| --- |
| 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} |
| } |
| ``` |
