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

354 papers, page 4 of 8

151. NeurIPS 2025TANDEM: Bi-Level Data Mixture Optimization with Twin Networks
152. NeurIPS 2025The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model
153. NeurIPS 2025The Omni-Expert: A Computationally Efficient Approach to Achieve a Mixture of Experts in a Single Expert Model
154. NeurIPS 2025Toward Efficient Inference Attacks: Shadow Model Sharing via Mixture-of-Experts
155. NeurIPS 2025Towards Interpretability Without Sacrifice: Faithful Dense Layer Decomposition with Mixture of Decoders
156. NeurIPS 2025Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training
157. NeurIPS 2025UGG-ReID: Uncertainty-Guided Graph Model for Multi-Modal Object Re-Identification
158. NeurIPS 2025UMoE: Unifying Attention and FFN with Shared Experts
159. NeurIPS 2025Unifying and Enhancing Graph Transformers via a Hierarchical Mask Framework
160. NeurIPS 2025Where Graph Meets Heterogeneity: Multi-View Collaborative Graph Experts
161. NeurIPS 2025Who Speaks for the Trigger? Dynamic Expert Routing in Backdoored Mixture-of-Experts Transformers
162. NeurIPS 2024$\textit{Read-ME}$: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design
163. NeurIPS 2024$\texttt{Model-GLUE}$: Democratized LLM Scaling for A Large Model Zoo in the Wild
164. NeurIPS 2024Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures
165. NeurIPS 2024BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts
166. NeurIPS 2024Breaking Long-Tailed Learning Bottlenecks: A Controllable Paradigm with Hypernetwork-Generated Diverse Experts
167. NeurIPS 2024Continual learning with the neural tangent ensemble
168. NeurIPS 2024CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
169. NeurIPS 2024Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
170. NeurIPS 2024Data Mixture Inference Attack: BPE Tokenizers Reveal Training Data Compositions
171. NeurIPS 2024DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception
172. NeurIPS 2024Diffusion Actor-Critic with Entropy Regulator
173. NeurIPS 2024Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity
174. NeurIPS 2024Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion
175. NeurIPS 2024Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
176. NeurIPS 2024Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
177. NeurIPS 2024FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
178. NeurIPS 2024GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
179. NeurIPS 2024How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers
180. NeurIPS 2024LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization
181. NeurIPS 2024LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
182. NeurIPS 2024Mixture of Demonstrations for In-Context Learning
183. NeurIPS 2024Mixture of Experts Meets Prompt-Based Continual Learning
184. NeurIPS 2024Mixture of In-Context Experts Enhance LLMs' Long Context Awareness
185. NeurIPS 2024Mixture of Link Predictors on Graphs
186. NeurIPS 2024Mixture of Nested Experts: Adaptive Processing of Visual Tokens
187. NeurIPS 2024Mixture of neural fields for heterogeneous reconstruction in cryo-EM
188. NeurIPS 2024Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models
189. NeurIPS 2024Mixture of Tokens: Continuous MoE through Cross-Example Aggregation
190. NeurIPS 2024MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Tasks
191. NeurIPS 2024MoEUT: Mixture-of-Experts Universal Transformers
192. NeurIPS 2024MoLE: Enhancing Human-centric Text-to-image Diffusion via Mixture of Low-rank Experts
193. NeurIPS 2024MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models
194. NeurIPS 2024MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts
195. NeurIPS 2024MoVA: Adapting Mixture of Vision Experts to Multimodal Context
196. NeurIPS 2024Multi-Head Mixture-of-Experts
197. NeurIPS 2024Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization
198. NeurIPS 2024Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs
199. NeurIPS 2024Neural Experts: Mixture of Experts for Implicit Neural Representations
200. NeurIPS 2024Non-Euclidean Mixture Model for Social Network Embedding

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.