--- dataset_info: - config_name: v1_2024 features: - name: id dtype: int64 - name: description dtype: string - name: time_limit dtype: int64 - name: memory_limit dtype: int64 - name: checker dtype: string - name: test_cases list: - name: input dtype: string - name: output dtype: string - name: year dtype: int64 - name: date dtype: string - name: difficulty dtype: string - name: contest_category dtype: string - name: contest_name dtype: string splits: - name: test num_bytes: 20187500547 num_examples: 400 download_size: 12737762718 dataset_size: 20187500547 - config_name: v1_2025 features: - name: id dtype: int64 - name: description dtype: string - name: time_limit dtype: int64 - name: memory_limit dtype: int64 - name: checker dtype: string - name: year dtype: int64 - name: date dtype: string - name: difficulty dtype: string - name: contest_category dtype: string - name: contest_name dtype: string splits: - name: test num_bytes: 201028 num_examples: 56 download_size: 104645 dataset_size: 201028 configs: - config_name: v1_2024 data_files: - split: test path: v1_2024/test-* - config_name: v1_2025 data_files: - split: test path: v1_2025/test-* ---

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

Comming Soon Hugging Face Dataset License
## Introduction 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. ## Highlights **Problem Curation from Top-Tier Competitions**: AetherCode is the first benchmark to systematically collect problems from premier programming competitions worldwide, including the Olympiad in Informatics (OI) and the International Collegiate Programming Contest (ICPC). Our process involved a comprehensive collection, meticulous cleaning, and format conversion of problems from PDF to a Markdown+LaTeX structure. Each problem statement was manually proofread for correctness, and a team of competitive programming experts mannotated each problem with classification tags. **High-Quality Test Case Generation**: We developed a hybrid methodology, combining automated generation with expert annotation, to create high-quality test cases for every problem. We evaluated the correctness and comprehensiveness of our test cases by validating them against a large corpus of collected solutions, enforcing a standard of zero false positives and zero false negatives. ## Quickstart ```python from datasets import load_dataset # Login using e.g. `huggingface-cli login` to access this dataset ds = load_dataset("m-a-p/AetherCode", "v1_2024") ``` ## License This project is licensed under CC-BY-4.0. See the [LICENSE file](https://huggingface.co/datasets/m-a-p/AetherCode/blob/main/LICENSE) for details.