Improve model card and add robotics metadata

README.md

@@ -1,7 +1,46 @@
 ---
 license: apache-2.0
+pipeline_tag: robotics
+library_name: transformers
+tags:
+- vla
+- world-model
+- embodied-ai
 ---
 
-Project page: https://fx-hit.github.io/cowvla-io/
+# Chain of World: World Model Thinking in Latent Motion
 
-Paper: https://huggingface.co/papers/2603.03195
+This repository contains the weights for **CoWVLA** (Chain-of-World VLA), a Vision-Language-Action framework that unifies world-model temporal reasoning with disentangled latent motion modeling.
+
+[**🌐 Project Page**](https://fx-hit.github.io/cowvla-io/) | [**📄 Paper**](https://huggingface.co/papers/2603.03195) | [**💻 GitHub**](https://github.com/fx-hit/CoWVLA)
+
+## Overview
+
+CoWVLA introduces a "Chain of World" paradigm to address limitations in current VLA models. While world-model VLAs often waste capacity reconstructing redundant backgrounds and latent-action VLAs lack temporally continuous modeling, CoWVLA:
+- Uses a pretrained video VAE (**VidTwin**) to disentangle structure and motion latents.
+- Pre-trains a VLA decoder to infer a continuous latent motion chain from an instruction and initial frame.
+- Co-fine-tunes the model to align latent dynamics with discrete action prediction in a single autoregressive decoder.
+
+This design preserves the temporal reasoning benefits of world models while maintaining the compactness and interpretability of latent actions.
+
+## Evaluation Results
+
+CoWVLA demonstrates strong performance across major robotic simulation benchmarks:
+
+| Benchmark | Metric | CoWVLA |
+| --- | --- | --- |
+| **LIBERO** | Spatial / Object / Goal / Long / Avg. | 97.2 / 97.8 / 94.6 / 92.8 / 95.6 |
+| **SimplerEnv-WidowX** | Stack / Carrot / Spoon / Eggplant / Avg. | 62.5 / 66.7 / 79.2 / 95.8 / 76.0 |
+
+## Citation
+
+If you find this work useful for your research, please cite:
+
+```bibtex
+@inproceedings{yang2026cowvla,
+  title     = {Chain of World: World Model Thinking in Latent Motion},
+  author    = {Yang, Fuxiang and Di, Donglin and Tang, Lulu and Zhang, Xuancheng and Fan, Lei and Li, Hao and Chen, Wei and Su, Tonghua and Ma, Baorui},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year      = {2026}
+}
+```