LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?
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Computer Science > Artificial Intelligence
arXiv:2510.09595 (cs)
[Submitted on 10 Oct 2025 (v1), last revised 8 Jul 2026 (this version, v3)]
Title:LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?
Authors:Kaijian Zou, Aaron Xiong, Yunxiang Zhang, Frederick Zhang, Yueqi Ren, Jirong Yang, Ayoung Lee, Shitanshu Bhushan, Lu Wang
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Abstract:Competitive programming problems are increasingly used to evaluate the coding capabilities of large language models (LLMs) due to their complexity and ease of verification. Yet, current coding benchmarks face limitations such as a lack of exceptionally challenging problems, insufficient test case coverage, and reliance on online platform APIs that limit accessibility. To address these issues, we introduce LiveOIBench, a large-scale competitive programming benchmark featuring 403 expert-curated problems, averaging 60 official test cases each, drawn from 72 contests across 14 Informatics Olympiads held between 2023 and 2025. LiveOIBench has four key features: (1) expert-designed tasks with detailed subtask rubrics and extensive test cases; (2) direct comparison to elite human contestants; (3) continuous updates to reduce contamination risk; and (4) a fully offline, reproducible evaluation system. Benchmarking 34 popular general-purpose and reasoning LLMs, we find that GPT-5 achieves an 81.76th percentile, still falling short of top human contestants, while among the open-weight models, GPT-OSS-120B reaches only the 60th percentile. Reasoning-trace analyses indicate that robust reasoning models prioritize precise problem analysis over excessive exploration. Finally, analyses across release dates, task familiarity, and code similarity find minimal evidence of data contamination in our benchmark. Our leaderboard, code, and data are available at: this https URL.
| Comments: | ICML 2026 Camera Ready |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2510.09595 [cs.AI] |
| (or arXiv:2510.09595v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2510.09595
arXiv-issued DOI via DataCite
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Submission history
From: Kaijian Zou [view email][v1] Fri, 10 Oct 2025 17:54:24 UTC (1,944 KB)
[v2] Mon, 22 Dec 2025 18:56:01 UTC (2,956 KB)
[v3] Wed, 8 Jul 2026 17:08:31 UTC (2,400 KB)
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View a PDF of the paper titled LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?, by Kaijian Zou and 8 other authors
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