Improve model card: add metadata and links to paper/code

README.md

@@ -1,11 +1,35 @@
 ---
-license: mit
 language:
 - en
-tags:
-- arxiv:2602.16855
+license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 
+# Mobile-Agent-v3.5 (GUI-Owl-1.5)
+
+This repository contains the model weights for **Mobile-Agent-v3.5** (also known as **GUI-Owl-1.5**), a native multi-platform GUI agent foundation model.
+
+- **Paper:** [Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents](https://huggingface.co/papers/2602.16855)
+- **Repository:** [GitHub - X-PLUG/MobileAgent](https://github.com/X-PLUG/MobileAgent)
+- **Demos:** [ModelScope Online Demo](http://modelscope.cn/studios/MobileAgentTest/computer_use) | [Bailian Online Demo](https://bailian.console.aliyun.com/next?tab=demohouse#/experience/adk-computer-use/pc)
+
+## Introduction
+
+GUI-Owl-1.5 is a native multi-platform GUI agent model family featuring instruct and thinking variants. It supports a wide range of platforms, including desktop, mobile, and browser environments, to enable cloud-edge collaboration and real-time interaction. The model unifies perception, grounding, reasoning, planning, and action execution within a single policy network.
+
+Key features include:
+- **Hybrid Data Flywheel:** A data pipeline for UI understanding and trajectory generation based on simulated and cloud-based sandbox environments.
+- **Unified Enhancement of Agent Capabilities:** A unified thought-synthesis pipeline to enhance reasoning, memory, and Tool/MCP (Model Context Protocol) usage.
+- **Multi-platform Environment RL Scaling:** Uses a new environment RL algorithm, MRPO, to address challenges in multi-platform interaction and long-horizon task training.
+
+## Benchmarks
+
+GUI-Owl-1.5 achieves state-of-the-art results on more than 20 GUI benchmarks:
+- **GUI Automation:** OSWorld (56.5), AndroidWorld (71.6), and WebArena (48.4).
+- **Grounding:** ScreenSpotPro (80.3).
+- **Tool-calling:** OSWorld-MCP (47.6) and MobileWorld (46.8).
+- **Memory & Knowledge:** GUI-Knowledge Bench (75.5).
 
 ## Citation
 
@@ -18,3 +42,4 @@ If you find this model useful, please cite our paper:
   journal={arXiv preprint arXiv:2602.16855},
   year={2026}
 }
+```