Update README.md

README.md

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 license: mit
 task_categories:
 - video-text-to-text
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 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; 
 * **Hierarchical Question Structure**, combining basic and advanced queries; and
-* **Multi-dimensional Evaluation**, assessing the ability of continuous perception, understanding, and reasoning. 
+* **Multi-dimensional Evaluation**, assessing the ability of continuous perception, understanding, and reasoning.  -->
 
 
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