Cover Image

Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Pingzhi, Huang, Morris Yu-Chao, Tan, Zhen, Song, Qingquan, Peng, Jie, Zou, Kai, Cheng, Yu, Xu, Kaidi, Chen, Tianlong
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts
by: Luo, Shuqing, et al.
Published: (2024)

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference
by: Luo, Shuqing, et al.
Published: (2025)

QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
by: Li, Pingzhi, et al.
Published: (2024)

Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design
by: Zhang, Mohan, et al.
Published: (2025)

Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
by: Jaiswal, Ajay, et al.
Published: (2025)

Beyond Redundancy: Diverse and Specialized Multi-Expert Sparse Autoencoder
by: Xu, Zhen, et al.
Published: (2025)

$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
by: Chen, Guanjie, et al.
Published: (2024)

Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
by: Yun, Sukwon, et al.
Published: (2024)

I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts
by: Xin, Jiayi, et al.
Published: (2025)

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
by: Deng, Jianing, et al.
Published: (2026)

TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration
by: Wei, Linye, et al.
Published: (2026)

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)

Mozart: Modularized and Efficient MoE Training on 3.5D Wafer-Scale Chiplet Architectures
by: Luo, Shuqing, et al.
Published: (2026)

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

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

VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting
by: Chen, Hao, et al.
Published: (2024)

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
by: Wu, Haoyuan, et al.
Published: (2025)

DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillation
by: Li, Pingzhi, et al.
Published: (2025)

EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
by: Qian, Yulei, et al.
Published: (2024)

Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts
by: Sun, Weigao, et al.
Published: (2025)

Expert Streaming: Accelerating Low-Batch MoE Inference via Multi-chiplet Architecture and Dynamic Expert Trajectory Scheduling
by: Ma, Songchen, et al.
Published: (2026)

TRUST: A Decentralized Framework for Auditing Large Language Model Reasoning
by: Huang, Morris Yu-Chao, et al.
Published: (2025)

Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection
by: Lei, Tianwu, et al.
Published: (2024)

Post-Trained MoE Can Skip Half Experts via Self-Distillation
by: Lv, Xingtai, et al.
Published: (2026)

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

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

SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation
by: Li, Zichong, et al.
Published: (2025)

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

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

Glider: Global and Local Instruction-Driven Expert Router
by: Li, Pingzhi, et al.
Published: (2024)

MoE-SpeQ: Speculative Quantized Decoding with Proactive Expert Prefetching and Offloading for Mixture-of-Experts
by: Wang, Wenfeng, et al.
Published: (2025)

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

Symphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-Experts
by: Wang, Qi, et al.
Published: (2025)

CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling
by: Zhang, Jihai, et al.
Published: (2024)

MH-MoE: Multi-Head Mixture-of-Experts
by: Huang, Shaohan, et al.
Published: (2024)

MoE-Loco: Mixture of Experts for Multitask Locomotion
by: Huang, Runhan, 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)

Awakening Dormant Experts:Counterfactual Routing to Mitigate MoE Hallucinations
by: Hu, Wentao, et al.
Published: (2026)

EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
by: Chen, Yuanteng, et al.
Published: (2025)

Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts
by: Gu, Naibin, et al.
Published: (2025)