Add paper link, project page, and metadata to dataset card

README.md

@@ -1,3 +1,37 @@
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-license: cc-by-sa-4.0
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+---
+license: cc-by-sa-4.0
+task_categories:
+- robotics
+---
+
+# Latent Particle World Models (LPWM)
+
+[Project Website](https://taldatech.github.io/lpwm-web) | [Paper](https://huggingface.co/papers/2603.04553) | [GitHub](https://github.com/taldatech/lpwm)
+
+Latent Particle World Model (LPWM) is a self-supervised object-centric world model scaled to real-world multi-object datasets and applicable in decision-making. LPWM autonomously discovers keypoints, bounding boxes, and object masks directly from video data, enabling it to learn rich scene decompositions without supervision. The architecture is trained end-to-end purely from videos and supports flexible conditioning on actions, language, and image goals.
+
+## Sample Usage
+
+To train LPWM on a dataset like Sketchy using the official implementation, you can use the following commands:
+
+```bash
+# Install environment
+conda env create -f environment.yml
+conda activate dlp
+
+# Train LPWM on Sketchy
+python train_lpwm.py --dataset sketchy
+```
+
+## Citation
+
+```bibtex
+@inproceedings{
+    daniel2026latent,
+    title={Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling},
+    author={Tal Daniel and Carl Qi and Dan Haramati and Amir Zadeh and Chuan Li and Aviv Tamar and Deepak Pathak and David Held},
+    booktitle={The Fourteenth International Conference on Learning Representations},
+    year={2026},
+    url={https://openreview.net/forum?id=lTaPtGiUUc}
+}
+```