AetherCode: Evaluating LLMs' Ability to Win In Premier Programming Competitions
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 22 Aug 2025.
Builds on
11 of 15 references.
References · 11
- 2025Qwen3 Technical Reportcited by 8,195
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
- 2021Program Synthesis with Large Language Modelscited by 3,509
- 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
- 2022Competition-Level Code Generation with AlphaCodecited by 1,427
- 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
- 2021Measuring Coding Challenge Competence With APPScited by 879
- 2025CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratingscited by 109
- 2024Can Language Models Solve Olympiad Programming?cited by 70
- 2025CodeContests+: High-Quality Test Case Generation for Competitive Programmingcited by 39
Built on by
Citing papers per year: 2025: 4; 2026: 6.
10 papers.
- 2025Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluationcited by 83
- 2026Seed1.8 Model Card: Towards Generalized Real-World Agencycited by 47
- 2025TeachLM: Post-Training LLMs for Education Using Authentic Learning Datacited by 8
- 2025VeriEquivBench: An Equivalence Score for Ground-Truth-Free Evaluation of Formally Verifiable Codecited by 7
- 2025LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?cited by 3
- 2026GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learningcited by 3
- 2026Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolutioncited by 3
- 2026Scaling Reasoning Tokens via RL and Parallel Thinking: Evidence From Competitive Programmingcited by 1
- 2026Beyond Problem Solving: UOJ-Bench for Evaluating Code Generation, Hacking, and Repair in Competitive Programmingcited by 1
- 2026Mi:dm K 2.5 Procited by 0
- 2026Beyond Problem Solving: UOJ-Bench for Evaluating Code Generation, Hacking, and Repair in Competitive Programming11 Jun 2026
- 2026Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution14 May 2026
- 2026GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning3 Apr 2026
- 2026Scaling Reasoning Tokens via RL and Parallel Thinking: Evidence From Competitive Programming1 Apr 2026
- 2026Seed1.8 Model Card: Towards Generalized Real-World Agency21 Mar 2026
- 2026Mi:dm K 2.5 Pro19 Mar 2026
- 2025Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation13 Oct 2025
- 2025LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?10 Oct 2025
- 2025VeriEquivBench: An Equivalence Score for Ground-Truth-Free Evaluation of Formally Verifiable Code7 Oct 2025
- 2025TeachLM: Post-Training LLMs for Education Using Authentic Learning Data6 Oct 2025