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Mixture of Experts (MoE) is a machine learning technique that uses multiple expert models to make predictions. Each expert specializes in different aspects of the data, and a gating network determines which expert to use for a given input. This approach can improve model performance and efficiency.
 Towards Principled Design of Mixture-of-Experts Language Models under Memory and Inference Constraints Seng Pei Liew Kenta Shinzato Yuyang Dong | |||
 Ministral 3 Alexander H. Liu Kartik Khandelwal Sandeep Subramanian Victor Jouault Abhinav Rastogi ...Thibaut Lavril Thiziri Nait Saada Thomas Chabal Thomas Foubert Thomas Robert | |||
 Deconstructing Pre-training: Knowledge Attribution Analysis in MoE and Dense Models Bo Wang Junzhuo Li Hong Chen Yuanlin Chu Yuxuan Fan Xuming Hu | |||
 Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models Xin Cheng Wangding Zeng Damai Dai Qinyu Chen Bingxuan Wang ...Yukun Li Han Zhang Huishuai Zhang Dongyan Zhao Wenfeng Liang | |||
 Towards Specialized Generalists: A Multi-Task MoE-LoRA Framework for Domain-Specific LLM Adaptation Yuxin Yang Aoxiong Zeng Xiangquan Yang | |||
 High-Rank Structured Modulation for Parameter-Efficient Fine-Tuning Yongkang Liu Xing Li Mengjie Zhao Shanru Zhang Zijing Wang Qian Li Shi Feng Feiliang Ren Daling Wang Hinrich Schütze | |||
 SecMoE: Communication-Efficient Secure MoE Inference via Select-Then-Compute Bowen Shen Yuyue Chen Peng Yang Bin Zhang Xi Zhang Zoe L. Jiang | |||
 MoE-DisCo:Low Economy Cost Training Mixture-of-Experts Models Xin Ye Daning Cheng Boyang Zhang Yunquan Zhang | |||
 Monkey Jump : MoE-Style PEFT for Efficient Multi-Task Learning Nusrat Jahan Prottasha Md Kowsher Chun-Nam Yu Chen Chen Ozlem Garibay | |||
 DR-LoRA: Dynamic Rank LoRA for Mixture-of-Experts Adaptation Guanzhi Deng Bo Li Ronghao Chen Huacan Wang Linqi Song Lijie Wen | |||
 MoE3D: A Mixture-of-Experts Module for 3D Reconstruction Zichen Wang Ang Cao Liam J. Wang Jeong Joon Park | |||
 A Scheduling Framework for Efficient MoE Inference on Edge GPU-NDP Systems Qi Wu Chao Fang Jiayuan Chen Ye Lin Yueqi Zhang Yichuan Bai Yuan Du Li Du | |||
 MiMo-V2-Flash Technical Report Xiaomi LLM-Core Team Bangjun Xiao Bingquan Xia Bo Yang Bofei Gao ...Shicheng Li Shuhao Gu Shuhuai Ren Sirui Deng Tao Guo | |||
 The Illusion of Specialization: Unveiling the Domain-Invariant "Standing Committee" in Mixture-of-Experts Models Yan Wang Yitao Xu Nanhan Shen Jinyan Su Jimin Huang Zining Zhu | |||
 MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free Yishu Lei Shuwei He Jing Hu Dan Zhang Xianlong Luo ...Rui Liu Jingzhou He Yu Sun Hua Wu Haifeng Wang | |||
 K-EXAONE Technical Report Eunbi Choi Kibong Choi Seokhee Hong Junwon Hwang Hyojin Jeon ...Sihyuk Yi Chansik Yoon Dongkeun Yoon Sangyeon Yoon Hyeongu Yun | |||
 Routing by Analogy: kNN-Augmented Expert Assignment for Mixture-of-Experts Boxuan Lyu Soichiro Murakami Hidetaka Kamigaito Peinan Zhang | |||
 Varying-Coefficient Mixture of Experts Model Qicheng Zhao Celia M.T. Greenwood Qihuang Zhang | |||
 Yuan3.0 Flash: An Open Multimodal Large Language Model for Enterprise Applications YuanLab.ai Shawn Wu Sean Wang Louie Li Darcy Chen ...James Gong Danied Zhao Penn Zheng Owen Zhu Tong Yu | |||
 Making MoE-based LLM Inference Resilient with Tarragon Songyu Zhang Aaron Tam Myungjin Lee Shixiong Qi K. K. Ramakrishnan | |||
 HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts Zihan Fang Zheng Lin Senkang Hu Yanan Ma Yihang Tao Yiqin Deng Xianhao Chen Yuguang Fang | |||
 Traffic-MoE: A Sparse Foundation Model for Network Traffic Analysis Jiajun Zhou Changhui Sun Meng Shen Shanqing Yu Qi Xuan | |||
 RepetitionCurse: Measuring and Understanding Router Imbalance in Mixture-of-Experts LLMs under DoS Stress Ruixuan Huang Qingyue Wang Hantao Huang Yudong Gao Dong Chen Shuai Wang Wei Wang | |||
 Training Report of TeleChat3-MoE Xinzhang Liu Chao Wang Zhihao Yang Zhuo Jiang Xuncheng Zhao ...Teng Su Xin Jiang Shuangyong Song Yongxiang Li Xuelong Li | |||
 Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss Ang Lv Jin Ma Yiyuan Ma Siyuan Qiao | |||
 Text-Routed Sparse Mixture-of-Experts Model with Explanation and Temporal Alignment for Multi-Modal Sentiment Analysis Dongning Rao Yunbiao Zeng Zhihua Jiang Jujian Lv | |||
 FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment Boyang Zhang Xiaobing Chen Songyang Zhang Shuai Zhang Xiangwei Zhou Mingxuan Sun | |||
 Accelerate Speculative Decoding with Sparse Computation in Verification Jikai Wang Jianchao Tan Yuxuan Hu Jiayu Qin Yerui Sun Yuchen Xie Xunliang Cai Juntao Li Min Zhang | |||
 InstructMoLE: Instruction-Guided Mixture of Low-rank Experts for Multi-Conditional Image Generation Jinqi Xiao Qing Yan Liming Jiang Zichuan Liu Hao Kang ...Tiancheng Zhi Jing Liu Cheng Yang Xin Lu Bo Yuan | |||
 MoRAgent: Parameter Efficient Agent Tuning with Mixture-of-RolesInternational Conference on Machine Learning (ICML), 2025 Jing Han Binwei Yan Tianyu Guo Zheyuan Bai Mengyu Zheng Hanting Chen Ying Nie | |||
 Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism Xinglin Pan Shaohuai Shi Wenxiang Lin Yuxin Wang Zhenheng Tang Wei Wang Xiaowen Chu | |||
 RevFFN: Memory-Efficient Full-Parameter Fine-Tuning of Mixture-of-Experts LLMs with Reversible Blocks Ningyuan Liu Jing Yang Kaitong Cai Keze Wang | |||
 Mixture of Experts in Large Language Models Danyang Zhang Junhao Song Ziqian Bi Xinyuan Song Yingfang Yuan Tianyang Wang Joe Yeong Junfeng Hao | |||
 GateBreaker: Gate-Guided Attacks on Mixture-of-Expert LLMs Lichao Wu Sasha Behrouzi Mohamadreza Rostami Stjepan Picek Ahmad-Reza Sadeghi | |||
 Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI ArchitecturesInternational Symposium on Computer Architecture (ISCA), 2025 | |||
 MoE-DiffuSeq: Enhancing Long-Document Diffusion Models with Sparse Attention and Mixture of Experts Alexandros Christoforos Chadbourne Davis | |||
 Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning NVIDIA Aaron Blakeman Aaron Grattafiori Aarti Basant Abhibha Gupta ...Ferenc Galko Frankie Siino Gal Hubara Agam Ganesh Ajjanagadde Gantavya Bhatt | |||
 Mirage Persistent Kernel: A Compiler and Runtime for Mega-Kernelizing Tensor Programs Xinhao Cheng Zhihao Zhang Yu Zhou Jianan Ji Jinchen Jiang ...Songting Wang Wenqin Yang Xupeng Miao Tianqi Chen Zhihao Jia | |||
 Remoe: Towards Efficient and Low-Cost MoE Inference in Serverless Computing Wentao Liu Yuhao Hu Ruiting Zhou Baochun Li Ne Wang | |||
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 Sigma-MoE-Tiny Technical Report Qingguo Hu Zhenghao Lin Ziyue Yang Yucheng Ding Xiao Liu ...Rui Gao Lei Qu Jinsong Su Peng Cheng Yeyun Gong | |||
 LoPA: Scaling dLLM Inference via Lookahead Parallel Decoding Chenkai Xu Yijie Jin Jiajun Li Yi Tu Guoping Long ...Mingcong Song Hongjie Si Tianqi Hou Junchi Yan Zhijie Deng | |||
 Bandwidth-Efficient Adaptive Mixture-of-Experts via Low-Rank Compensation Zhenyu Liu Yunzhen Liu Zehao Fan Garrett Gagnon Yayue Hou Nan Wu Yangwook Kang Liu Liu | |||
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