Update README.md

README.md

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 ## 🔥 News
-* **`2025.05.03`** 🌟 We are happy to release the RTV-Bench. You can find the RTV-Bench from [![hf_checkpoint](https://img.shields.io/badge/🤗-RTV--Bench-9C276A.svg)](https://huggingface.co/datasets/xunsh/RTV-Bench) or [![ms_checkpoint](https://img.shields.io/badge/🤖-RTV--Bench-8A2BE2.svg)](https://www.modelscope.cn/datasets/Jungang/RTV-Bench).
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+* **`2025.05.03`** 🌟 We are happy to release the RTV-Bench.
+  
 ## TODO
 - [ ] Release the final label json.
 - [ ] Release the evaluation code.
 - [ ] Construct a more comprehensive benchmark for real-time video analysis.
 - [ ] ···
-## 👀 $\mathcal{RTV}\text{-}Bench$ Overview
+## 👀 RTV-Bench Overview
 
 We introduce RTV-Bench, a fine-grained benchmark for MLLM real-time video analysis, which contains **552** videos (167.2 hours) and **4,631** high-quality QA pairs. We evaluated leading MLLMs, including proprietary (GPT-4o, Gemini 2.0), open-source offline (Qwen2.5-VL, VideoLLaMA3), and open-source real-time (VITA-1.5, InternLM-XComposer2.5-OmniLive) models. Experiment results show open-source real-time models largely outperform offline ones but still trail top proprietary models. Our analysis also reveals that larger model size or higher frame sampling rates do not significantly boost $\mathcal{RTV}\text{-}Bench$ performance, sometimes causing slight decreases. This underscores the need for better model architectures optimized for video stream processing and long sequences to advance real-time video analysis with MLLMs.  $\mathcal{RTV}\text{-}Bench$ includes three key principles: 
 * **Multi-Timestamp Question Answering (MTQA)**, where answers evolve with scene changes;