Dynamic Expert Specialization: Towards Catastrophic Forgetting-Free Multi-Domain MoE Adaptation
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Junzhuo, Wang, Bo, Zhou, Xiuze, Hu, Xuming |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis
by: Li, Junzhuo, et al.
Published: (2025)
by: Li, Junzhuo, et al.
Published: (2025)
Deconstructing Pre-training: Knowledge Attribution Analysis in MoE and Dense Models
by: Wang, Bo, et al.
Published: (2026)
by: Wang, Bo, et al.
Published: (2026)
Towards Specialized Generalists: A Multi-Task MoE-LoRA Framework for Domain-Specific LLM Adaptation
by: Yang, Yuxin, et al.
Published: (2026)
by: Yang, Yuxin, et al.
Published: (2026)
Polysemantic Experts, Monosemantic Paths: Routing as Control in MoEs
by: Ye, Charles, et al.
Published: (2026)
by: Ye, Charles, et al.
Published: (2026)
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)
SD-MoE: Spectral Decomposition for Effective Expert Specialization
by: Huang, Ruijun, et al.
Published: (2026)
by: Huang, Ruijun, et al.
Published: (2026)
Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
by: Liu, Baihui, et al.
Published: (2026)
by: Liu, Baihui, et al.
Published: (2026)
Optimal Expert-Attention Allocation in Mixture-of-Experts: A Scalable Law for Dynamic Model Design
by: Li, Junzhuo, et al.
Published: (2026)
by: Li, Junzhuo, et al.
Published: (2026)
Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, et al.
Published: (2025)
Post-Trained MoE Can Skip Half Experts via Self-Distillation
by: Lv, Xingtai, et al.
Published: (2026)
by: Lv, Xingtai, et al.
Published: (2026)
Towards an empirical understanding of MoE design choices
by: Fan, Dongyang, et al.
Published: (2024)
by: Fan, Dongyang, et al.
Published: (2024)
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
by: Ludziejewski, Jan, et al.
Published: (2025)
by: Ludziejewski, Jan, et al.
Published: (2025)
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts
by: Pióro, Maciej, et al.
Published: (2024)
by: Pióro, Maciej, et al.
Published: (2024)
$μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)
by: Jin, Peng, et al.
Published: (2024)
Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation
by: Zheng, Kening, et al.
Published: (2026)
by: Zheng, Kening, et al.
Published: (2026)
GRIN: GRadient-INformed MoE
by: Liu, Liyuan, et al.
Published: (2024)
by: Liu, Liyuan, et al.
Published: (2024)
Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning
by: Ren, Weijieying, et al.
Published: (2024)
by: Ren, Weijieying, et al.
Published: (2024)
Evolutionary Strategies lead to Catastrophic Forgetting in LLMs
by: Abdi, Immanuel, et al.
Published: (2026)
by: Abdi, Immanuel, et al.
Published: (2026)
MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios
by: Li, Shuhuai, et al.
Published: (2026)
by: Li, Shuhuai, et al.
Published: (2026)
MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
by: Xie, Yanyue, et al.
Published: (2024)
by: Xie, Yanyue, et al.
Published: (2024)
Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing
by: Cong, Peizhuang, et al.
Published: (2024)
by: Cong, Peizhuang, et al.
Published: (2024)
Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE
by: Huang, Haiduo, et al.
Published: (2025)
by: Huang, Haiduo, et al.
Published: (2025)
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting
by: Li, Tianhao, et al.
Published: (2024)
by: Li, Tianhao, et al.
Published: (2024)
LocMoE: A Low-Overhead MoE for Large Language Model Training
by: Li, Jing, et al.
Published: (2024)
by: Li, Jing, et al.
Published: (2024)
Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers
by: Kenneweg, Philip, et al.
Published: (2024)
by: Kenneweg, Philip, et al.
Published: (2024)
Turn Waste into Worth: Rectifying Top-$k$ Router of MoE
by: Zeng, Zhiyuan, et al.
Published: (2024)
by: Zeng, Zhiyuan, et al.
Published: (2024)
Self-Distillation as a Performance Recovery Mechanism for LLMs: Counteracting Compression and Catastrophic Forgetting
by: Liu, Chi, et al.
Published: (2026)
by: Liu, Chi, 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)
by: Hua, Yongxiang, 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)
Awakening Dormant Experts:Counterfactual Routing to Mitigate MoE Hallucinations
by: Hu, Wentao, et al.
Published: (2026)
by: Hu, Wentao, et al.
Published: (2026)
Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts
by: Sun, Weigao, et al.
Published: (2025)
by: Sun, Weigao, et al.
Published: (2025)
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
by: Liu, Yang, et al.
Published: (2026)
by: Liu, Yang, et al.
Published: (2026)
MESA: Improving MoE Safety Alignment via Decentralized Expertise
by: Sun, Yitong, et al.
Published: (2026)
by: Sun, Yitong, et al.
Published: (2026)
SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training
by: Tang, Shengkun, et al.
Published: (2026)
by: Tang, Shengkun, et al.
Published: (2026)
CURLoRA: Stable LLM Continual Fine-Tuning and Catastrophic Forgetting Mitigation
by: Fawi, Muhammad
Published: (2024)
by: Fawi, Muhammad
Published: (2024)
Dynamic Orthogonal Continual Fine-tuning for Mitigating Catastrophic Forgettings
by: Zhang, Zhixin, et al.
Published: (2025)
by: Zhang, Zhixin, et al.
Published: (2025)
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)
Exploiting the Experts: Unauthorized Compression in MoE-LLMs
by: Neogi, Pinaki Prasad Guha, et al.
Published: (2025)
by: Neogi, Pinaki Prasad Guha, et al.
Published: (2025)
Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers
by: Ma, Wenhan, et al.
Published: (2025)
by: Ma, Wenhan, et al.
Published: (2025)
Similar Items
-
Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis
by: Li, Junzhuo, et al.
Published: (2025) -
Deconstructing Pre-training: Knowledge Attribution Analysis in MoE and Dense Models
by: Wang, Bo, et al.
Published: (2026) -
Towards Specialized Generalists: A Multi-Task MoE-LoRA Framework for Domain-Specific LLM Adaptation
by: Yang, Yuxin, et al.
Published: (2026) -
Polysemantic Experts, Monosemantic Paths: Routing as Control in MoEs
by: Ye, Charles, et al.
Published: (2026) -
Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
by: Li, Pingzhi, et al.
Published: (2025)