---
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](https://huggingface.co/datasets/AnonBenchmark322/benchmark_data).
* **Evaluation Code:** [Github Repository](https://github.com/JamesRoggeveen/cmt_benchmark_data)
* **Paper:** [CMT-BENCHMARK: A BENCHMARK FOR CONDENSED MATTER THEORY BUILT BY EXPERT RESEARCHERS](https://arxiv.org/abs/2510.05228).

## 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)]( 	
https://doi.org/10.48550/arXiv.2505.11774).

## 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:**

```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}
}
```