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AdaVSkip

model1 events
papersTODAY 04:00 UTC

AdaVSkip Method Skips Visual Tokens Across Layers to Speed Up Multimodal LLM Inference

A new arXiv paper introduces AdaVSkip, a technique that reduces the number of visual tokens processed at each transformer layer to lower the cost of multimodal large language model inference. Rather than only compressing tokens along the sequence dimension, the approach adapts skipping decisions per layer. The work targets efficiency gains without retraining the underlying model.

multimodal/efficiency/inference/research