[2510.05228] CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers

Computer Science > Machine Learning arXiv:2510.05228 (cs) [Submitted on 6 Oct 2025 (v1), last revised 27 Feb 2026 (this version, v2)] Title:CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers Authors:Haining Pan, James V. Roggeveen, Erez Berg, Juan Carrasquilla, Debanjan Chowdhury, Surya Ganguli, Federico Ghimenti, Juraj Hasik, Henry Hunt, Hong-Chen Jiang, Mason Kamb, Ying-Jer Kao, Ehsan Khatami, Michael J. Lawler, Di Luo, Titus Neupert, Xiaoliang Qi, Michael P. Brenner, Eun-Ah Kim View a PDF of the paper titled CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers, by Haining Pan and 18 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) have shown remarkable progress in coding and math problem-solving, but evaluation on advanced research-level problems in hard sciences remains scarce. To fill this gap, we present CMT-Benchmark, a dataset of 50 problems covering condensed matter theory (CMT) at the level of an expert researcher. Topics span analytical and computational approaches in quantum many-body, and classical statistical mechanics. The dataset was designed and verified by a panel of expert researchers from around the world. We built the dataset through a collaborative environment that challenges the panel to write and refine problems they would want a research assistant to solve, including Hartree-Fock, exact diagonalization, quantum/variational Monte Carlo, density matrix renor...