混合专家论文趋势 2023–2026

主要结论

• 混合专家论文从 2023 年的 236 篇增长到 2026 年的 326 篇，三年间扩大到约 1.4 倍。
• NeurIPS 是发混合专家论文最多的会议，2023–2026 年共 354 篇，占四个会议总量的 39%。
• 交叉方向里涨得最快的是多模态大模型：同时属于两个主题的论文从 2023 年的 4 篇增至 2026 年的 79 篇。
• 在「优化与缩放」的 5 个主题里，它的 2026 年论文数排第 4；同组增长最快的缩放定律是 4.1 倍，混合专家是 1.4 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部混合专家论文

共 326 篇，第 4 / 7 页

151. ICML 2026EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts
152. ICML 2026Effective MoE-based LLM Compression by Exploiting Heterogeneous Inter-Group Experts Routing Frequency and Information Density
153. ICML 2026Efficient Bilevel Optimization for CKA-Guided MoE Upcycling
154. ICML 2026Eigenvectors of Experts are Training-free Non-collapsing Routers
155. ICML 2026ExpertWeaver: Unlocking the Inherent MoE in Dense LLMs with GLU Activation Patterns
156. ICML 2026Fair-FedMOE: Group-Fair One-Shot Federated Learning via Prototype-Guided Experts for Medical Imaging Analysis
157. ICML 2026Fast Estimation for Forest Matrix of Signed Graphs
158. ICML 2026Fast Mixture of Curvature-Aware Experts for Diverse and Dynamic Graph Topologies
159. ICML 2026FedEMoE: Improving Personalization on Heterogeneous Federated Learning via Elastic Mixture of Experts Architecture
160. ICML 2026From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging
161. ICML 2026From Moments to Models: Graphon-Mixture Learning for Mixup and Contrastive Learning
162. ICML 2026GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
163. ICML 2026Generalization and Scaling Laws for Mixture-of-ExpertsTransformers
164. ICML 2026Generalization Bounds for Out-of-distribution Generalization
165. ICML 2026Geometric and Stochastic Analysis of Discontinuities in Sparse Mixture-of-Experts
166. ICML 2026Grouter: Decoupling Routing from Representation for Accelerated MoE Training
167. ICML 2026HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reduction
168. ICML 2026Hyperparameter Transfer with Mixture-of-Expert Layers
169. ICML 2026Label-Guided Representation Learning for Incomplete Multi-View Multi-Label Classification
170. ICML 2026Least-Loaded Expert Parallelism: Load Balancing An Imbalanced Mixture-of-Experts
171. ICML 2026Less Token, More Signal: MoE Expert Pruning via Critical Token Selection
172. ICML 2026LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
173. ICML 2026Little By Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts
174. ICML 2026Mean-Shift PCA by Knockoff Mean
175. ICML 2026MergeMix: Optimizing Mid-Training Data Mixtures via Learnable Model Merging
176. ICML 2026MESA: Improving MoE Safety Alignment via Decentralized Expertise
177. ICML 2026MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unification
178. ICML 2026Mining Tensor/Neuron-Level Sparsity to Maximize Mixture-of-Experts Potential in Post-Training and Inference
179. ICML 2026Mixing Expertise with Confidence: A Mixture of Experts Framework for Robust Multi-Modal Continual Learning
180. ICML 2026Mixture of Concept Bottleneck Experts
181. ICML 2026Mixture of Horizons in Action Chunking
182. ICML 2026Mixtures of geodesic factor analyzers on Riemannian homogeneous spaces
183. ICML 2026MM-Spectrum: Multimodal Multi-spectral Molecular Structural Elucidation with a Stable MoE Framework
184. ICML 2026MoLF: Mixture-of-Latent-Flow for Pan-Cancer Spatial Gene Expression Prediction from Histology
185. ICML 2026MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models
186. ICML 2026MoST: Mixing Speech and Text with Modality-Aware Mixture of Experts
187. ICML 2026Multi-Head LatentMoE and Head Parallel: Communication-Efficient and Deterministic MoE Parallelism
188. ICML 2026Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights
189. ICML 2026NeurIPS: Neuro-anatomical Inductive Priors for Sphere-based Brain Decoding
190. ICML 2026OmniAID: Decoupling Semantic and Artifacts for Universal AI-Generated Image Detection in the Wild
191. ICML 2026OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
192. ICML 2026Opportunistic Expert Activation: Batch-Aware Expert Routing for Faster Decode Without Retraining
193. ICML 2026PADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student Learning
194. ICML 2026Parameters as Experts: Adapting Vision Models with Dynamic Parameter Routing
195. ICML 2026Phase-Aware Mixture of Experts for Agentic Reinforcement Learning
196. ICML 2026Prism-MoE: Efficient Dense-to-MoE Conversion for Visual Autoregressive Generation
197. ICML 2026ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts
198. ICML 2026PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference
199. ICML 2026RaGEP: Rank-aware Geometric Expert Pruning for Mixture-of-Experts Language Models
200. ICML 2026RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing

常见问题

2023–2026 年四大会议一共有多少篇混合专家论文？

共 904 篇，按年份分别是 2023 年 236 篇、2024 年 119 篇、2025 年 223 篇、2026 年 326 篇。

NeurIPS 2026 有多少篇混合专家论文？

NeurIPS 2026 有 75 篇混合专家论文，上一届是 86 篇。

ICLR 2026 有多少篇混合专家论文？

ICLR 2026 有 73 篇混合专家论文，上一届是 59 篇。

ICML 2026 有多少篇混合专家论文？

ICML 2026 有 132 篇混合专家论文，上一届是 57 篇。

CVPR 2026 有多少篇混合专家论文？

CVPR 2026 有 46 篇混合专家论文，上一届是 21 篇。

哪个会议的混合专家论文最多？

NeurIPS，2023–2026 年共 354 篇。