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 2 of 8

51. NeurIPS 2026Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer
52. NeurIPS 2026N-vium: Mixture-of-Exits Transformer for Accelerated Exact Generation
53. NeurIPS 2026Non-Expanding Gated Continued Fraction Architecture for Feedforward Layers in Language Models
54. NeurIPS 2026Not All Routing Drift Is Harmful: Trust-Region Projection for Class-Incremental Learning
55. NeurIPS 2026One-Bit Clustering for Two Component Sub-Gaussian Mixture Models
56. NeurIPS 2026PGMS: Pyramidal Gaussian Mixture Splatting for 3DGS Compression
57. NeurIPS 2026Physics-Informed Video Generation via Mixture-of-Experts Latent Alignment
58. NeurIPS 2026PithTrain: A Compact and Agent-Native MoE Training System
59. NeurIPS 2026POISE: Instance-Specific Prompt Tuning under Latent Mixture Target Distributions
60. NeurIPS 2026Predicting Only from Selected Evidence: A Tempered Product-of-Experts Bottleneck for Auditable EEG Diagnosis
61. NeurIPS 2026PriSM: Prior-guided Shared-basis Mixture Personalization for LLMs under Sparse User Histories
62. NeurIPS 2026Private Online Prediction from Experts with Small Losses
63. NeurIPS 2026Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models
64. NeurIPS 2026Replicas-as-Variables: A Planner for Throughput-Optimal Routing in LLM Deployment
65. NeurIPS 2026Residual Paving: Diagnosing the Routing Bottleneck in Selective Refusal Editing
66. NeurIPS 2026Route-Consistent Adaptation for Stable Quantization of Mixture-of-Experts Models with Theoretical Guarantees
67. NeurIPS 2026S²MoE: Shared-Subspace Mixture of Sparse Experts
68. NeurIPS 2026SAME: Stability-Aware Embedding Extraction in Mixture-of-Experts Language Models
69. NeurIPS 2026SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization
70. NeurIPS 2026Spectral Geometry of Attention: From Information Routing to Uncertainty
71. NeurIPS 2026Structure, Subspace and System: Push the Real Limit of Extremely Low-Bit Quantization for MoE-LLMs
72. NeurIPS 2026Swimba: Switch Mamba Model Scales State Space Models
73. NeurIPS 2026Training-Free Dynamic Upcycling of Expert Language Models
74. NeurIPS 2026vExpert: Virtualizing Expert Storage for Adaptive Load Balancing in Distributed MoE Inference
75. NeurIPS 2026Why Routers Freeze: Infinite Width Learning Dynamics for Mixture of Experts
76. NeurIPS 2025A Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Random
77. NeurIPS 2025Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
78. NeurIPS 2025Advancing Expert Specialization for Better MoE
79. NeurIPS 2025ALTER: All-in-One Layer Pruning and Temporal Expert Routing for Efficient Diffusion Generation
80. NeurIPS 2025Blindfolded Experts Generalize Better: Insights from Robotic Manipulation and Videogames
81. NeurIPS 2025Brain-Like Processing Pathways Form in Models With Heterogeneous Experts
82. NeurIPS 2025BrainMoE: Cognition Joint Embedding via Mixture-of-Expert Towards Robust Brain Foundation Model
83. NeurIPS 2025Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
84. NeurIPS 2025Continual Gaussian Mixture Distribution Modeling for Class Incremental Semantic Segmentation
85. NeurIPS 2025CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing
86. NeurIPS 2025Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework
87. NeurIPS 2025Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts
88. NeurIPS 2025Dense Backpropagation Improves Training for Sparse Mixture-of-Experts
89. NeurIPS 2025DiEP: Adaptive Mixture-of-Experts Compression through Differentiable Expert Pruning
90. NeurIPS 2025Discovering Important Experts for Mixture-of-Experts Models Pruning Through a Theoretical Perspective
91. NeurIPS 2025Diverse Influence Component Analysis: A Geometric Approach to Nonlinear Mixture Identifiability
92. NeurIPS 2025Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
93. NeurIPS 2025Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations
94. NeurIPS 2025Enhanced Expert Merging for Mixture-of-Experts in Graph Foundation Models
95. NeurIPS 2025Evaluating multiple models using labeled and unlabeled data
96. NeurIPS 2025FAME: Adaptive Functional Attention with Expert Routing for Function-on-Function Regression
97. NeurIPS 2025Fisher meets Feynman: score-based variational inference with a product of experts
98. NeurIPS 2025FlashMoE: Fast Distributed MoE in a Single Kernel
99. NeurIPS 2025FlowMoE: A Scalable Pipeline Scheduling Framework for Distributed Mixture-of-Experts Training
100. NeurIPS 2025FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise 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.