#token-pruning

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papersSEP 10 04:00 UTC

Sample-Adaptive Strategy Routing Improves Vision Token Pruning in Multimodal LLMs

A new arXiv preprint proposes choosing among vision token pruning strategies on a per-image basis rather than applying one fixed policy to every input. By adapting the pruning approach to each sample, the method aims to lower the heavy inference costs that multimodal language models incur from processing large numbers of visual tokens while maintaining output quality.

multimodal/efficiency/token-pruning/research

papersSEP 10 04:00 UTC

Predicting Middle-Layer Attention in Multimodal LLMs for Efficient Visual Token Pruning

Multimodal large language models spend significant compute processing large numbers of visual tokens, and effective pruning depends on knowing which tokens actually matter. This paper introduces a learned approach that predicts attention at middle layers, enabling models to identify and drop less relevant visual tokens. The method aims to cut inference costs while maintaining performance across vision-language tasks.

multimodal/efficiency/token-pruning/attention