README.md

---
license: mit
task_categories:
- question-answering
language:
- en
tags:
- reasoning
- physics
size_categories:
- n<1K
arxiv:
- 2502.15815
configs:
- config_name: default
  data_files:
  - split: public
    path: data/public-*
dataset_info:
  features:
  - name: problem_id
    dtype: string
  - name: domain
    dtype: string
  - name: difficulty_level
    dtype: string
  - name: problem
    dtype: string
  - name: solution
    dtype: string
  - name: answer
    dtype: string
  - name: code_answer_requirements
    dtype: string
  - name: reference_implementation
    dtype: string
  splits:
  - name: public
    num_bytes: 52743
    num_examples: 10
  download_size: 29845
  dataset_size: 52743
---
# TP Bench – Theoretical Physics Benchmark for AI

<!-- Provide a quick summary of the dataset. -->

TPBench is a curated dataset and evaluation suite designed to measure the reasoning capabilities of AI models in theoretical physics. Our test problems span multiple difficulty levels—from undergraduate to frontier research—and cover topics such as cosmology, high-energy theory, general relativity, and more. By providing a unified framework for problem-solving and auto-verifiable answers, TPBench aims to drive progress in AI-based research assistance for theoretical physics.
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### Dataset Sources

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- **Paper:** https://arxiv.org/abs/2502.15815
- **Website:** https://tpbench.org/
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## Uses

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## Citation

If you do find our dataset helpful, please cite our paper.

**BibTeX:**

    @misc{chung2025theoreticalphysicsbenchmarktpbench,  
      title={Theoretical Physics Benchmark (TPBench) -- a Dataset and Study of AI Reasoning Capabilities in Theoretical Physics},   
      author={Daniel J. H. Chung and Zhiqi Gao and Yurii Kvasiuk and Tianyi Li and Moritz Münchmeyer and Maja Rudolph and Frederic Sala and Sai Chaitanya Tadepalli},  
      year={2025},  
      eprint={2502.15815},  
      archivePrefix={arXiv},  
      primaryClass={cs.LG},  
      url={https://arxiv.org/abs/2502.15815},   
    }

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## Glossary [optional]

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