@inproceedings{gan-etal-2026-pcomoe,
title = "{PC}o{M}o{E}: Shifting {M}o{E} Inference from Monolithic Expert Selection to Fine-Grained Path Composition",
author = "Gan, Ziyan and
Liu, Fangxin and
Guan, Chenyang and
Wang, Junjie and
Yang, Ning and
Li, Haomin and
Li, Xiang and
Yang, Siran and
Wang, Jiamang and
Qu, Lin and
Wang, Zongwu and
Jiang, Li and
Guan, Haibing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2048/",
pages = "46662--46672",
ISBN = "979-8-89176-459-0",
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 abstraction. Existing frameworks manage, schedule, or prune experts as atomic execution units, which fixes the optimization boundary too early and leaves fine-grained intra-expert computational redundancy underexplored. In this work, we present PCoMoE, a path-compositional execution framework that shifts MoE inference from coarse-grained expert selection to fine-grained path composition. PCoMoE incorporates a path-level formulation of expert computation, a compatibility-aware layer-wise pruning strategy to suppress low-value path combinations, and a hardware-friendly execution engine to exploit reusable sub-expert structures under strictly bounded overheads. Experimental results demonstrate that PCoMoE achieves up to a 1.31x end-to-end inference speedup while enhancing model accuracy by 10{\%}. The code is available at https://github.com/gzyyy0/PCoMoE"
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<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 abstraction. Existing frameworks manage, schedule, or prune experts as atomic execution units, which fixes the optimization boundary too early and leaves fine-grained intra-expert computational redundancy underexplored. In this work, we present PCoMoE, a path-compositional execution framework that shifts MoE inference from coarse-grained expert selection to fine-grained path composition. PCoMoE incorporates a path-level formulation of expert computation, a compatibility-aware layer-wise pruning strategy to suppress low-value path combinations, and a hardware-friendly execution engine to exploit reusable sub-expert structures under strictly bounded overheads. Experimental results demonstrate that PCoMoE achieves up to a 1.31x end-to-end inference speedup while enhancing model accuracy by 10%. The code is available at https://github.com/gzyyy0/PCoMoE</abstract>
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%0 Conference Proceedings
%T PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition
%A Gan, Ziyan
%A Liu, Fangxin
%A Guan, Chenyang
%A Wang, Junjie
%A Yang, Ning
%A Li, Haomin
%A Li, Xiang
%A Yang, Siran
%A Wang, Jiamang
%A Qu, Lin
%A Wang, Zongwu
%A Jiang, Li
%A Guan, Haibing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F gan-etal-2026-pcomoe
%X 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 abstraction. Existing frameworks manage, schedule, or prune experts as atomic execution units, which fixes the optimization boundary too early and leaves fine-grained intra-expert computational redundancy underexplored. In this work, we present PCoMoE, a path-compositional execution framework that shifts MoE inference from coarse-grained expert selection to fine-grained path composition. PCoMoE incorporates a path-level formulation of expert computation, a compatibility-aware layer-wise pruning strategy to suppress low-value path combinations, and a hardware-friendly execution engine to exploit reusable sub-expert structures under strictly bounded overheads. Experimental results demonstrate that PCoMoE achieves up to a 1.31x end-to-end inference speedup while enhancing model accuracy by 10%. The code is available at https://github.com/gzyyy0/PCoMoE
%U https://aclanthology.org/2026.emnlp-main.2048/
%P 46662-46672
Markdown (Informal)
[PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition](https://aclanthology.org/2026.emnlp-main.2048/) (Gan et al., EMNLP 2026)
ACL
- Ziyan Gan, Fangxin Liu, Chenyang Guan, Junjie Wang, Ning Yang, Haomin Li, Xiang Li, Siran Yang, Jiamang Wang, Lin Qu, Zongwu Wang, Li Jiang, and Haibing Guan. 2026. PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 46662–46672, Budapest, Hungary. Association for Computational Linguistics.