Update README.md

README.md

@@ -203,6 +203,7 @@ size_categories:
 # QuAnTS: Question Answering on Time Series
 
 [![Dataset Generation GitHub Repo](https://img.shields.io/badge/GitHub-Dataset%20Generation-blue?logo=github)](https://github.com/mauricekraus/quants-generate)
+[![arXiv](https://img.shields.io/badge/arXiv-2511.05124-b31b1b.svg)](https://arxiv.org/abs/2511.05124)
 
 QuAnTS is a challenging dataset designed to bridge the gap in question-answering research on time series data.
 The dataset features a wide variety of questions and answers concerning human movements, presented as tracked skeleton trajectories.
@@ -234,9 +235,17 @@ You are free to mix the training and validation splits as needed.
 
 The QuAnTS dataset is licensed under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](ttps://creativecommons.org/licenses/by/4.0/ ) license.
 
-If you use the QuAnTS dataset in your research, please cite:
+If you use the QuAnTS dataset in your research, please cite [the paper]([2511.05124](https://arxiv.org/abs/2511.05124)):
 ```
-Under Review, TODO
+@misc{divo2025quantsquestionansweringtime,
+      title={QuAnTS: Question Answering on Time Series}, 
+      author={Felix Divo and Maurice Kraus and Anh Q. Nguyen and Hao Xue and Imran Razzak and Flora D. Salim and Kristian Kersting and Devendra Singh Dhami},
+      year={2025},
+      eprint={2511.05124},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2511.05124}, 
+}
 ```
 
 The dataset was curated by a team of researchers from various institutions: