MMSciCode: Real-world Evaluation of Multilingual Multi-Discipline Scientific Research Coding
Xue Xia, Zheyuan Yang, Arman Cohan Yale University and Allen Institute for Artificial Intelligence, Yilun Zhao Yale University
We introduce MMSciCode, a comprehensive expert-level, multilingual multi-discipline benchmark for evaluating foundation models in scientific code generation. It includes 624 expert-annotated research coding problems spanning six core scientific disciplines. Compared to prior benchmarks, MMSciCode features three key advancements. First, it challenges models to integrate domain-specific knowledge with algorithmic reasoning to implement core functions from research papers, moving beyond the isolated, general-purpose coding tasks typically assessed in current benchmarks. Second, each problem is meticulously annotated by domain experts through a rigorous paper-grounded process, with strict quality controls implemented to ensure dataset integrity and authenticity. Finally, each problem is equipped with comprehensive unit test suites and containerized environments, enabling reproducible and diagnostic evaluation of both functional correctness and domain validity. We conduct an extensive evaluation of 28 state-of-the-art foundation models and 2 agentic coding tools on MMSciCode. Our results reveal that even the best non-agentic model achieves only around 15% accuracy, while the top agentic coding tool reaches 32.2%, both still far below human expert performance of 68.8%. Through comprehensive error analyses and case studies, we identify substantial performance gaps between models and human experts, providing actionable insights for advancing expert-level scientific code generation.
尚无 AI 解读，展示原文摘要
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表