AetherCode: Evaluating LLMs' Ability to Win In Premier Programming Competitions
This addresses the need for more rigorous evaluation of LLMs in competitive programming, though it is incremental as it builds on existing benchmarking efforts.
The authors tackled the problem of overstating LLM proficiency in programming by creating AetherCode, a benchmark using premier competition problems and expert-validated test suites, which revealed a substantial gap between LLMs and elite human programmers.
Competitive programming has emerged as a critical benchmark for evaluating the reasoning and coding capabilities of Large Language Models (LLMs). Despite impressive progress on existing benchmarks, we argue that current evaluations overstate model proficiency, masking a substantial gap between LLMs and elite human programmers. This gap arises from two key limitations: insufficient difficulty and scope of benchmark problems, and evaluation bias from low-quality test cases. To address these shortcomings, we present AetherCode, a new benchmark that draws problems from premier programming competitions such as IOI and ICPC, offering broader coverage and higher difficulty. AetherCode further incorporates comprehensive, expert-validated test suites built through a hybrid of automated generation and human curation, ensuring rigorous and reliable assessment. By combining challenging problem design with robust evaluation, AetherCode provides a more faithful measure of LLM capabilities and sets a new standard for future research in code reasoning.