Cover Image

MoE Lens -- An Expert Is All You Need

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chaudhari, Marmik, Gulati, Idhant, Hundia, Nishkal, Karra, Pranav, Raval, Shivam
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Sparse Crosscoders for diffing MoEs and Dense models
by: Chaudhari, Marmik, et al.
Published: (2026)

Narrow Fine-Tuning Erodes Safety Alignment in Vision-Language Agents
by: Gulati, Idhant, et al.
Published: (2026)

Sparsity and Superposition in Mixture of Experts
by: Chaudhari, Marmik, et al.
Published: (2025)

Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing
by: Cong, Peizhuang, et al.
Published: (2024)

Harder Tasks Need More Experts: Dynamic Routing in MoE Models
by: Huang, Quzhe, et al.
Published: (2024)

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

Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training
by: Wang, Mengru, et al.
Published: (2025)

MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache
by: Xue, Leyang, et al.
Published: (2024)

MoE Pathfinder: Trajectory-driven Expert Pruning
by: Yang, Xican, et al.
Published: (2025)

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

$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts
by: Takashiro, Shota, et al.
Published: (2026)

MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing
by: Ma, Haiyue, et al.
Published: (2025)

DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models
by: Aghdam, Maryam Akhavan, et al.
Published: (2024)

FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving
by: Liu, Qingxiu, et al.
Published: (2026)

MoE-Spec: Expert Budgeting for Efficient Speculative Decoding
by: McDanel, Bradley, et al.
Published: (2026)

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

Exploiting the Experts: Unauthorized Compression in MoE-LLMs
by: Neogi, Pinaki Prasad Guha, et al.
Published: (2025)

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
by: Ma, Songkai, et al.
Published: (2025)

MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs
by: Xia, Xinfeng, et al.
Published: (2025)

MergeMoE: Efficient Compression of MoE Models via Expert Output Merging
by: Miao, Ruijie, et al.
Published: (2025)

MoNE: Replacing Redundant Experts with Lightweight Novices for Structured Pruning of MoE
by: Zhang, Geng, et al.
Published: (2025)

Fast MoE Inference via Predictive Prefetching and Expert Replication
by: Jyothish, Ankit, et al.
Published: (2026)

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE
by: Liu, Zongfang, et al.
Published: (2026)

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

SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts
by: Muzio, Alexandre, et al.
Published: (2024)

CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution
by: Son, Muyoung, et al.
Published: (2026)

SD-MoE: Spectral Decomposition for Effective Expert Specialization
by: Huang, Ruijun, et al.
Published: (2026)

SDG-MoE: Signed Debate Graph Mixture-of-Experts
by: Kulibaba, Stepan, et al.
Published: (2026)

Expert Divergence Learning for MoE-based Language Models
by: Li, Jiaang, et al.
Published: (2026)

Mixture of Experts (MoE): A Big Data Perspective
by: Gan, Wensheng, et al.
Published: (2025)

Steering MoE LLMs via Expert (De)Activation
by: Fayyaz, Mohsen, et al.
Published: (2025)

FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing
by: Zhang, Boyang, et al.
Published: (2025)

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

AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference
by: Zhong, Shuzhang, et al.
Published: (2024)

DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient MoE Inference
by: Zhang, Yujie, et al.
Published: (2024)

HI-MoE: Hierarchical Instance-Conditioned Mixture-of-Experts for Object Detection
by: Vashkelis, Vadim, et al.
Published: (2026)

Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization
by: Hu, Rizhen, et al.
Published: (2026)

Anchor-MoE: A Mean-Anchored Mixture of Experts For Probabilistic Regression
by: Su, Baozhuo, et al.
Published: (2025)

MoE-Beyond: Learning-Based Expert Activation Prediction on Edge Devices
by: Gavhane, Nishant, et al.
Published: (2025)

FT-MoE: Sustainable-learning Mixture of Experts for Fault-Tolerant Computing
by: Xiao, Wenjing, et al.
Published: (2025)