arXiv:2606.07577v1 Announce Type: new Abstract: Audio-visual large language models (LLMs) hold strong promise for long-form video understanding, yet their long-video inference is fundamentally limited by the linear growth of video tokens and key-value (KV) caches. We present OmniMem, a memory-efficient streaming framework designed specifically for audio-visual LLMs. Unlike existing compression methods that treat all tokens uniformly, OmniMem introduces a modality-aware memory allocation strategy that separately manages visual and audio contexts, addressing the severe token imbalance between th
The increasing complexity and length of video data are pushing the limits of current LLM architectures, creating an immediate need for more efficient memory management techniques.
This development addresses a fundamental limitation in long-form video understanding for audio-visual LLMs, which is critical for their broader adoption in applications like surveillance, content creation, and autonomous systems.
The ability of audio-visual LLMs to process and understand long-duration video streams without prohibitive memory costs is enhanced, making advanced applications more feasible.
- · AI compute infrastructure providers
- · Developers of long-form video AI applications
- · Cloud service providers
- · Companies using LLMs for video analytics
- · Companies reliant on conventional, unoptimized LLM architectures
- · Providers of less efficient video processing solutions
Audio-visual LLMs become more practical and cost-effective for analyzing extended video content.
This efficiency gain could accelerate the development and deployment of autonomous AI agents capable of understanding complex, dynamic environments over long periods.
Improved long-term video understanding could lead to new forms of societal monitoring, creative content generation, and immersive digital experiences.
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Read at arXiv cs.AI