Improve dataset card: add metadata, links and descriptions

Hi! I'm Niels, part of the community science team at Hugging Face.

This PR improves the dataset card for the Mnist World dataset. Changes include:
- Adding the `image-to-video` task category to the metadata.
- Adding tags for `world-models` and `reinforcement-learning`.
- Providing links to the associated paper, project page, and GitHub repository.
- Adding descriptions for the different dataset configurations (`dynamic_po`, `static_po`, etc.) to help users understand the splits.
- Including the BibTeX citation for the paper.

README.md

@@ -1,4 +1,9 @@
 ---
+task_categories:
+- image-to-video
+tags:
+- world-models
+- reinforcement-learning
 dataset_info:
 - config_name: dynamic_fo
   features:
@@ -149,4 +154,40 @@ configs:
     path: static_po/validation_200-*
 ---
 
-Mnist World dataset for experiments of FloWM: https://huggingface.co/papers/2601.01075.
+# MNIST World Dataset
+
+This repository contains the **MNIST World** dataset, used for experiments in the paper [Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments](https://huggingface.co/papers/2601.01075), accepted at ICML 2026.
+
+MNIST World is a 2D partially observed video world modeling benchmark designed to evaluate how well models can handle smooth, time-parameterized symmetries and unobserved regions that continue to evolve.
+
+## Project Resources
+- **Paper:** [arXiv:2601.01075](https://arxiv.org/abs/2601.01075)
+- **Project Page:** [https://flowequivariantworldmodels.github.io/](https://flowequivariantworldmodels.github.io/)
+- **GitHub Repository:** [https://github.com/hlillemark/flowm](https://github.com/hlillemark/flowm)
+
+## Dataset Configurations
+
+The dataset is provided in several configurations to test different aspects of world modeling:
+
+- **`dynamic_po`**: Partially observed environments with dynamic elements (the main benchmark for the paper).
+- **`static_po`**: Partially observed environments where the world is static.
+- **`dynamic_fo`**: Fully observed environments with dynamic elements.
+- **`dynamic_fo_no_sm`**: Fully observed environments with dynamic elements but no self-motion.
+
+Each configuration includes `train` and `validation` splits.
+
+## Citation
+
+If you find this dataset or the FloWM framework useful, please cite:
+
+```bibtex
+@misc{lillemark2026flowequivariantworldmodels,
+    title={Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments}, 
+    author={Hansen Jin Lillemark and Benhao Huang and Fangneng Zhan and Yilun Du and Thomas Anderson Keller},
+    year={2026},
+    eprint={2601.01075},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG},
+    url={https://arxiv.org/abs/2601.01075}, 
+  }
+```