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 1 / 6 页

1. ICML 2026$\phi$-Balancing for Mixture-of-Experts Training
2. ICML 2026$L^3$: Large Lookup Layers
3. ICML 2026ACO-MoE-LoRA: Evolving-while-Training for Adapting Segment Anything Model 2 to Specialized Domains
4. ICML 2026Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse
5. ICML 2026Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression
6. ICML 2026Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts
7. ICML 2026Breaking the Echo Chamber: A Dynamic Ensemble Pruning Perspective on MoE
8. ICML 2026Calibrated Knowledge Aggregation in Bayesian Mixture-of-Experts for Continual VQA
9. ICML 2026Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization
10. ICML 2026Certain Head, Uncertain Tail: Expert-Sample for Test-Time Scaling in Fine-Grained MoE
11. ICML 2026cMoLLM at Scale: Horizontal Scaling Laws for Convolutionally-Gated Mixture-of-LLMs
12. ICML 2026CoME: Empowering Channel-of-Mobile-Experts with Informative Hybrid-Capabilities Reasoning
13. ICML 2026Complexity Bounds for Dirichlet Process Slice Samplers
14. ICML 2026Compositional Generative Modeling from Decentralized Data
15. ICML 2026Conservation Laws for Modern Neural Architectures
16. ICML 2026Continual Model Routing in Evolving Model Hubs
17. ICML 2026CoPE: Continual Probe-guided Expansion for Large Vision-Language Models
18. ICML 2026DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
19. ICML 2026DC-LA: Difference-of-Convex Langevin Algorithm
20. ICML 2026Decomposing the Basic Abilities of Large Language Models: Mitigating Cross-Task Interference in Multi-Task Instruct-Tuning
21. ICML 2026Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
22. ICML 2026Dirichlet-Prior Shaping: Guiding Expert Specialization in Upcycled MoEs
23. ICML 2026Discriminative Mixture-of-Experts on Graphs with Reliable Expert Fusion
24. ICML 2026Don't Reinvent the Wheel, Just Realign the Spokes: Resource-Efficient Federated Fine-Tuning via Rank-Wise Expert Assembly
25. ICML 2026DOT-MoE: Differentiable Optimal Transport for MoEfication
26. ICML 2026DroneDINO: Towards Heterogeneous Routed Mixture of Experts for Drone-based Unified Object Detection
27. ICML 2026DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training
28. ICML 2026Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting
29. ICML 2026EasyBalance: Cross-Layer Load Balancing in Distributed MoE Inference
30. ICML 2026ECo-MoE: Embodiment-Conditioned Mixture of Experts Increases the Evolvability of Robots
31. ICML 2026ECO: Quantized Training without Full-Precision Master Weights
32. ICML 2026EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts
33. ICML 2026Effective MoE-based LLM Compression by Exploiting Heterogeneous Inter-Group Experts Routing Frequency and Information Density
34. ICML 2026Efficient Bilevel Optimization for CKA-Guided MoE Upcycling
35. ICML 2026Eigenvectors of Experts are Training-free Non-collapsing Routers
36. ICML 2026ExpertWeaver: Unlocking the Inherent MoE in Dense LLMs with GLU Activation Patterns
37. ICML 2026Fair-FedMOE: Group-Fair One-Shot Federated Learning via Prototype-Guided Experts for Medical Imaging Analysis
38. ICML 2026Fast Estimation for Forest Matrix of Signed Graphs
39. ICML 2026Fast Mixture of Curvature-Aware Experts for Diverse and Dynamic Graph Topologies
40. ICML 2026FedEMoE: Improving Personalization on Heterogeneous Federated Learning via Elastic Mixture of Experts Architecture
41. ICML 2026From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging
42. ICML 2026From Moments to Models: Graphon-Mixture Learning for Mixup and Contrastive Learning
43. ICML 2026GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
44. ICML 2026Generalization and Scaling Laws for Mixture-of-ExpertsTransformers
45. ICML 2026Generalization Bounds for Out-of-distribution Generalization
46. ICML 2026Geometric and Stochastic Analysis of Discontinuities in Sparse Mixture-of-Experts
47. ICML 2026Grouter: Decoupling Routing from Representation for Accelerated MoE Training
48. ICML 2026HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reduction
49. ICML 2026Hyperparameter Transfer with Mixture-of-Expert Layers
50. ICML 2026Label-Guided Representation Learning for Incomplete Multi-View Multi-Label Classification

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。