:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Gu, Yupu, Wei, Rongzhe, Zhu, Andy, Li, Pan
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2602.10965
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GRIP: Algorithm-Agnostic Machine Unlearning for Mixture-of-Experts via Geometric Router Constraints
by: Zhu, Andy, et al.
Published: (2026)

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging
by: Li, Lujun, et al.
Published: (2025)

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing
by: Wang, Ziteng, et al.
Published: (2024)

Scalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured Updates
by: Maksimov, Roman, et al.
Published: (2026)

Routing Manifold Alignment Improves Generalization of Mixture-of-Experts LLMs
by: Li, Zhongyang, et al.
Published: (2025)

MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
by: Chen, Xiaodong, et al.
Published: (2025)

Stable Routing for Mixture-of-Experts in Class-Incremental Learning
by: Guo, Zirui, et al.
Published: (2026)

RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs
by: Xu, Zhiyuan, et al.
Published: (2026)

CartesianMoE: Boosting Knowledge Sharing among Experts via Cartesian Product Routing in Mixture-of-Experts
by: Su, Zhenpeng, et al.
Published: (2024)

Maximum Score Routing For Mixture-of-Experts
by: Dong, Bowen, et al.
Published: (2025)

Efficiently Editing Mixture-of-Experts Models with Compressed Experts
by: He, Yifei, et al.
Published: (2025)

ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts
by: Zhao, Heng, et al.
Published: (2026)

PreMoE: Proactive Inference for Efficient Mixture-of-Experts
by: Pei, Zehua, et al.
Published: (2025)

Differentially Private Graph Diffusion with Applications in Personalized PageRanks
by: Wei, Rongzhe, et al.
Published: (2024)

MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)

MoETuner: Optimized Mixture of Expert Serving with Balanced Expert Placement and Token Routing
by: Go, Seokjin, et al.
Published: (2025)

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs
by: Zhang, Ze Yu, et al.
Published: (2025)

Model Generalization on Text Attribute Graphs: Principles with Large Language Models
by: Wang, Haoyu, et al.
Published: (2025)

Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs
by: Jiang, Yukun, et al.
Published: (2026)

Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
by: Liu, Baihui, et al.
Published: (2026)

Expert Routing for Communication-Efficient MoE via Finite Expert Banks
by: Salehi, Mohammad Reza Deylam, et al.
Published: (2026)

MoDE: A Mixture-of-Experts Model with Mutual Distillation among the Experts
by: Xie, Zhitian, et al.
Published: (2024)

MoE-nD: Per-Layer Mixture-of-Experts Routing for Multi-Axis KV Cache Compression
by: Sun, Libo, et al.
Published: (2026)

Mixture Compressor for Mixture-of-Experts LLMs Gains More
by: Huang, Wei, et al.
Published: (2024)

Routing-Free Mixture-of-Experts
by: Liu, Yilun, et al.
Published: (2026)

Multilingual Routing in Mixture-of-Experts
by: Bandarkar, Lucas, et al.
Published: (2025)

ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration
by: Ai, Mengting, et al.
Published: (2025)

Not All Models Suit Expert Offloading: On Local Routing Consistency of Mixture-of-Expert Models
by: Liang, Jingcong, et al.
Published: (2025)

When Are Experts Misrouted? Counterfactual Routing Analysis in Mixture-of-Experts Language Models
by: Yoon, Youngsik, et al.
Published: (2026)

Input Domain Aware MoE: Decoupling Routing Decisions from Task Optimization in Mixture of Experts
by: Hua, Yongxiang, et al.
Published: (2025)

Multi-Task Vehicle Routing Solver via Mixture of Specialized Experts under State-Decomposable MDP
by: Pan, Yuxin, et al.
Published: (2025)

PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model
by: Liu, Yilun, et al.
Published: (2024)

Privately Learning from Graphs with Applications in Fine-tuning Large Language Models
by: Yin, Haoteng, et al.
Published: (2024)

CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning
by: Feng, Jinyuan, et al.
Published: (2025)

LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
by: Liu, Xinyi, et al.
Published: (2026)

Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules
by: Liu, Yilun, et al.
Published: (2025)

MoESD: Mixture of Experts Stable Diffusion to Mitigate Gender Bias
by: Wang, Guorun, et al.
Published: (2024)

Horseshoe Mixtures-of-Experts (HS-MoE)
by: Polson, Nick, et al.
Published: (2026)

Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning
by: Cao, Haifang, et al.
Published: (2026)

MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models
by: Tastan, Nurbek, et al.
Published: (2026)