arXiv:2603.15685v2 Announce Type: replace-cross Abstract: Omnimodal large language models (OmniLLMs) jointly process audio and visual streams, but the resulting long multimodal token sequences make inference prohibitively expensive. Existing compression methods typically rely on fixed window partitioning and attention-based pruning, which overlook the piecewise semantic structure of audio-visual signals and become fragile under aggressive token reduction. We propose Dynamic Audio-driven Semantic cHunking (DASH), a training-free framework that aligns token compression with semantic structure. D
The proliferation of omnimodal large language models is creating significant computational bottlenecks, necessitating immediate solutions for efficient processing.
This development addresses a core technical hurdle in scaling advanced AI models, making them more practical and economical for widespread deployment.
Omnimodal LLMs can now process longer and more complex audio-visual sequences with significantly reduced computational cost, potentially accelerating their adoption and capability growth.
- · AI model developers
- · Cloud computing providers
- · Companies using visual/audio AI
- · End-users of omnimodal AI applications
- · Companies reliant on less efficient compression methods
- · High-latency real-time AI applications
Reduced inference costs for omnimodal AI models make them more accessible and competitive.
This efficiency gain could lead to a rapid expansion of AI applications integrating audio and visual data.
More sophisticated and real-time omnimodal AI systems could emerge, influencing new user interfaces and autonomous systems.
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Read at arXiv cs.AI