Omni-Prune: Query-Aware Unified Token Pruning for Efficient Omnimodal Large Language Models
Omnimodal large language models (OmniLLMs) are rapidly extending multimodal reasoning to cover synchronized audio and video. However, the resulting audio-video token sequences are long, leading to high prefill latency and GPU memory usage at inference time. Existing token pruning methods, designed mainly for vision-only inputs, miss both the cross-modal links between audio and video and the user query that decides which content matters. To bridge this gap, we present Omni-Prune, a training-free, query-aware audio-visual token pruning framework that jointly removes redundancy from both modalities while keeping task-relevant cross-modal evidence. Specifically, Omni-Prune first splits the to...
| Authors | Yiming Zhong, Chang Nie, Caifeng Shan |
|---|---|
| Primary category | cs.CV |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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