Lune

ICLR2026Top-tier venue

AetherCode: Evaluating LLMs’ Ability to Win In Premier Programming Competitions

Zihan Wang, Jiaze Chen, Zhicheng Liu, Haojie Pan, Markus Mak, Yidi Du, Geonsik Moon, Aaron Tua, Kunshuo Peng, Jiayi Lu, Boqian Zou, Chenyang Ran

2026Year
11Citations
2Top-tier citations

Abstract

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.

Ask about this paper

Your agent reads all of it.

Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.

Questions to start from

Your agent calls

Luneget_paper_fulltext

Ask in Lune

Free to start. No credit card required.

lune papers fulltext fd67a0d7-a344-44ba-93e9-56cf02c4c518

Cited by top-tier papers2

Ask how each one uses it

Builds on1

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines