Update README.md

README.md

@@ -306,102 +306,9 @@ Command for running OpenCUA-7B in OSWorld:
         --coordinate_type qwen25
 ```
 
----
-
-# AgentNet Dataset - Large-Scale Computer-Use Dataset
-
-<div align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/67b327cdd4665a0448eef7d5/dw5k183ucDSB2SZuS5f2V.png" width="400" alt="AgentNet Dataset Domain Distribution">
-</div>
-
-AgentNet is the first large-scale desktop computer-use agent trajectory dataset, containing 22.6K human-annotated computer-use tasks across Windows, macOS, and Ubuntu systems.
-
-👉 **[AgentNet Huggingface Dataset](https://huggingface.co/datasets/xlangai/AgentNet)**
-
-Download the dataset here：
-```
-pip install -U huggingface_hub
-huggingface-cli download xlangai/AgentNet --repo-type dataset --local-dir ./AgentNet
-```
-
-Collecting computer-use agent training data requires 3 steps:
-- Demonstrate human computer-use task via [AgentNetTool](https://agentnet-tool.xlang.ai/);
-- Preprocess the demonstration using [Action Reduction & State-Action Matching](./data/data-processor);
-- For each step, [synthesize reflective long CoT](./data/cot-generator)
-
-
-## 1 AgentNetTool – Annotation & Verification Tool
-<div align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/67b327cdd4665a0448eef7d5/ETjCOoIRR7f1YZCJ2kfiW.png" width="700" alt="AgentNet Tool">
-</div>
-
-
-Our **AgentNetTool** is a cross-platform GUI recorder that runs unobtrusively on annotators' machines. It captures synchronized **screen video**, **mouse/keyboard events**, and **accessibility trees**, then provides an in-browser UI for reviewing, trimming, and submitting demonstrations. AgentNet Tool is available on Windows, macOS and Ubuntu.
-
-👉 **[AgentNetTool Document](https://agentnet-tool.xlang.ai/)**
-
-
-
-## 2 DataProcessor – Action Reduction & State–Action Matching
-Raw demonstrations can contain thousands of low-level events that are too dense for model training.
-The **DataProcessor** module (`./data/data-process/`) performs two key steps:
-
-1.  **Action Reduction** — merges granular signals into concise, semantically meaningful PyAutoGUI actions (e.g., collapsing mouse moves → click, coalescing scrolls, grouping key-press sequences into text or hotkeys).
-2.  **State–Action Matching** — aligns every reduced action with the *last visually distinct frame* **before** the action begins, avoiding future-information leakage and yielding compact state–action pairs.
-
-These processed trajectories underlie all downstream training and evaluation.
-
----
-
-## 3 CoTGenerator – Synthesizing Reflective Long Chain-of-Thought Inner Monologue
-To boost robustness and interpretability, we augment each trajectory with **reflective long Chain-of-Thought (CoT) reasoning**.
-The **CoTGenerator** pipeline (`./data/cot-generator/`) synthesizes step-level reflections that:
-
-*   reflect on the previous action,
-*   explain *why* an action is chosen given the current observation and history,
-*   note potential alternative actions, and
-*   forecast the expected next state.
-
-Empirically, models trained with these rich CoTs scale better with data and generalize across unseen applications.
-
-
-# Evaluation
-
-<div align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/67b327cdd4665a0448eef7d5/emy1QCJwQj9KqHkVmtNH2.png" width="800" alt="AgentNetBench">
-</div>
-
-
-**AgentNetBench** (`./AgentNetBench/`) provides a realistic offline evaluator for OS agent trajectories. It compares model-predicted low-level actions (click, moveTo, write, press, scroll, terminate, etc.) against ground-truth human actions and reports detailed metrics.
-
-👉 See **[AgentNetBench/README.md](./evaluation/agentnetbench/README.md)** for usage instructions.
-
-# Acknowledge
-<p>
-We thank Yu Su, Caiming Xiong, and the anonymous reviewers for their insightful discussions and valuable feedback.
-We are grateful to Moonshot AI for providing training infrastructure and annotated data.
-We also sincerely appreciate Hao Yang, Zhengtao Wang, and Yanxu Chen from the Kimi Team for their strong infrastructure support and helpful guidance.
-We thank Chong Peng, Taofeng Xue, and Qiumian Huang from the <a href="https://github.com/meituan/EvoCUA" target="_blank">Meituan EvoCUA Team</a> for their contributions to vLLM integration.
-The development of our tool is based on the open-source projects-<a href="https://github.com/TheDuckAI/DuckTrack" target="_blank">DuckTrack</a> and <a href="https://github.com/OpenAdaptAI/OpenAdapt" target="_blank">OpenAdapt</a>.
-We are very grateful to their commitment to the open source community. Finally, we extend our deepest thanks to all annotators for their tremendous effort and contributions to this project.
-</p>
-
-# License
-
-This project is licensed under the MIT License - see the LICENSE file in the root folder for details.
-
-## Research Use and Disclaimer
-
-OpenCUA models are intended for **research and educational purposes only**.
-
-### Prohibited Uses
-- The model may **not** be used for any purpose or activity that violates applicable laws or regulations in any jurisdiction
-- Use for illegal, unethical, or harmful activities is strictly prohibited
+## Research and Commercial Use
 
-### Disclaimer
-- The authors, contributors, and copyright holders are **not responsible** for any illegal, unethical, or harmful use of the Software, nor for any direct or indirect damages resulting from such use
-- Use of the "OpenCUA" name, logo, or trademarks does **not** imply any endorsement or affiliation unless separate written permission is obtained
-- Users are solely responsible for ensuring their use complies with applicable laws and regulations
+OpenCUA (including the model, dataset, tools, and code) may be used for **research, educational, and commercial purposes** under the **MIT License** (see `LICENSE`).
 
 ## Important Notes on Coordinate Systems
 <div style="border-left: 6px solid #9ca3af; background: #f5f5f5; padding: 12px 16px; margin: 16px 0;">