a comprehensive assessment across various programming paradigms and complexities, and we demonstrate its contamination-resistant properties via experiments. We propose Time/Space Completion Curves for finer-grained efficiency analysis and enable direct human-model comparisons through high-level participant evaluations. Our experiments reveal that while open-source models lag behind closed-source counterparts, current SOTA models already outperform most human participants in both correctness and efficiency, while still being suboptimal compared to the canonical solutions. By releasing OIBench as a fully open-source resource (https://huggingface.co/datasets/AGI-Eval/OIBench), we hope this benchmark will contribute to advancing code reasoning capabilities for future LLMs.

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