diff --git a/README.md b/README.md
index 6cdc0605..f861c9bc 100644
--- a/README.md
+++ b/README.md
@@ -15,9 +15,9 @@
 <!-- Comment badge to wait until the project is publicly available. --> 
 
 # 📰 News
-| 🗞️News        | 📝Description                 |
+| 🗞️ News        | 📝 Description                 |
 | --            | ------                        |
-| First release | RDAgent is release on Github |
+| First release | **RDAgent** is released on Github |
 
 
 # 🌟 Introduction
@@ -31,15 +31,15 @@ We believe that the automatic evolution of R&D will lead to solutions of signifi
 
 <!-- Tag Cloud -->
 R&D is a very general scenario. The advent of RDAgent can be your
-- [🎥Automatic Quant Factory](https://rdagent.azurewebsites.net/factor_loop)
-- 🤖Data mining agent: iteratively proposing [🎥data](https://rdagent.azurewebsites.net/dmm) & [models](https://rdagent.azurewebsites.net/model_loop) and implementing them by gaining knowledge from data.
-- 🦾Research copilot: Auto read [🎥research papers](https://rdagent.azurewebsites.net/report_model)/[🎥reports](https://rdagent.azurewebsites.net/report_factor) and implement model structures or building datasets.
+- 🎥 [Automatic Quant Factory](https://rdagent.azurewebsites.net/factor_loop)
+- 🤖 Data Mining Agent: iteratively proposing 🎥 [data](https://rdagent.azurewebsites.net/dmm) & 🎥 [models](https://rdagent.azurewebsites.net/model_loop) and implementing them by gaining knowledge from data.
+- 🦾 Research Copilot: Auto read 🎥 [research papers](https://rdagent.azurewebsites.net/report_model) /🎥 [financial reports](https://rdagent.azurewebsites.net/report_factor) and implement model structures or building datasets.
 - ...
 
-You can click the [🎥link](https://rdagent.azurewebsites.net) above to view the demo. More methods and scenarios are being added to the project to empower your R&D processes and boost productivity.
+You can click the 🎥 [link](https://rdagent.azurewebsites.net) above to view the demo. More methods and scenarios are being added to the project to empower your R&D processes and boost productivity.
 <!-- 
 - TODO: Demo: it fails to display the video in the README.md.
-We have a quick 🎥demo for one use case of RDAgent.
+We have a quick 🎥 demo for one use case of RDAgent.
 [![Demo Video](https://img.youtube.com/vi/5275fcb75803ad2bb9541c3abd86dedfd578a28fa32b46fa28917b33/0.jpg)](https://rdagent.azurewebsites.net:443/media/5275fcb75803ad2bb9541c3abd86dedfd578a28fa32b46fa28917b33.mp4)
  <p align="center">
   <a href="https://rdagent.azurewebsites.net/"> <img src="docs/_static/img/logo/1.png" /> </a>
@@ -49,10 +49,11 @@ We have a quick 🎥demo for one use case of RDAgent.
  [![Watch the video](https://img.freepik.com/premium-vector/video-streaming-media-player-template-mockup-live-stream-window-player-online-broadcasting_659151-73.jpg)](https://rdagent.azurewebsites.net/)
  
 
-# ⚡Quick start
+# ⚡ Quick start
+
 You can try above demos by running the following command:
 
-### 🐳Docker installation.
+### 🐳 Docker installation.
 Users must ensure Docker is installed before attempting most scenarios. Please refer to the [official 🐳Docker page](https://docs.docker.com/engine/install/) for installation instructions.
 
 ### 🐍 Create a Conda Environment
@@ -83,7 +84,7 @@ EOF
 
 ### 🚀 Run the Application
 
-The [🎥demo](https://rdagent.azurewebsites.net) is implemented by the following commands(each item represents one demo, you can select the one you prefer):
+The 🎥 [demo](https://rdagent.azurewebsites.net) is implemented by the following commands(each item represents one demo, you can select the one you prefer):
 
 - Run the **Automated Quantitative Trading & Iterative Factors Evolution**:  Qlib self-loop factor proposal and implementation application
   ```sh
@@ -92,58 +93,58 @@ The [🎥demo](https://rdagent.azurewebsites.net) is implemented by the followin
 
 - Run the **Automated Quantitative Trading & Iterative Model Evolution**: Qlib self-loop model proposal and implementation application
   ```sh
-  python fin_model
+  rdagent fin_model
   ```
 
 - Run the **Automated Medical Predtion Model Evolution**: medical self-loop model proposal and implementation application
   ```sh
-  python med_model
+  rdagent med_model
   ```
 
 - Run the **Automated Quantitative Trading & Factors Extraction from Financial Reports**:  Run the Qlib factor extraction and implementation application based on financial reports
   ```sh
-  rdagent fin_factor_report <Your report folder>
+  rdagent fin_factor_report
   ```
 
-- Run the **Automated Model Research & Development Co-Pilot**: model extraction and implementation application
+- Run the **Automated Model Research & Development Copilot**: model extraction and implementation application
   ```sh
   rdagent general_model  <Your paper url>
   ```
 
-### 🚀 Monitor the Application Results
+### 🖥️ Monitor the Application Results
 - You can serve our demo app to monitor the RD loop by running the following command:
   ```sh
   rdagent ui --port 80 --log_dir <your log folder like "log/2024-07-16_11-21-46-612120/">
   ```
 
-# Scenarios
+# 🏭 Scenarios
 
-We have applied RD-Agent to multiple valuable data-driven industrial scenarios..
+We have applied RD-Agent to multiple valuable data-driven industrial scenarios.
 
 
 ## 🎯 Goal: Agent for Data-driven R&D
 
 In this project, we are aiming to build a Agent to automate Data-Driven R\&D that can
-+ 📄Read real-world material (reports, papers, etc.) and **extract** key formulas, descriptions of interested **features** and **models**, which are the key components of data-driven R&D .
-+ 🛠️**Implement** the extracted formulas (e.g., features, factors, and models) in runnable codes.
++ 📄 Read real-world material (reports, papers, etc.) and **extract** key formulas, descriptions of interested **features** and **models**, which are the key components of data-driven R&D .
++ 🛠️ **Implement** the extracted formulas (e.g., features, factors, and models) in runnable codes.
    + Due to the limited ability of LLM in implementing at once, evolve the agent to be able to extend abilities by learning from feedback and knowledge and improve the agent's ability to implement more complex models.
-+ 💡Propose **new ideas** based on current knowledge and observations.
++ 💡 Propose **new ideas** based on current knowledge and observations.
 
 <!-- ![Data-Centric R&D Overview](docs/_static/overview.png) -->
 
 ## 📈 Scenarios/Demos
 
-In the two key areas of data-driven scenarios, model implementation and data building, our system aims to serve two main roles: 🦾copilot and 🤖agent. 
-- The 🦾copilot follows human instructions to automate repetitive tasks. 
-- The 🤖agent, being more autonomous, actively proposes ideas for better results in the future.
+In the two key areas of data-driven scenarios, model implementation and data building, our system aims to serve two main roles: 🦾Copilot and 🤖Agent. 
+- The 🦾Copilot follows human instructions to automate repetitive tasks. 
+- The 🤖Agent, being more autonomous, actively proposes ideas for better results in the future.
 
 The supported scenarios are listed below:
 
 | Scenario/Target | Model Implementation                   | Data Building                                                                      |
 | --              | --                                     | --                                                                                 |
-| 💹 Finance      | 🤖[Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/model_loop) | - 🦾[Auto reports reading & implementation](https://rdagent.azurewebsites.net/report_factor) <br/> - 🤖[Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/factor_loop) |
-| 🩺 Medical      | 🤖[Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/dmm) | -                                                                                  |
-| 🏭 General      | 🦾[Auto paper reading & implementation](https://rdagent.azurewebsites.net/report_model)    | -                                                                                  |
+| 💹 Finance      | 🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/model_loop) |  🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/factor_loop)  <br/>   🦾 [Auto reports reading & implementation](https://rdagent.azurewebsites.net/report_factor)  |
+| 🩺 Medical      | 🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/dmm) | -                                                                                  |
+| 🏭 General      | 🦾 [Auto paper reading & implementation](https://rdagent.azurewebsites.net/report_model)    | -                                                                                  |
 
 Different scenarios vary in entrance and configuration. Please check the detailed setup tutorial in the scenarios documents.
 
@@ -153,8 +154,9 @@ Here is a gallery of successful explorations. You can download the source code a
 rdagent ui --port 80 --log_dir gallary/
 ```
 
+Please refer to [📚readthedocs](https://rdagent.readthedocs.io/) for more details of the scenarios.
 
-# ⚙️Framework
+# ⚙️ Framework
 
 ![image](https://github.com/user-attachments/assets/98fce923-77ab-4982-93c8-a7a01aece766)
 
@@ -170,10 +172,11 @@ The research questions within this framework can be divided into three main cate
 
 We believe that the key to delivering high-quality solutions lies in the ability to evolve R&D capabilities. Agents should learn like human experts, continuously improving their R&D skills.
 
+More documents can be found in the [📚readthedocs](https://rdagent.readthedocs.io/).
 
-# 📃Paper/Work list
+# 📃 Paper/Work list
 
-## Benchmark
+## 📊 Benchmark
 - [Towards Data-Centric Automatic R&D](https://arxiv.org/abs/2404.11276);
 ```BibTeX
 @misc{chen2024datacentric,
@@ -187,7 +190,7 @@ We believe that the key to delivering high-quality solutions lies in the ability
 ```
 ![image](https://github.com/user-attachments/assets/494f55d3-de9e-4e73-ba3d-a787e8f9e841)
 
-## Research
+## 🔍 Research
 
 In a data mining expert's daily research and development process, they propose a hypothesis (e.g., a model structure like RNN can capture patterns in time-series data), design experiments (e.g., finance data contains time-series and we can verify the hypothesis in this scenario), implement the experiment as code (e.g., Pytorch model structure), and then execute the code to get feedback (e.g., metrics, loss curve, etc.). The experts learn from the feedback and improve in the next iteration.
 
@@ -195,7 +198,7 @@ Based on the principles above, we have established a basic method framework that
 
 For more detail, please refer to our [Demos page](https://rdagent.azurewebsites.net).
 
-## Development
+## 🛠️ Development
 
 - [Collaborative Evolving Strategy for Automatic Data-Centric Development](https://arxiv.org/abs/2407.18690)
 ```BibTeX
@@ -211,18 +214,19 @@ For more detail, please refer to our [Demos page](https://rdagent.azurewebsites.
 ![image](https://github.com/user-attachments/assets/75d9769b-0edd-4caf-9d45-57d1e577054b)
 
 
-# Contributing
+# 🤝 Contributing
 
-More documents can be found in the [📚readthedocs](https://rdagent.readthedocs.io/).
-
-## Guidance
+## 📝 Guidelines
 This project welcomes contributions and suggestions.
-You can find issues in the issues list or simply running `grep -r "TODO:"`.
+Contributing to this project is straightforward and rewarding. Whether it's solving an issue, addressing a bug, enhancing documentation, or even correcting a typo, every contribution is valuable and helps improve RDAgent.
+
+To get started, you can explore the issues list, or search for `TODO:` comments in the codebase by running the command `grep -r "TODO:"`.
 
-Making contributions is not a hard thing. Solving an issue(maybe just answering a question raised in issues list ), fixing/issuing a bug, improving the documents and even fixing a typo are important contributions to RDAgent.
 <img src="https://img.shields.io/github/contributors-anon/microsoft/RD-Agent"/>
 
-<a href="https://github.com/microsoft/RD-Agent/graphs/contributors"><img src="https://contrib.rocks/image?repo=microsoft/RD-Agent" /></a>
+<a href="https://github.com/microsoft/RD-Agent/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=microsoft/RD-Agent&max=100&columns=15" />
+</a>
 
-# Legal disclaimer
+# ⚖️ Legal disclaimer
 <p style="line-height: 1; font-style: italic;">The RD-agent is provided “as is”, without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. The RD-agent is aimed to facilitate research and development process in the financial industry and not ready-to-use for any financial investment or advice. Users shall independently assess and test the risks of the RD-agent in a specific use scenario, ensure the responsible use of AI technology, including but not limited to developing and integrating risk mitigation measures, and comply with all applicable laws and regulations in all applicable jurisdictions. The RD-agent does not provide financial opinions or reflect the opinions of Microsoft, nor is it designed to replace the role of qualified financial professionals in formulating, assessing, and approving finance products. The inputs and outputs of the RD-agent belong to the users and users shall assume all liability under any theory of liability, whether in contract, torts, regulatory, negligence, products liability, or otherwise, associated with use of the RD-agent and any inputs and outputs thereof.</p>