English(EN) Efficient Omni-modal Large Language Model Inference via Per-Query Modality Selection

全模态LLM通过每查询模态选择优化推理成本

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一种方法，通过为每个查询自适应地选择要使用的模态来优化全模态大语言模型（Omni-LLMs）的推理成本。该方法被表述为一个准确率-成本优化问题，并在WorldSense数据集上使用Qwen2.5-Omni和Qwen3-Omni模型进行了评估。结果表明，按查询进行模态选择显著改善了准确率-成本权衡，在提供所有模态的情况下实现了相当或更好的准确率，同时大幅减少了输入Token数、延迟和金钱成本。 AI

影响这种方法可能导致多模态LLM更高效的部署和使用，降低计算成本和延迟。

排序理由详细介绍一种新颖的LLM推理优化方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

本文如何被排名

Signal score: 8 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍一种新颖的LLM推理优化方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; 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; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Shuqi Liu, Yuzhou Cao, Luke Ong · 2026-10-27 04:00

通过每查询模态选择实现高效全模态大语言模型推理

arXiv:2610.31768v1 Announce Type: new Abstract: Omni-modal large language models (Omni-LLMs) can jointly process a broad range of modalities, such as text, audio, and video, enabling stronger multimodal understanding and reasoning capabilities, but often at substantial inference …

本文如何被排名

报道来源 [1]

通过每查询模态选择实现高效全模态大语言模型推理

相关实体

相关话题