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

904 papers, page 5 of 19

201. ICML 2026Regret Minimization With a Crowd of Awakening Experts
202. ICML 2026ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference
203. ICML 2026RepetitionCurse: Measuring and Understanding Router Imbalance in Mixture-of-Experts LLMs under DoS Stress
204. ICML 2026Rethinking Convergence in MoE Training: The Role of Routing Sparsity
205. ICML 2026Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate
206. ICML 2026Rethinking LLM Ensembling from the Perspective of Mixture Models
207. ICML 2026Rethinking Sparse Mixture of Experts from a Unified Perspective
208. ICML 2026Revisiting Efficiency–Accuracy Scaling in Mixture-of-Experts Architectures
209. ICML 2026Rewiring Experts on the Fly: Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert Models
210. ICML 2026Robustness of Mixtures of Experts to Feature Noise
211. ICML 2026RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing
212. ICML 2026RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
213. ICML 2026SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning
214. ICML 2026Sampling and Identity-Testing Without Approximate Tensorization of Entropy
215. ICML 2026ScaleMoE: Mixture-of-Experts for Scalable Continuous Control in Actor-Critic Reinforcement Learning
216. ICML 2026Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts
217. ICML 2026SCHUR-A*: Layer-wise Optimal Expert Pruning for MoEs via Schur-Complement Guided A* Search
218. ICML 2026SD-MoE: Spectral Decomposition for Effective Expert Specialization
219. ICML 2026SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacement
220. ICML 2026Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws
221. ICML 2026Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills
222. ICML 2026SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs
223. ICML 2026SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning
224. ICML 2026Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs
225. ICML 2026SpecMD: A Comprehensive Study On Speculative Expert Prefetching
226. ICML 2026SPHERE: Mitigating the Loss of Spectral Plasticity in Mixture-of-Experts for Deep Reinforcement Learning
227. ICML 2026Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers
228. ICML 2026STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning
229. ICML 2026Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization
230. ICML 2026Task-Aware Mechanism: Hybrid MoE Vision Tower Towards Holistic Video Understanding
231. ICML 2026TEAM: Temporal–Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration
232. ICML 2026The Catastrophic Failure of *the* k-Means Algorithm in High Dimensions, and How Hartigan's Algorithm Avoids It
233. ICML 2026The Cost of Commitment in Option-Based Hierarchical RL
234. ICML 2026The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level
235. ICML 2026TileQ: Efficient Low-Rank Quantization of Mixture-of-Experts with 2D Tiling
236. ICML 2026TIME: Tensor-Factorized Mixture-of-Experts with Intrinsic Routing for Lifelong Multimodal Knowledge Editing
237. ICML 2026TiME: Test-Time Mixture-of-Experts Routing via Asymmetric CO-Optimal Transport for Continual Test-Time Adaptation
238. ICML 2026TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
239. ICML 2026Toward Calibrated Mixture-of-Experts Under Distribution Shift
240. ICML 2026Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts
241. ICML 2026Towards a Unified Generative Model for Scarce Time Series with Domain Experts
242. ICML 2026Towards One-for-All Anomaly Detection for Tabular Data
243. ICML 2026TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning
244. ICML 2026UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models
245. ICML 2026Unified Multimodal Visual Tracking with Dual Mixture-of-Experts
246. ICML 2026Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers
247. ICML 2026WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems
248. ICML 2026When Model Merging Breaks Routing: Training-Free Calibration for MoE
249. ICML 2026When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
250. ICML 2026X-MoGe: A Cross-Modal Adaptation Framework with Mixture-of-Experts and Geometry Guidance for Heterogeneous Collaborative Perception

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.