OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models
arXiv:2607.03050v1 Announce Type: cross Abstract: Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate large token sequences under audio-visual inputs, leading to substantial inference cost. Existing audio-visual token compression methods often rely on unimodal guidance, overlooking the temporal locality of query-relevant evidence in audio-visual inputs and implicitly assuming that the two modalities share a temporally aligned information density distribution. We propose \textbf{OmniFocus}, a training-
The rapid advancement and adoption of Omni-modal Large Language Models are driving the urgent need for more efficient processing methods to handle growing data complexity and computational costs.
This research addresses a key computational bottleneck for advanced AI systems, enabling more scalable and practical deployment of multimodal AI that can process diverse inputs like audio and video.
The ability to efficiently compress and process multimodal tokens will reduce inference costs and latency, making sophisticated OmniLLMs more viable for real-world applications and less constrained by compute infrastructure.
- · AI compute providers
- · Multimodal AI developers
- · Cloud infrastructure providers
- · Edge AI developers
- · Companies with inefficient multimodal AI architectures (without compression)
- · Small-scale AI developers without access to efficient compression methods
Reduced computational costs for multimodal AI will accelerate wider adoption of sophisticated AI systems across various industries.
This efficiency gain could lead to a proliferation of AI applications that integrate seamlessly with human senses, impacting fields from robotics to remote work.
The development of more efficient AI inference could reduce the overall energy footprint of AI, potentially alleviating some pressure on the energy bottleneck.
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Read at arXiv cs.AI