Mixture of Experts papers, 2023–2026

Key findings

• Mixture of Experts went from 236 papers in 2023 to 326 in 2026, roughly 1.4x in three years.
• NeurIPS publishes the most Mixture of Experts work: 354 papers from 2023 to 2026, 39% of the four-venue total.
• The fastest-growing overlap is with Multimodal LLMs: papers tagged with both rose from 4 in 2023 to 79 in 2026.
• Among the 5 topics under Optimization and Scaling, it ranks #4 by 2026 paper count; the fastest grower in the group, Scaling Laws, is up 4.1x versus 1.4x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Biconvex Formulation for Stable Transport of Mixture Models with a Unique Solution
• ICML 2026$\phi$-Balancing for Mixture-of-Experts Training
• ICLR 2026A Federated Generalized Expectation-Maximization Algorithm for Mixture Models with an Unknown Number of Components
• CVPR 2026Divide Conquer and Aggregate Asymmetric Experts for Class-Imbalanced Semi-Supervised Medical
• NeurIPS 2025Advancing Expert Specialization for Better MoE
• ICML 2025$\texttt{I$^2$MoE}$: Interpretable Multimodal Interaction-aware Mixture-of-Experts
• ICLR 2025A Sanity Check for AI-generated Image Detection
• CVPR 2025Complexity Experts are Task-Discriminative Learners for Any Image Restoration
• NeurIPS 2024$\texttt{Model-GLUE}$: Democratized LLM Scaling for A Large Model Zoo in the Wild
• ICML 2024$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
• ICLR 2024Fusing Models with Complementary Expertise
• CVPR 2024MoDE: CLIP Data Experts via Clustering
• NeurIPS 2023[Re] Numerical influence of ReLU'(0) on backpropagation
• ICML 2023A Distribution Optimization Framework for Confidence Bounds of Risk Measures
• ICLR 2023Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models
• CVPR 2023Balanced Product of Calibrated Experts for Long-Tailed Recognition
• NeurIPS 2026AdERA: Adaptive Exponent Reuse for Lossless Allgather in Sharded MoE Training
• ICML 2026Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse
• ICLR 2026Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies
• CVPR 2026ERMoE Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization

All Mixture of Experts papers

223 papers, page 3 of 5

101. ICML 2025M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Embedding Predictive Architecture
102. ICML 2025Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment
103. ICML 2025Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer
104. ICML 2025Mastering Multiple-Expert Routing: Realizable $H$-Consistency and Strong Guarantees for Learning to Defer
105. ICML 2025MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning
106. ICML 2025Mixture of Experts Made Intrinsically Interpretable
107. ICML 2025Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning
108. ICML 2025Mixture of Hidden-Dimensions: Not All Hidden-States’ Dimensions are Needed in Transformer
109. ICML 2025Mixture of Lookup Experts
110. ICML 2025MoE-SVD: Structured Mixture-of-Experts LLMs Compression via Singular Value Decomposition
111. ICML 2025MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance
112. ICML 2025MoH: Multi-Head Attention as Mixture-of-Head Attention
113. ICML 2025MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition
114. ICML 2025Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
115. ICML 2025Multi-Modal Object Re-identification via Sparse Mixture-of-Experts
116. ICML 2025MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design
117. ICML 2025Occult: Optimizing Collaborative Communications across Experts for Accelerated Parallel MoE Training and Inference
118. ICML 2025On Learning Parallel Pancakes with Mostly Uniform Weights
119. ICML 2025On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation
120. ICML 2025On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists
121. ICML 2025Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach
122. ICML 2025Oracle-MoE: Locality-preserving Routing in the Oracle Space for Memory-constrained Large Language Model Inference
123. ICML 2025Organize the Web: Constructing Domains Enhances Pre-Training Data Curation
124. ICML 2025Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
125. ICML 2025Parrot: Multilingual Visual Instruction Tuning
126. ICML 2025Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale
127. ICML 2025QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration
128. ICML 2025R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts
129. ICML 2025RepLoRA: Reparameterizing Low-rank Adaptation via the Perspective of Mixture of Experts
130. ICML 2025Retraining-free Merging of Sparse MoE via Hierarchical Clustering
131. ICML 2025Scaling Laws for Upcycling Mixture-of-Experts Language Models
132. ICML 2025Score-of-Mixture Training: One-Step Generative Model Training Made Simple via Score Estimation of Mixture Distributions
133. ICML 2025Shortcut-connected Expert Parallelism for Accelerating Mixture of Experts
134. ICML 2025Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging
135. ICML 2025SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
136. ICML 2025TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision
137. ICML 2025What Limits Bidirectional Model's Generative Capabilities? A Uni-Bi-Directional Mixture-of-Expert Method For Bidirectional Fine-tuning
138. NeurIPS 2025A Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Random
139. NeurIPS 2025Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
140. NeurIPS 2025Advancing Expert Specialization for Better MoE
141. NeurIPS 2025ALTER: All-in-One Layer Pruning and Temporal Expert Routing for Efficient Diffusion Generation
142. NeurIPS 2025Blindfolded Experts Generalize Better: Insights from Robotic Manipulation and Videogames
143. NeurIPS 2025Brain-Like Processing Pathways Form in Models With Heterogeneous Experts
144. NeurIPS 2025BrainMoE: Cognition Joint Embedding via Mixture-of-Expert Towards Robust Brain Foundation Model
145. NeurIPS 2025Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
146. NeurIPS 2025Continual Gaussian Mixture Distribution Modeling for Class Incremental Semantic Segmentation
147. NeurIPS 2025CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing
148. NeurIPS 2025Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework
149. NeurIPS 2025Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts
150. NeurIPS 2025Dense Backpropagation Improves Training for Sparse Mixture-of-Experts

FAQ

How many Mixture of Experts papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

904 in total: 236 in 2023, 119 in 2024, 223 in 2025, 326 in 2026.

How many Mixture of Experts papers are at NeurIPS 2026?

NeurIPS 2026 has 75 Mixture of Experts papers, versus 86 the year before.

How many Mixture of Experts papers are at ICLR 2026?

ICLR 2026 has 73 Mixture of Experts papers, versus 59 the year before.

How many Mixture of Experts papers are at ICML 2026?

ICML 2026 has 132 Mixture of Experts papers, versus 57 the year before.

How many Mixture of Experts papers are at CVPR 2026?

CVPR 2026 has 46 Mixture of Experts papers, versus 21 the year before.

Which conference publishes the most Mixture of Experts papers?

NeurIPS, with 354 papers from 2023 to 2026.