---
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.
<!-- ## Dataset Details

### Dataset Description -->

<!-- Provide a longer summary of what this dataset is. -->



<!-- - **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed] -->

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

<!-- - **Repository:** [More Information Needed] -->
- **Paper:** https://arxiv.org/abs/2502.15815
- **Website:** https://tpbench.org/
<!-- 
## Uses

<!-- Address questions around how the dataset is intended to be used. -->

<!-- ### Direct Use -->

<!-- This section describes suitable use cases for the dataset. -->

<!-- [More Information Needed] -->

<!-- ### Out-of-Scope Use -->

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

<!-- [More Information Needed] -->

<!-- ## Dataset Structure -->

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

<!-- [More Information Needed] -->

<!-- ## Dataset Creation -->

<!-- ### Curation Rationale -->

<!-- Motivation for the creation of this dataset. -->

<!-- [More Information Needed] -->

<!-- ### Source Data -->

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

<!-- #### Data Collection and Processing -->

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

<!-- [More Information Needed] -->

<!-- #### Who are the source data producers? -->

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->


<!-- ### Annotations [optional] -->

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

<!-- #### Annotation process -->

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->


<!-- #### Who are the annotators? -->

<!-- This section describes the people or systems who created the annotations. -->

<!-- #### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->


<!-- ## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->


<!-- ### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

<!-- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. -->

## 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},   
    }

<!-- **APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

<!-- [More Information Needed]

## More Information [optional]

[More Information Needed]

## Dataset Card Authors [optional]

[More Information Needed]

## Dataset Card Contact