ResearchCodeBench: Benchmarking LLMs on Implementing Novel Machine Learning Research Code
Tianyu Hua, Harper Hua, Violet Xiang, Benjamin Klieger, Sang T. Truong, Weixin Liang, Fan-Yun Sun, Nick Haber
Abstract
Large language models (LLMs) have shown promise in transforming machine learning research, yet their capability to faithfully implement genuinely novel ideas from recent research papers—ideas unseen during pretraining—remains unclear. We introduce ResearchCodeBench, a benchmark that evaluates LLMs’ ability to translate cutting-edge ML contributions from top 2024-2025 research papers into executable code. We assessed 30+ proprietary and open-source LLMs, finding that even the best models correctly implement less than 40% of the code. We present empirical findings on performance comparison, contamination, and error patterns. By providing a rigorous evaluation platform, ResearchCodeBench enables continuous understanding and advancement of LLM-driven innovation in research code generation.
BibTeX
@inproceedings{
hua2025researchcodebench,
title={ResearchCodeBench: Benchmarking {LLM}s on Implementing Novel Machine Learning Research Code},
author={Tianyu Hua and Harper Hua and Violet Xiang and Benjamin Klieger and Sang T. Truong and Weixin Liang and Fan-Yun Sun and Nick Haber},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2025},
url={https://openreview.net/forum?id=3k70Vt0YFS}
}