Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models
The rapid growth of visual tokens in multimodal large language models (MLLMs) leads to excessive memory consumption and inference latency, especially when handling high-resolution images and videos. Token pruning is a technique used to mitigate this issue by removing redundancy, but existing methods often ignore relevance to the user query or suffer from the limitations of attention mechanisms, reducing their adaptability and effectiveness. To address these challenges, we propose Script, a plug-and-play pruning method that requires no retraining and generalizes across diverse MLLMs. Script comprises two modules: a graph-structured pruning module that removes visually redundant tokens, and a query-conditioned semantic pruning module that preserves query-relevant visual information. Together, they enhance performance on multimodal tasks. Experiments on fourteen benchmarks across image and video understanding tasks show that Script consistently achieves higher model efficiency and predictive accuracy compared to existing pruning methods. On LLaVA-NeXT-7B, it achieves up to 6.8x prefill speedup and 10x FLOP reduction, while retaining 96.88% of the original performance.
692e70a137312eaa83fd88a2
Zhongyu Yang
claimed_verified
2025-12-02T08:39:17.255Z
654f99f74c8874c64d4e5664
/avatars/e9da0d688f91ae49db91d0ebebb3782a.svg
Zhongyu Yang
yzzyu
user
yzzyu
692e70a137312eaa83fd88a3
Dannong Xu
692e70a137312eaa83fd88a4
Wei Pang
692e70a137312eaa83fd88a5
Yingfang Yuan
claimed_verified
2026-03-23T11:02:43.939Z
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/avatars/8a2b1b0fafe08051fd220a606e02fe3b.svg
James
Jamesyuan
user
Jamesyuan
Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models
The rapid growth of visual tokens in multimodal large language models (MLLMs) leads to excessive memory consumption and inference latency, especially when handling high-resolution images and videos. Token pruning is a technique used to mitigate this issue by removing redundancy, but existing methods often ignore relevance to the user query or suffer from the limitations of attention mechanisms, reducing their adaptability and effectiveness. To address these challenges, we propose Script, a plug-and-play pruning method that requires no retraining and generalizes across diverse MLLMs. Script comprises two modules: a graph-structured pruning module that removes visually redundant tokens, and a query-conditioned semantic pruning module that preserves query-relevant visual information. Together, they enhance performance on multimodal tasks. Experiments on fourteen benchmarks across image and video understanding tasks show that Script consistently achieves higher model efficiency and predictive accuracy compared to existing pruning methods. On LLaVA-NeXT-7B, it achieves up to 6.8x prefill speedup and 10x FLOP reduction, while retaining 96.88% of the original performance.
Script, a plug-and-play token pruning method for multimodal large language models, improves efficiency and accuracy by removing redundant and irrelevant visual tokens without retraining.
multimodal large language models visual tokens token pruning graph-structured pruning query-conditioned semantic pruning image understanding video understanding prefill speedup FLOP reduction