Omnimodal large language models (OmniLLMs) have attracted increasing research attention of late towards unified audio-video understanding. However, the high computational cost of processing longer joint audio-video token sequences has become a key bottleneck. Existing token compression methods have not addressed the emerging need to jointly compress multimodal tokens. To bridge this gap, we present OmniZip, a training-free, audio-guided audio-visual token-compression framework that optimizes multimodal token representation and accelerates model inference. Specifically, OmniZip first identifies salient audio tokens, then computes an audio retention score for each time group to capture information density, thereby dynamically guiding video token pruning and preserving cues from audio anchors enhanced by cross-modal similarity. For each time window, OmniZip compresses the video tokens using an interleaved spatio-temporal scheme. Extensive results demonstrate the merits of OmniZip: it achieves a 3.42X inference speedup and a 1.4X memory reduction over other top-performing counterparts, while maintaining the performance of OmniLLMs without training.
@article{arxiv.2511.14582,
title = {OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models},
author = {Keda Tao and Kele Shao and Bohan Yu and Weiqiang Wang and Jian liu and Huan Wang},
journal= {arXiv preprint arXiv:2511.14582},
year = {2026}
}