update readme doc

constants.py

@@ -29,14 +29,14 @@ We aim to provide cost-effective and accurate evaluation for multimodal models,
 ## 📊🔍 Results & Takeaways from Evaluating Top Models
 
 
-### ️‍🔥📝 January 2025
+### ️‍🔥📝 2025.01
 
 - **Gemini 2.0 Experimental (1206)** and **Gemini 2.0 Flash Experimental** outperform **GPT-4o** and **Claude 3.5 Sonnet**. 
 - We add **Grok-2-vision-1212** to the single-image leaderboard. The model seems to use a lot of tokens per image, and cannot run many of our multi-image and video tasks.
 - We will evaluate o1 series models when there is budget.
 
 
-### 📝 November 2024
+### 📝 2024.11
 
 - **GPT-4o (0513)** and **Claude 3.5 Sonnet (1022)** lead the benchmark. **Claude 3.5 Sonnet (1022)** improves over **Claude 3.5 Sonnet (0620)** obviously in planning tasks (application dimension) and UI/Infographics inputs (input format dimension).
 - **Qwen2-VL** stands out among open-source models, and its flagship model gets close to some proprietary flagship models
@@ -84,7 +84,9 @@ CITATION_BUTTON_TEXT = r"""
 
 SUBMIT_INTRODUCTION = """# Submit on MEGA-Bench Leaderboard
 
-Our evaluation pipeline is released on our [GitHub repository](https://github.com/TIGER-AI-Lab/MEGA-Bench). We will provide details on how to submit third-party results to this leaderboard.
+Our evaluation pipeline is released on our [GitHub repository](https://github.com/TIGER-AI-Lab/MEGA-Bench). 
+
+The evaluation results processed by the [breakdown analysis script](https://github.com/TIGER-AI-Lab/MEGA-Bench?tab=readme-ov-file#get-multi-dimensional-breakdown-analysis) are put into this leaderboard.
 
 """