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 55 篇，第 1 / 2 页

1. NeurIPS 2024$\textit{Read-ME}$: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design
2. NeurIPS 2024$\texttt{Model-GLUE}$: Democratized LLM Scaling for A Large Model Zoo in the Wild
3. NeurIPS 2024Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures
4. NeurIPS 2024BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts
5. NeurIPS 2024Breaking Long-Tailed Learning Bottlenecks: A Controllable Paradigm with Hypernetwork-Generated Diverse Experts
6. NeurIPS 2024Continual learning with the neural tangent ensemble
7. NeurIPS 2024CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
8. NeurIPS 2024Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
9. NeurIPS 2024Data Mixture Inference Attack: BPE Tokenizers Reveal Training Data Compositions
10. NeurIPS 2024DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception
11. NeurIPS 2024Diffusion Actor-Critic with Entropy Regulator
12. NeurIPS 2024Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity
13. NeurIPS 2024Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion
14. NeurIPS 2024Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
15. NeurIPS 2024Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
16. NeurIPS 2024FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
17. NeurIPS 2024GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
18. NeurIPS 2024How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers
19. NeurIPS 2024LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization
20. NeurIPS 2024LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
21. NeurIPS 2024Mixture of Demonstrations for In-Context Learning
22. NeurIPS 2024Mixture of Experts Meets Prompt-Based Continual Learning
23. NeurIPS 2024Mixture of In-Context Experts Enhance LLMs' Long Context Awareness
24. NeurIPS 2024Mixture of Link Predictors on Graphs
25. NeurIPS 2024Mixture of Nested Experts: Adaptive Processing of Visual Tokens
26. NeurIPS 2024Mixture of neural fields for heterogeneous reconstruction in cryo-EM
27. NeurIPS 2024Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models
28. NeurIPS 2024Mixture of Tokens: Continuous MoE through Cross-Example Aggregation
29. NeurIPS 2024MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Tasks
30. NeurIPS 2024MoEUT: Mixture-of-Experts Universal Transformers
31. NeurIPS 2024MoLE: Enhancing Human-centric Text-to-image Diffusion via Mixture of Low-rank Experts
32. NeurIPS 2024MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models
33. NeurIPS 2024MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts
34. NeurIPS 2024MoVA: Adapting Mixture of Vision Experts to Multimodal Context
35. NeurIPS 2024Multi-Head Mixture-of-Experts
36. NeurIPS 2024Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization
37. NeurIPS 2024Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs
38. NeurIPS 2024Neural Experts: Mixture of Experts for Implicit Neural Representations
39. NeurIPS 2024Non-Euclidean Mixture Model for Social Network Embedding
40. NeurIPS 2024Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge
41. NeurIPS 2024Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts
42. NeurIPS 2024Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
43. NeurIPS 2024ProxyFusion: Face Feature Aggregation Through Sparse Experts
44. NeurIPS 2024Robust Mixture Learning when Outliers Overwhelm Small Groups
45. NeurIPS 2024Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts
46. NeurIPS 2024Smoothie: Label Free Language Model Routing
47. NeurIPS 2024Spiking Transformer with Experts Mixture
48. NeurIPS 2024Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning
49. NeurIPS 2024SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention
50. NeurIPS 2024Toward Efficient Inference for Mixture of Experts

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。