license: mit
language:
- en
tags:
- physics
- cmt
pretty_name: CMT Benchmark
CMT-Benchmark
CMT-Benchmark is a specialized dataset of 50 original, research-level problems in Condensed Matter Theory (CMT) designed to evaluate the scientific reasoning, analytical, and computational capabilities of Large Language Models (LLMs).
Details
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
Description
Unlike traditional textbook-based benchmarks, CMT-Benchmark consists of bespoke problems authored by a panel of experts. The problems are designed to test if an AI can function as a scientific research assistant, requiring the synthesis of material knowledge with theoretical principles. The dataset covers seven major computational and theoretical methods including Hartree-Fock, Exact Diagonalization, and Quantum Monte Carlo.
- Funded by: NSF (Awards 2433348, OAC-2118310, DMR-2237522), US-ONR, Swiss National Science Foundation, NTT Research, and the U.S. Department of Energy.
- Language: English.
Sources
- Dataset: Hugging Face Repository.
- Evaluation Code: Github Repository
- Paper: CMT-BENCHMARK: A BENCHMARK FOR CONDENSED MATTER THEORY BUILT BY EXPERT RESEARCHERS.
Structure
The dataset contains 50 problems categorized by theoretical/computational methods:
- Exact Diagonalization (ED): 16% (8 problems).
- Statistical Mechanics (SM): 12% (6 problems).
- Quantum Monte Carlo (QMC): 12% (6 problems).
- Hartree-Fock (HF): 10% (5 problems).
- DMRG: 8% (4 problems).
- PEPS: 6% (3 problems).
- Variational Monte Carlo (VMC): 4% (2 problems).
- Other: 32% (16 problems).
Creation
Problems were authored in a collaborative Google Sheet environment using a custom-built extension. Authors iteratively refined problems to identify gaps in LLM reasoning.
- Technical Note on Infrastructure: The custom Google Sheet integration and the LaTeX-to-Sympy parsing infrastructure used for automated grading are described in further detail in Roggeveen et al. (2025).
Model Performance Summary
The following table summarizes the Overall Pass@1 rates (%) as reported in the paper.
| Model | Overall |
|---|---|
| GPT-4o | 2.0 |
| GPT-4.1 | 4.0 |
| GPT-5 | 30.0 |
| GPT-5-mini | 24.0 |
| GPT-5-nano | 14.0 |
| GPT-o3 | 26.0 |
| GPT-o4-mini | 18.0 |
| Gemini 2.0 Flash | 10.0 |
| Gemini 2.5 Flash | 4.0 |
| Gemini 2.5 Pro | 14.0 |
| Claude 3.7 Sonnet | 6.0 |
| Claude 4.1 Sonnet | 2.0 |
| Claude 4.0 Sonnet | 6.0 |
| Claude 4.1 Opus | 8.0 |
| Claude 4.0 Opus | 10.0 |
| DeepSeek v3 | 4.0 |
| LLaMA Maverick | 12.0 |
Citation
BibTeX:
@misc{https://doi.org/10.48550/arxiv.2510.05228,
doi = {10.48550/ARXIV.2510.05228},
url = {https://arxiv.org/abs/2510.05228},
author = {Pan, Haining and Roggeveen, James V. and Berg, Erez and Carrasquilla, Juan and Chowdhury, Debanjan and Ganguli, Surya and Ghimenti, Federico and Hasik, Juraj and Hunt, Henry and Jiang, Hong-Chen and Kamb, Mason and Kao, Ying-Jer and Khatami, Ehsan and Lawler, Michael J. and Luo, Di and Neupert, Titus and Qi, Xiaoliang and Brenner, Michael P. and Kim, Eun-Ah},
keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers},
publisher = {arXiv},
year = {2025},
copyright = {Creative Commons Zero v1.0 Universal}
}