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 296 篇，第 4 / 6 页

151. ICML 2025Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
152. ICML 2025Learning Mixtures of Experts with EM: A Mirror Descent Perspective
153. ICML 2025M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Embedding Predictive Architecture
154. ICML 2025Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment
155. ICML 2025Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer
156. ICML 2025Mastering Multiple-Expert Routing: Realizable $H$-Consistency and Strong Guarantees for Learning to Defer
157. ICML 2025MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning
158. ICML 2025Mixture of Experts Made Intrinsically Interpretable
159. ICML 2025Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning
160. ICML 2025Mixture of Hidden-Dimensions: Not All Hidden-States’ Dimensions are Needed in Transformer
161. ICML 2025Mixture of Lookup Experts
162. ICML 2025MoE-SVD: Structured Mixture-of-Experts LLMs Compression via Singular Value Decomposition
163. ICML 2025MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance
164. ICML 2025MoH: Multi-Head Attention as Mixture-of-Head Attention
165. ICML 2025MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition
166. ICML 2025Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
167. ICML 2025Multi-Modal Object Re-identification via Sparse Mixture-of-Experts
168. ICML 2025MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design
169. ICML 2025Occult: Optimizing Collaborative Communications across Experts for Accelerated Parallel MoE Training and Inference
170. ICML 2025On Learning Parallel Pancakes with Mostly Uniform Weights
171. ICML 2025On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation
172. ICML 2025On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists
173. ICML 2025Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach
174. ICML 2025Oracle-MoE: Locality-preserving Routing in the Oracle Space for Memory-constrained Large Language Model Inference
175. ICML 2025Organize the Web: Constructing Domains Enhances Pre-Training Data Curation
176. ICML 2025Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
177. ICML 2025Parrot: Multilingual Visual Instruction Tuning
178. ICML 2025Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale
179. ICML 2025QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration
180. ICML 2025R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts
181. ICML 2025RepLoRA: Reparameterizing Low-rank Adaptation via the Perspective of Mixture of Experts
182. ICML 2025Retraining-free Merging of Sparse MoE via Hierarchical Clustering
183. ICML 2025Scaling Laws for Upcycling Mixture-of-Experts Language Models
184. ICML 2025Score-of-Mixture Training: One-Step Generative Model Training Made Simple via Score Estimation of Mixture Distributions
185. ICML 2025Shortcut-connected Expert Parallelism for Accelerating Mixture of Experts
186. ICML 2025Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging
187. ICML 2025SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
188. ICML 2025TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision
189. ICML 2025What Limits Bidirectional Model's Generative Capabilities? A Uni-Bi-Directional Mixture-of-Expert Method For Bidirectional Fine-tuning
190. ICML 2024$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
191. ICML 2024A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts
192. ICML 2024A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM)
193. ICML 2024A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
194. ICML 2024Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts
195. ICML 2024Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
196. ICML 2024BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation
197. ICML 2024Convergence of Online Learning Algorithm for a Mixture of Multiple Linear Regressions
198. ICML 2024Efficient Mixture Learning in Black-Box Variational Inference
199. ICML 2024Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
200. ICML 2024Exploring the Benefit of Activation Sparsity in Pre-training

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。