English(EN) PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition

PCoMoE 框架通过细粒度路径组合将 MoE LLM 推理速度提升 31%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员推出了一种名为 PCoMoE 的新颖框架，旨在提高专家混合（MoE）大型语言模型（LLM）的推理效率。与将整个专家视为原子单元的传统方法不同，PCoMoE 实现了细粒度的路径组合，从而能够进行更灵活、更高效的计算。该方法包含一个路径级公式、一种兼容性感知剪枝策略以消除冗余路径组合，以及一个专门的执行引擎。实验表明，PCoMoE 可将推理速度提高高达 1.31 倍，同时将模型准确率提高 10%。 AI

影响提高了 LLM 的推理效率，可能带来更快、更准确的模型部署。

排序理由该集群包含一篇详细介绍 LLM 新技术框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Ziyan Gan, Fangxin Liu, Chenyang Guan, Junjie Wang, Ning Yang, Haomin Li, Xiang Li, Siran Yang, Jiamang Wang, Lin Qu, Zongwu Wang, Li Jiang, Haibing Guan · 2026-09-02 04:00

PCoMoE：将 MoE 推理从单体专家选择转向细粒度路径组合

arXiv:2609.01024v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures scale Large Language Model (LLM) capacity efficiently by activating a sparse subset of experts per token. However, modern MoE inference remains heavily constrained by the rigid, whole-expert ab…

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报道来源 [1]

PCoMoE：将 MoE 推理从单体专家选择转向细粒度路径组合

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