Poster Wed, Jul 8, 2026 • 2:30 PM – 4:15 PM KST Coex: HALL A

OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models

Yue Ding ⋅ Yiyan Ji ⋅ Jungang Li ⋅ Xuyang Liu ⋅ Xinlong Chen ⋅ Junfei Wu ⋅ Bozhou Li ⋅ Bohan Zeng ⋅ Yang Shi ⋅ Yushuo Guan ⋅ Yuanxing Zhang ⋅ Jiaheng Liu ⋅ Qiang Liu ⋅ Pengfei Wan ⋅ Liang Wang

Abstract

Omni-modal Large Language Models (Omni-LLMs) have demonstrated strong capabilities in audio-video understanding tasks. However, their reliance on long multimodal token sequences leads to substantial computational overhead. Despite this challenge, token compression methods designed for Omni-LLMs remain limited. To bridge this gap, we propose OmniSIFT (Omni-modal Spatio-temporal Informed Fine-grained Token compression), a modality-asymmetric token compression framework tailored for Omni-LLMs. Specifically, OmniSIFT adopts a two-stage compression strategy: (i) a spatio-temporal video pruning module that removes video redundancy arising from both intra-frame structure and inter-frame overlap, and (ii) a vision-guided audio selection module that filters audio tokens. The entire framework is optimized end-to-end via a differentiable straight-through estimator. Extensive experiments on five representative benchmarks verify the efficacy and robustness of OmniSIFT. Notably, for Qwen2.5-Omni-7B, OmniSIFT adds 4.85M parameters while still achieving lower latency than training-free baselines such as OmniZip. With only 25% of the original token context, OmniSIFT consistently outperforms all compression baselines and even surpasses the full-token model on several tasks.