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README.md

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-This dataset was presented in the paper [OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?](https://huggingface.co/papers/2501.05510).
+<h1 align="center">
+  <font color=#0088cc>O</font><font color=#060270>V</font><font color=#0088cc>O</font>-Bench: How Far is Your Video-LLMs from Real-World <font color=#0088cc>O</font>nline <font color=#060270>V</font>ide<b style="color: #0088cc;">O</b> Understanding?
+</h1>
 
-Project page: https://joeleelyf.github.io/OVO-Bench/
-Code: https://github.com/JoeLeelyf/OVO-Bench
+<p align="center">
+  🔥🔥OVO-Bench is accepted by CVPR 2025!🔥🔥
+</p>
 
-The repository contains the following file information:
+<p align="center">
+  <a href="https://arxiv.org/abs/2501.05510" style="margin-right: 10px;"> 
+    <img src="https://img.shields.io/badge/arXiv-2501.05510-b31b1b.svg?logo=arXiv">
+  </a>
+</p>
+
+> **Important Note:** Current codebase is modified compared to our initial arXiv paper. We strongly recommend that any use of OVO-Bench should be based on current edition.
+
+## Introduction
+### 🌟 Three distinct problem-solving modes
+-  **Backward Tracing**: trace back to past events to answer the question.
+-  **Real-Time Visual Perception**: understand and respond to events as they unfold at the current timestamp.
+-  **Forward Active Responding**: delay the response until sufficient future information becomes available to answer the question accurately.