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

• URL: http://arxiv.org/abs/2602.04804v1
• Date: Wed, 04 Feb 2026 17:51:05 GMT
• Title: OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models
• Abstract summary: We propose OmniSIFT, a token compression framework tailored for Omni-LLMs.<n>The entire framework is optimized end-to-end via a differentiable straight-through estimator.<n>With merely 25% of the original token context, OmniSIFT consistently outperforms all compression baselines and even surpasses the performance of the full-token model on several tasks.
• Score: 42.615226139135174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 demonstrate the efficacy and robustness of OmniSIFT. Notably, for Qwen2.5-Omni-7B, OmniSIFT introduces only 4.85M parameters while maintaining lower latency than training-free baselines such as OmniZip. With merely 25% of the original token context, OmniSIFT consistently outperforms all compression baselines and even surpasses the performance of the full-token model on several tasks.

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