Mixture-Of-Experts

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Understanding Safety-Sensitive Expert Behavior in Mixture-of-Experts LLMs
May 28, 2026Zhibo Zhang, Yuxi Li, Zhen Ouyang +2Safety Alignment Mixture-Of-Expert Language Models
ConMoE: Expert-Pool Consolidation via Prototype Reassignment for MoE Compression
May 28, 2026Yilun Yao, Jiaming Pan, Elsie Dai +3Mixture-Of-Expert Language Models Mixture-of-Experts Models
Motion-guided sparse correction enables expert-quality point tracking across diverse microscopy regimes
May 28, 2026Leonidas Zimianitis, Pasindu Thenahandi, Kai Buckhalter +10Microscopy Annotations
A Minimal Bifurcation Model of Load Imbalance in a Softmax Mixture-of-Experts Router
May 27, 2026O. M. KiselevLoad Balancing Mixture-of-Experts Models
Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation
May 27, 2026Yujia Guo, Mikhail Kabeshov, Tat Hong Duong Le +6Vast Chemical Space Automated Synthesis Techniques
Routing-Aligned Fine-Tuning for Multilingual Downstream Tasks in Mixture-of-Experts Models
May 27, 2026Guanzhi Deng, Kuan Wu, Haibo Wang +3Mixture-Of-Expert Language Models Mixture-of-Experts Models
How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving
May 27, 2026Hanjiang Wu, Abhimanyu Rajeshkumar Bambhaniya, Sarbartha Banerjee +9Mixture-Of-Expert Inference Mixture-Of-Expert Language Models
VidPrism: Heterogeneous Mixture of Experts for Image-to-Video Transfer
May 27, 2026Rui Lin, Chuanming Wang, Huadong MaMixture-of-Experts Models Motion-Focused Representations
Pruning and Distilling Mixture-of-Experts into Dense Language Models
May 27, 2026Junhyuck Kim, Jihun Yun, Haechan Kim +3Mixture-Of-Expert Language Models Mixture-of-Experts Models
Extracting Small Translation Specialists from LLMs by Aggressively Pruning Experts
May 27, 2026Liu O. Martin, Lucas Bandarkar, Nanyun PengMixture-Of-Expert Language Models Machine Translation Models
FPMoE: A Sparse Mixture-of-Experts Approach to Functional Code Generation
May 27, 2026Loc Pham, Lang Hong Nguyet Anh, Thanh Le-CongMixture-Of-Expert Language Models Code Generation
Laguna M.1/XS.2 Technical Report
May 26, 2026Julien Abadji, Marah Abdin, Connor Adams +93Mixture-Of-Expert Language Models Mle-Bench Lite
MobileMoE: Scaling On-Device Mixture of Experts
May 26, 2026Yanbei Chen, Hanxian Huang, Ernie Chang +5Mixture-Of-Expert Language Models Mixture-Of-Expert Inference
ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference
May 26, 2026Xiongwei Zhu, Xiaojian Liao, Tianyang Jiang +3Mixture-Of-Expert Inference LLM Inference Efficiency
Towards Generalization-Oriented Models for Vehicle Routing Problems with Mixture-of-Experts
May 26, 2026Changhao Miao, Yuntian Zhang, Tongyu Wu +2Vehicle Routing Problem Mixture-of-Experts Models
L2Rec: Towards Dual-View Understanding of LLMs for Personalized Recommendation
May 26, 2026Pingjun Pan, Tingting Zhou, Peiyao Lu +3Recommendation Personalization
Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling
May 26, 2026Fengfa Li, Hongjin Ji, Yifeng Ding +2Mixture-Of-Expert Inference Mixture-of-Experts Models
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
May 26, 2026MiniMax, :, Aili Chen +204Mixture-Of-Expert Language Models Minimax
BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma
May 25, 2026Junlin Yang, Tian Yu, Nicha C. Dvornek +6Cholangiocarcinoma Experts
VEN-VL: A Visual Ensemble MoE Framework for Effective and Efficient Multi-Modal Understanding
May 25, 2026Yinghao Wu, Zhuoyan Luo, Yiyao Yu +3Universal Multimodal Visual Tokens
Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?
May 25, 2026Franka Bause, Jonas Niederle, Martin Pawelczyk +1Multi-Agent Systems Opinion Dynamics
RotMoLE: Enhancing Mixture of Low-Rank Experts through Rotational Gating Mechanism
May 25, 2026Mengyang Sun, Maochuan Dou, Tao Feng +5Mixture-Of-Expert Language Models Mixture-of-Experts Models
Cross-Stage Attention Multi-Expert Network for Radiologist-Inspired Breast Ultrasound Diagnosis
May 25, 2026Xinyang Zhai, Chong Yang, Ruizhi ZhangMammography Ultrasound
AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
May 24, 2026Rui Wang, Renhao Xue, Ray Razi +2Time Series Foundation Models Time Series
RouteScan: A Non-Intrusive Approach to Auditing MoE LLMs Safety via Expert Routing Telemetry
May 24, 2026Bo Lv, Zhiheng Xu, KeDong Xiu +4Large Language Model Safety Mixture-Of-Expert Language Models
Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEs
May 23, 2026Bo Li, Tianyu Dong, Shaolin Zhu +1Mixture-of-Experts Models Text Corpora
GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration
May 23, 2026Junjie Zhao, Jingyi Liang, Zhenyang Cai +22Clinical Reasoning Benchmarking Large Language Models
ConceptM $^3$ oE: Concept-Guided Multimodal Mixture of Experts for Interpretable Computational Pathology
May 23, 2026Xuan Wang, Zhongling Xu, Gopi Kannedhara +13Pathology Foundation Models Multimodal Clinical Data
Safety-Oriented Routing Analysis of Mixtral MoE Under Benign and Harmful Prompts
May 22, 2026Md Nurul Absar SiddikyMixture-Of-Expert Language Models Large Language Model Routing
ECo-MoE: Embodiment-Conditioned Mixture of Experts Increases the Evolvability of Robots
May 22, 2026Yibin Wang, Muhan Li, Zihan Guo +1Co-Evolution Robot Systems
Complete-muE: Optimal Hyperparameter Transfer and Scaling for MoE Models
May 22, 2026Hongwu Peng, Ohiremen Dibua, Yuanjun Xiong +3Mixture-of-Experts Models Large Language
BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization
May 22, 2026Jiayu Zhao, Zihan Teng, Minhao Fan +4Large Language Model Quantization Mixture-Of-Expert Language Models
Semantically Structured Mixture-of-Experts for Compositional Robotic Manipulation
May 22, 2026Chengyu Deng, Guanqi Chen, Yizhou Chen +4Generalizable Robotic Manipulations Diffusion Policies
CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels
May 22, 2026Mengke Li, Haiquan Ling, Lihao Chen +3Noisy Labels Vision-Language Model Adaptation
Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey
May 22, 2026Liangwei Nathan Zheng, Wei Emma Zhang, Olaf Maennel +2Robust Multimodal Learning Mixture-of-Experts Models
SpikingMoE: SDPrompt-Guided Dynamic Expert Fusion in Spiking Neural Networks
May 22, 2026Yukai Yang, Chenxi Qin, Jungang Li +3Spiking Neural Networks Binary Spikes
GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences
May 22, 2026Pengfei Song, Fangjin Liu, Wenwen Zeng +5Glioma Magnetic Resonance Imaging
GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
May 21, 2026Jianing Deng, Song Wang, Dongwei Wang +4Mixed-Precision Quantization Mixture-Of-Expert Language Models
FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection
May 21, 2026Huanchi Wang, Zihang Huang, Yifang Tian +3Mixture-of-Experts Models
Dynamic Mixture of Latent Memories for Self-Evolving Agents
May 21, 2026Dianzhi Yu, Vireo Zhang, Hongru Wang +7Catastrophic Forgetting Self-Evolving Agents
Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models
May 21, 2026Yuting He, Chenyu You, Shuo LiSelf-Supervised Experts
PALS: Power-Aware LLM Serving for Mixture-of-Experts Models
May 20, 2026Can Hankendi, Rana Shahout, Minlan Yu +1LLM Inference Efficiency Single Graphics Processing Unit
FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
May 20, 2026Penglin Dai, Fulian Li, Xincao Xu +3Federated Learning Fedavg
Diagnosing Overhead in Dispatch Operations: Cross-architecture Observatory
May 20, 2026Bole Ma, Jan Eitzinger, Harald Koestler +1Load Balancing Mixture-of-Experts Models
Task-Routed Mixture-of-Experts with Cognitive Appraisal for Implicit Sentiment Analysis
May 20, 2026Yaping Chai, Haoran Xie, Joe S. QinSentiment Analysis Mixture-of-Experts Models
HDMoE: A Hierarchical Decoupling-Fusion Mixture-of-Experts Framework for Multimodal Cancer Survival Prediction
May 20, 2026Huayi Wang, Haochao Ying, Yuyang Xu +5Multimodal Clinical Data Mixture-of-Experts Models
Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting
May 20, 2026Jiawen Zhu, Shuhan Liu, Di Weng +1Non-Stationarity Mixture-of-Experts Models
LER-YOLO: Reliability-Aware Expert Routing for Misaligned RGB-Infrared UAV Detection
May 20, 2026Liming Hou, Yueping Peng, Hexiang Hao +6Unmanned Aerial Vehicle Detection Infrared
Beyond Routing: Characterising Expert Tuning and Representation in Vision Mixture-of-Experts
May 20, 2026Gene Tangtartharakul, Katherine R. StorrsMixture-of-Experts Models Experts
GEM: GPU-Variability-Aware Expert to GPU Mapping for MoE Systems
May 19, 2026Sourish Wawdhane, Avinash Kumar, Poulami DasMixture-Of-Expert Inference Mixture-of-Experts Models
A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images
May 19, 2026João Pedro Matos-Carvalho, Laio Oriel Seman, Stefano Frizzo Stefenon +2Yolo26 Aerial Imagery
A Data-Driven Approach to Idiomaticity Based on Experts' Criteria in Theoretical Linguistics
May 19, 2026Elena Mikhalkova, Anastasiya Vishnyakova, Anastasiya Drozdova +3Multilingual Idioms Grammaticality
FPED: A Functional-Network Prior-Guided Mixture-of-Experts Framework for Interpretable Brain Decoding
May 19, 2026Yudan Ren, Pengcheng Shi, Zihan Ma +2Human Brain Networks Brain Magnetic Resonance Imaging
Post-Trained MoE Can Skip Half Experts via Self-Distillation
May 18, 2026Xingtai Lv, Li Sheng, Kaiyan Zhang +12Mixture-of-Experts Models Sparse Expert Activation
Estimating Item Difficulty with Large Language Models as Experts
May 18, 2026Diana Kolesnikova, Kirill Fedyanin, Abe D. Hofman +2Item Response Theory Mean Opinion Scores
DBES: A Systematic Benchmark and Metric Suite for Evaluating Expert Specialization in Large-Scale MoEs
May 18, 2026Jing Wang, Hongxuan Lu, Jazze Young +2Mixture-of-Experts Models
A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM $Δ$ Integration into Upcycled MoE
May 18, 2026Hao Zhou, Tianhao Li, Zhijun Wang +6Multilingual Large Language Models Mixture-Of-Expert Language Models
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
May 18, 2026Yang Liu, Toan Nguyen, Flora D. SalimEfficient Continual Learning Mixture-Of-Expert Language Models
AdaptiveLoad: Towards Efficient Video Diffusion Transformer Training
May 18, 2026Yucheng Guo, Yongjian Guo, Zhong Guan +6Pre-Trained Video Diffusion Models Generative Image Compression
CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution
May 18, 2026Muyoung Son, Yi Chen, Seungjae Yoo +2Mixture-Of-Expert Inference Mixture-of-Experts Models

May 28, 2026 · cs.CLJ/K move · Enter open · S save

Understanding Safety-Sensitive Expert Behavior in Mixture-of-Experts LLMs

Zhibo Zhang, Yuxi Li, Zhen Ouyang, Ling Shi+1

Huazhong University of Science and Technology, Wuhan, China · Nanyang Technological University, Singapore

Mixture-of-Experts (MoE) LLMs rely on sparse, router-driven expert activation, yet how safety alignment interacts with routed expert specialization remains underexplored. A common intuition is that safety behavior may be controlled by routing harmful requests to distinct refusal-oriented experts. In this work, we provide empirical evidence for a different picture: routing patterns in aligned MoE LLMs are largely topic-driven, while safety behavior can be altered with little change to the model's intrinsic routing path. Motivated by this observation, we present RASET (Router-Agnostic Safety-critical Expert Tuning), a red-teaming framework that probes safety enforcement that is localized in a small subset of experts while preserving the model's intrinsic routing behavior. RASET identifies safety-critical experts via a contrastive routing-sensitivity criterion and applies parameter-efficient tuning only to the selected experts, minimizing semantic disruption relative to router-steering interventions. These results reveal a distinct MoE safety risk, highlighting the need for expert-aware alignment mechanisms.

Safety Alignment Mixture-Of-Expert Language Models Experts

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