Adaptive Two-Stage Token Pruning for Efficient Video-Language Models

A new research paper on arXiv (2608.03112v1) proposes an adaptive two-stage token pruning strategy to reduce inference latency in vision-language models (VLMs) for video understanding. The method addresses the high computational cost of processing thousands of tokens per frame, which is especially problematic for real-time surveillance and edge devices. Existing token reduction techniques are designed for single images and fail to exploit temporal redundancies across video frames. Moreover, they use a fixed pruning ratio, which is suboptimal as redundancy varies across videos. The proposed approach adapts pruning levels based on content, aiming to preserve critical information while improving efficiency. The paper is authored by researchers and is available on arXiv, indicating a cross-listing type. This work contributes to the field of efficient AI inference, with potential applications in video analytics and on-device AI.