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commit CMPhysBench
Browse filesCo-authored-by: Cursor <cursoragent@cursor.com>
- CMPhysBench.json +0 -0
- README.md +39 -0
CMPhysBench.json
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README.md
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---
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language:
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- en
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license: apache-2.0
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size_categories:
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- n=520
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task_categories:
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- question-answering
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- text-generation
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pretty_name: CMPhysBench
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tags:
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- Condensed Matter Physics
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- physics
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- benchmark
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---
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# CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics
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> 🎉🎉🎉 This paper is accpeted by ICLR 2026.
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[](https://arxiv.org/abs/2508.18124) [](https://github.com/CMPhysBench/CMPhysBench) [](https://huggingface.co/datasets/weidawang/CMPhysBench) [](https://github.com/CMPhysBench/CMPhysBench/blob/main/LICENSE)
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We introduce **CMPhysBench**, designed to assess the proficiency of Large Language Models (LLMs) in **C**ondensed **M**atter **Phys**ics, as a novel **Bench**mark. CMPhysBench is composed of more than 520 graduate-level meticulously curated questions covering both representative subfields and foundational theoretical frameworks of condensed matter physics, such as magnetism, superconductivity, strongly correlated systems, etc. To ensure a deep understanding of the problem-solving process,we focus exclusively on calculation problems, requiring LLMs to independently generate comprehensive solutions. Meanwhile, leveraging tree-based representations of expressions, we introduce the Scalable Expression Edit Distance (SEED) score, which provides fine-grained (non-binary) partial credit and yields a more accurate assessment of similarity between prediction and ground-truth. Our results show that even the best models, Grok-4, reach only 36 average SEED score and 28% accuracy on CMPhysBench, underscoring a significant capability gap, especially for this practical and frontier domain relative to traditional physics.
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<div align="center">
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<img src="https://raw.githubusercontent.com/CMPhysBench/CMPhysBench/main/imgs/CMPhysBench.png" width="1000"/>
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</div>
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## Citations
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```bibtex
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@article{wang2025cmphysbench,
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title={CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics},
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author={Wang, Weida and Huang, Dongchen and Li, Jiatong and Yang, Tengchao and Zheng, Ziyang and Zhang, Di and Han, Dong and Chen, Benteng and Luo, Binzhao and Liu, Zhiyu and others},
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journal={arXiv preprint arXiv:2508.18124},
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year={2025}
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}
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```
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