Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: mit
+language:
+- en
+library_name: transformers
+tags:
+- video-generation
+- robotics
+- embodied-ai
+- physical-reasoning
+- causal-reasoning
+- inverse-dynamics
+- wow
+- arxiv:2509.22642
+datasets:
+- WoW-world-model/WoW-1-Benchmark-Samples
+pipeline_tag: video-generation
+base_model: wan
+---
+
+# 🤖 WoW-1-Wan-14B-2M
+
+**WoW-1-Wan-14B** is a 14-billion-parameter generative world model trained on **2 million real-world robot interaction trajectories**. It is designed to imagine, reason, and act in physically consistent environments, powered by SOPHIA-guided refinement and a co-trained **Inverse Dynamics Model**.
+
+This model is part of the [WoW (World-Omniscient World Model)](https://github.com/wow-world-model/wow-world-model) project, introduced in the paper:
+
+> **[WoW: Towards a World omniscient World model Through Embodied Interaction](https://arxiv.org/abs/2509.22642)**  
+> *Chi et al., 2025 – arXiv:2509.22642*
+
+## 🧠 Key Features
+
+- **14B parameters** trained on **2M robot interaction samples**
+- Learns **causal physical reasoning** from embodied action
+- Generates physically consistent video and robotic action plans
+- Uses **SOPHIA**, a vision-language critic, to refine outputs
+- Paired with an **Inverse Dynamics Model** to complete imagination-to-action loop
+
+## 🧪 Training Data
+
+<!-- - Dataset: [WoW-1-Benchmark-Samples](https://huggingface.co/datasets/WoW-world-model/WoW-1-Benchmark-Samples) -->
+- **2M** Real-world robot interaction trajectories
+- Multimodal scenes including vision, action, and language
+- Diverse **mixture captions** for better generalization
+### 🧠 Mixture Caption Strategy
+
+- **Prompt Lengths**:
+  - Short: *"The Franka robot, grasp the red bottle on the table"*
+  - Long: *"The scene... open the drawer, take the screwdriver, place it on the table..."*
+
+- **Robot Model Mixing**:
+  - Captions reference various robot types
+  - Example: *"grasp with the Franka Panda arm"*, *"use end-effector to align"*
+
+- **Action Granularity**:
+  - Coarse: *"move to object"*
+  - Fine: *"rotate wrist 30° before grasping"*
+
+
+## 🔄 Continuous Updates
+
+This dataset will be **continuously updated** with:
+- More trajectories
+- Richer language
+- Finer multimodal annotations
+
+## 🧩 Applications
+
+- Zero-shot video generation in robotics
+- Causal reasoning and physics simulation
+- Long-horizon manipulation planning
+- Forward and inverse control prediction
+
+## 📄 Citation
+
+```bibtex
+@article{chi2025wow,
+  title={WoW: Towards a World omniscient World model Through Embodied Interaction},
+  author={Chi, Xiaowei and Jia, Peidong and Fan, Chun-Kai and Ju, Xiaozhu and Mi, Weishi and Qin, Zhiyuan and Zhang, Kevin and Tian, Wanxin and Ge, Kuangzhi and Li, Hao and others},
+  journal={arXiv preprint arXiv:2509.22642},
+  year={2025}
+}
+```
+
+## 🔗 Resources
+
+- 🧠 Project page: [wow-world-model.github.io](https://wow-world-model.github.io/)
+- 💻 GitHub repo: [wow-world-model/wow-world-model](https://github.com/wow-world-model/wow-world-model)
+- 📊 Dataset: [WoW-1 Benchmark Samples](https://huggingface.co/datasets/WoW-world-model/WoW-1-Benchmark-Samples)
+
+---