English(EN) StepPrune: Adaptive Sequential Visual Token Selection across Multimodal Large Language Models

StepPrune方法通过自适应选择视觉标记来加速多模态大语言模型推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种新颖的StepPrune方法，用于在多模态大语言模型（MLLMs）中自适应地选择视觉标记以加速推理。与之前独立处理标记的Top-K方法不同，StepPrune根据先前选择的标记和文本上下文来顺序选择标记，动态确定要保留的标记数量。该方法在LLaVA-1.5上已显示出显著的性能保留，实现了94.6%的完整前缀归一化性能，同时删除了88.9%的视觉标记。该方法还实现了1.50倍的预填充速度提升，将延迟从59.95毫秒降低到40.05毫秒。 AI

影响这种自适应标记选择方法可以显著加快多模态大语言模型的推理速度，从而实现更高效的实时应用。

排序理由该集群包含一篇详细介绍多模态大语言模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Hansen Zhang, Landi He, Mingde Yao, Lijian Xu · 2026-09-15 04:00

StepPrune：多模态大语言模型中的自适应顺序视觉令牌选择

arXiv:2609.13804v1 Announce Type: new Abstract: Visual prefixes account for a major portion of the per-layer computation in multimodal large language models (MLLMs), making visual-token pruning a direct approach to accelerating inference. Existing top-K methods typically evaluate…

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报道来源 [1]

StepPrune：多模态大语言模型中的自适应顺序视觉令牌选择

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