LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?
arXiv:2510.09595v3 Announce Type: replace-cross 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
The continuous development and evaluation of large language models (LLMs) for complex tasks like competitive programming necessitate new, robust benchmarks, which LiveOIBench aims to provide to address existing limitations.
This benchmark indicates a significant push to rigorously test and improve LLMs' problem-solving and coding capabilities against human performance, potentially accelerating advancements in AI agents.
The introduction of LiveOIBench provides a more challenging, accessible, and comprehensive standard for evaluating LLM performance in competitive programming, offering a clearer picture of their capabilities and limitations.
- · AI research institutions
- · LLM developers
- · Competitive programming platforms
- · AI agent developers
- · Prior, less rigorous LLM coding benchmarks
Increased pressure on LLMs to perform at or above human expert levels in complex algorithmic and coding challenges.
Faster development and deployment of more capable AI agents proficient in logical reasoning and software development.
Potential for AI to automate significant portions of complex software engineering and problem-solving, impacting labor markets and innovation cycles.
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Read at arXiv cs.CL