English(EN) DecoMoE: Decoupling Visual Propagation and Expert Computation for Efficient Multimodal MoE Inference

DecoMoE框架提升多模态MoE模型效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了DecoMoE，一个旨在提高多模态混合专家（MoE）模型效率的新型框架。该方法将视觉传播过程与专家计算解耦，解决了长视觉-Token序列带来的高推理成本问题。DecoMoE采用样本自适应视觉边界动态移除视觉Token，并通过路由校准专家前缀优化专家选择。在Qwen3-VL-MoE等模型上的评估表明，在保持原始性能的百分比的同时，计算量和延迟显著降低。 AI

影响降低了多模态MoE模型的计算负载和延迟，可能支持更广泛的部署。

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在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Xudong Tan, Peng Ye, Ming Xie, Chenyu Huang, Yaoxin Yang, Jiayuan Fan, Tao Chen · 2026-10-01 04:00

DecoMoE：解耦视觉传播与专家计算，实现高效多模态MoE推理

arXiv:2609.38823v1 Announce Type: new Abstract: Multimodal mixture-of-experts (MoE) models combine sparse expert activation with visual-language capabilities, yet their inference remains costly because long visual-token sequences repeatedly incur attention, routing, dispatch, and…

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报道来源 [1]

DecoMoE：解耦视觉传播与专家计算，实现高效多模态MoE推理

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