Mixture-of-Experts (MoE) models enable scalable performance but face severe memory constraints on edge devices. Existing offloading strategies struggle with I/O bottlenecks due to the dynamic, low-information nature of autoregressive expert activation. In this paper, we propose to repurpose Speculative Decoding (SD) not merely as a compute accelerator, but as an informative lookahead sensor for memory management, supported by our theoretical and empirical analyses. Hence, we introduce MoE-SpAc, an MoE inference framework that integrates a Speculative Utility Estimator to track expert demand, a Heterogeneous Workload Balancer to dynamically partition computation via online integer optimization, and an Asynchronous Execution Engine to unify the prefetching and eviction in the same utility space. Extensive experiments on seven benchmarks demonstrate that MoE-SpAc achieves a 42% improvement in TPS over the SOTA SD-based baseline, and an average 4.04x speedup over all standard baselines. Code is available at https://github.com/lshAlgorithm/MoE-SpAc .
Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE
Advanced Micro Devices, Inc.（超威半导体公司）；Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University（西安交通大学人工智能与机器人研究所）；University of HongKong（香港大学）
S2-MoE: Enabling Efficient Self-Speculative Decoding for Mixture-of-Experts on Edge Devices
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；School of Integrated Circuits, Peking University（北京大学集成电路学院）；School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
UNC-Chapel Hill（北卡罗来纳大学教堂山分校）；University of Toronto（多伦多大学）；Vector Institue（向量研究所）；MIT（麻省理工学院）；MIT-IBM Watson AI Lab（麻省理工-IBM沃森人工智能实验室）；Microsoft Research（微软研究院）；Mila（蒙特利尔人工智能研究院）
SD-MoE: Spectral Decomposition for Effective Expert Specialization
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China（复旦大学计算机科学与人工智能学院）；University of Bath, Bath, United Kingdom（巴斯大学）；Oxford Suzhou Centre for Advanced Research, Suzhou, China（牛津苏泽研究中心）；Department of Electrical Engineering and Computer Science, University of Michigan（密歇根大学电气工程与计算机科学系）；Shanghai Innovation Institute, Shanghai, China（上海创新研究院）；Department of Computer Science, University of Colorado Boulder, Colorado, USA（科罗拉多大学博尔德分校计算机科学系）；Research Institute of Tsinghua University in Shenzhen, Shenzhen, China（清华大学深圳研究院）；Greater Bay Area National Center of Technology Innovation, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China（粤港澳大湾区国家技术创新中心，清华大学深圳研究院）；School of Microelectronics, Fudan University, Shanghai, China（复旦大学微电子学院）
DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
University of Science and Technology of China（中国科学技术大学）；Tsinghua University（清华大学）；Nanyang Technological University（南洋理工大学）；University of California, Berkeley（加州大学伯克利分校）；University of Washington（华盛顿大学）
GazeFormer-MoE: Context-Aware Gaze Estimation via CLIP and MoE Transformer
AIPM, School of Artificial Intelligence, Guilin University of Electronic Technology, China（AIPM，人工智能学院，桂林电子科技大学，中国）；Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Canada（影像、视觉与人工智能实验室，École de Technologie Supérieure，加拿大）
Holistic Scaling Laws for Optimal Mixture-of-Experts Architecture Optimization
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Hi Lab, Xiaohongshu Inc.（小红书公司Hi实验室）；Project Numina（项目Numina）；School of Data Science, Fudan University（复旦大学数据科学学院）
