OmniMem compresses memory for long audio-video LLM inference

OmniMem addresses a bottleneck in audio-visual LLMs: long videos create heavy memory demands during streaming inference. The paper proposes perturbation-aware memory compression to keep long-form understanding more efficient.

That matters as multimodal models move from short clips and images toward meetings, lectures, surveillance footage, and always-on assistants. Long context is useful only if systems can afford to process it.

Memory compression is becoming one of the less flashy but essential parts of making multimodal AI practical at scale.