SD-MoE: Spectral Decomposition for Effective Expert Specialization
Fuente:
arXiv
Saved in:
| Main Authors: | Huang, Ruijun, Dong, Fang, Zhang, Xin, Cao, Hengjie, Huang, Zhendong, Chen, Anrui, Zhou, Jixian, Chen, Mengyi, Yang, Yifeng, Dong, Mingzhi, Wang, Yujiang, Hou, Jinlong, Lv, Qin, Dick, Robert P., Cheng, Yuan, Yang, Fan, Lu, Tun, Zhang, Chun, Shang, Li |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Multi-Head Attention as a Source of Catastrophic Forgetting in MoE Transformers
by: Chen, Anrui, et al.
Published: (2026)
by: Chen, Anrui, et al.
Published: (2026)
Spectra: Rethinking Optimizers for LLMs Under Spectral Anisotropy
by: Huang, Zhendong, et al.
Published: (2026)
by: Huang, Zhendong, et al.
Published: (2026)
The Curse and Blessing of Mean Bias in FP4-Quantized LLM Training
by: Cao, Hengjie, et al.
Published: (2026)
by: Cao, Hengjie, et al.
Published: (2026)
Metis: Training LLMs with FP4 Quantization
by: Cao, Hengjie, et al.
Published: (2025)
by: Cao, Hengjie, et al.
Published: (2025)
Dispelling the Curse of Singularities in Neural Network Optimizations
by: Cao, Hengjie, et al.
Published: (2026)
by: Cao, Hengjie, et al.
Published: (2026)
Advancing Expert Specialization for Better MoE
by: Guo, Hongcan, et al.
Published: (2025)
by: Guo, Hongcan, et al.
Published: (2025)
Harder Tasks Need More Experts: Dynamic Routing in MoE Models
by: Huang, Quzhe, et al.
Published: (2024)
by: Huang, Quzhe, et al.
Published: (2024)
Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
by: Wu, Haoyuan, et al.
Published: (2025)
by: Wu, Haoyuan, et al.
Published: (2025)
MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition
by: Yang, Cheng, et al.
Published: (2024)
by: Yang, Cheng, et al.
Published: (2024)
SiftMoE: Similarity-Aware Energy-Efficient Expert Selection for Wireless Distributed MoE Inference
by: Chen, Qian, et al.
Published: (2026)
by: Chen, Qian, et al.
Published: (2026)
VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting
by: Chen, Hao, et al.
Published: (2024)
by: Chen, Hao, et al.
Published: (2024)
DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurance
by: Zhang, Yuning, et al.
Published: (2025)
by: Zhang, Yuning, et al.
Published: (2025)
Effective MoE-based LLM Compression by Exploiting Heterogeneous Inter-Group Experts Routing Frequency and Information Density
by: Mi, Zhendong, et al.
Published: (2026)
by: Mi, Zhendong, et al.
Published: (2026)
MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
by: Chen, Xiaodong, et al.
Published: (2025)
by: Chen, Xiaodong, et al.
Published: (2025)
Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Latent Generation
by: Wang, Chenyu, et al.
Published: (2024)
by: Wang, Chenyu, et al.
Published: (2024)
Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models
by: Shi, Yubin, et al.
Published: (2024)
by: Shi, Yubin, et al.
Published: (2024)
MoNE: Replacing Redundant Experts with Lightweight Novices for Structured Pruning of MoE
by: Zhang, Geng, et al.
Published: (2025)
by: Zhang, Geng, et al.
Published: (2025)
Janus: Disaggregating Attention and Experts for Scalable MoE Inference
by: Zhang, Zhexiang, et al.
Published: (2025)
by: Zhang, Zhexiang, et al.
Published: (2025)
Dirichlet-Prior Shaping: Guiding Expert Specialization in Upcycled MoEs
by: Mirvakhabova, Leyla, et al.
Published: (2025)
by: Mirvakhabova, Leyla, et al.
Published: (2025)
MH-MoE: Multi-Head Mixture-of-Experts
by: Huang, Shaohan, et al.
Published: (2024)
by: Huang, Shaohan, et al.
Published: (2024)
MoE-Loco: Mixture of Experts for Multitask Locomotion
by: Huang, Runhan, et al.
Published: (2025)
by: Huang, Runhan, 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)
by: Zhang, Boyang, et al.
Published: (2025)
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings
by: Farhat, Yehya, et al.
Published: (2023)
by: Farhat, Yehya, et al.
Published: (2023)
MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing
by: Ma, Haiyue, et al.
Published: (2025)
by: Ma, Haiyue, et al.
Published: (2025)
Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
by: Li, Pingzhi, et al.
Published: (2025)
by: Li, Pingzhi, et al.
Published: (2025)
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
by: Qian, Yulei, et al.
Published: (2024)
by: Qian, Yulei, et al.
Published: (2024)
MoE Pathfinder: Trajectory-driven Expert Pruning
by: Yang, Xican, et al.
Published: (2025)
by: Yang, Xican, et al.
Published: (2025)
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
by: Lin, Bin, et al.
Published: (2024)
by: Lin, Bin, et al.
Published: (2024)
OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference
by: Wang, Liujianfu, et al.
Published: (2025)
by: Wang, Liujianfu, et al.
Published: (2025)
Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization
by: Hu, Rizhen, et al.
Published: (2026)
by: Hu, Rizhen, et al.
Published: (2026)
SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs
by: Bo, Zi-Hao, et al.
Published: (2026)
by: Bo, Zi-Hao, et al.
Published: (2026)
MoE-DP: An MoE-Enhanced Diffusion Policy for Robust Long-Horizon Robotic Manipulation with Skill Decomposition and Failure Recovery
by: Cheng, Baiye, et al.
Published: (2025)
by: Cheng, Baiye, et al.
Published: (2025)
Surviving Partial Rank Failures in Wide Expert-Parallel MoE Inference
by: Sun, Xun, et al.
Published: (2026)
by: Sun, Xun, et al.
Published: (2026)
Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression
by: Zhu, Peijun, et al.
Published: (2025)
by: Zhu, Peijun, et al.
Published: (2025)
Expert Divergence Learning for MoE-based Language Models
by: Li, Jiaang, et al.
Published: (2026)
by: Li, Jiaang, et al.
Published: (2026)
DyMoE: Dynamic Expert Orchestration with Mixed-Precision Quantization for Efficient MoE Inference on Edge
by: Huang, Yuegui, et al.
Published: (2026)
by: Huang, Yuegui, et al.
Published: (2026)
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs
by: Zhou, Xin, et al.
Published: (2024)
by: Zhou, Xin, et al.
Published: (2024)
SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation
by: Li, Zichong, et al.
Published: (2025)
by: Li, Zichong, 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)
by: Ma, Songkai, et al.
Published: (2025)
Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts
by: Kang, Junmo, et al.
Published: (2024)
by: Kang, Junmo, et al.
Published: (2024)
Similar Items
-
Multi-Head Attention as a Source of Catastrophic Forgetting in MoE Transformers
by: Chen, Anrui, et al.
Published: (2026) -
Spectra: Rethinking Optimizers for LLMs Under Spectral Anisotropy
by: Huang, Zhendong, et al.
Published: (2026) -
The Curse and Blessing of Mean Bias in FP4-Quantized LLM Training
by: Cao, Hengjie, et al.
Published: (2026) -
Metis: Training LLMs with FP4 Quantization
by: Cao, Hengjie, et al.
Published: (2025) -
Dispelling the Curse of Singularities in Neural Network Optimizations
by: Cao, Hengjie, et al.
Published: (2026)