What is it about?
Online video understanding requires Video Large Language Models (Video-LLMs) to proactively respond to meaningful changes in continuous video streams. In this paper, we propose Reference State Verification (RSV), a training-free framework that enables pre-trained Video-LLMs to autonomously determine when to respond through semantic state comparison. RSV also reduces spatiotemporal redundancy through frame dropping and token pruning, improving proactive responsiveness and inference efficiency without additional training or architectural modifications.
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Why is it important?
Existing online Video-LLMs often rely on reactive interactions or costly instruction tuning to achieve proactive responses, while struggling with spatiotemporal redundancy. Our work addresses these limitations through a training-free verification framework that enables autonomous response triggering and multi-granular spatiotemporal compression. This is important for developing scalable and efficient streaming Video-LLMs, with potential applications in autonomous driving, intelligent surveillance, and embodied AI.
Perspectives
I hope this work encourages researchers to explore training-free approaches for proactive online video understanding. I believe that enabling Video-LLMs to autonomously respond to meaningful semantic changes while reducing spatiotemporal redundancy is an important step toward more practical and efficient streaming assistants. I also hope RSV provides new insights into leveraging internal model representations for proactive decision-making and efficient inference.
Kairui Zhang
ShanghaiTech University
Read the Original
This page is a summary of: RSV: A Training-Free Verification Framework for Proactive and Efficient Online Video Understanding, November 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3767308.3836303.
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