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 354 篇，第 4 / 8 页

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

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。