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English(EN) RUTA: Principled Visual Token Allocation via Rate-Utility Optimization

RUTA方法在保持性能的同时大幅减少了LLM的视觉标记

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了RUTA，一种用于减少大型语言模型处理的视觉标记数量的新颖方法。RUTA学习根据查询特定相关性和速率-效用目标来选择和分配标记，平衡任务性能与计算成本。在评估中，RUTA在LLaVA-NeXT-7B和Qwen3-VL-8B等模型上显著减少了标记使用量，同时保留了高比例的任务性能。 AI

影响 该方法可以显著降低使用高分辨率图像和长视频的视觉语言模型的计算成本。

排序理由 arXiv上发表了一篇关于视觉标记分配新颖方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

RUTA方法在保持性能的同时大幅减少了LLM的视觉标记

本文如何被排名

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Newsworthiness bucket
Tool
arXiv上发表了一篇关于视觉标记分配新颖方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, infra
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jian Zou, Xiaoyu Xu, Zhihua Wang, Yilin Wang, Balu Adsumilli, Kede Ma ·

    RUTA：通过速率-效用优化实现原则性的视觉令牌分配

    arXiv:2608.04132v1 Announce Type: new Abstract: High-resolution images and long videos provide vision-language models with rich context for multimodal reasoning and fine-grained perception, but the resulting long visual token sequences make large language model-side computation a…

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