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 76 篇，第 2 / 2 页

51. ICML 2023Personalized Federated Learning under Mixture of Distributions
52. ICML 2023Perturbation Analysis of Neural Collapse
53. ICML 2023Phase Transitions in the Detection of Correlated Databases
54. ICML 2023Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models
55. ICML 2023Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice
56. ICML 2023Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality
57. ICML 2023Robust Explanation for Free or At the Cost of Faithfulness
58. ICML 2023Statistical Foundations of Prior-Data Fitted Networks
59. ICML 2023The Computational Complexity of Concise Hypersphere Classification
60. ICML 2023The Edge of Orthogonality: A Simple View of What Makes BYOL Tick
61. ICML 2023The multimarginal optimal transport formulation of adversarial multiclass classification
62. ICML 2023The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
63. ICML 2023The Power of Uniform Sampling for k-Median
64. ICML 2023The Test of Tests: A Framework for Differentially Private Hypothesis Testing
65. ICML 2023The Value of Out-of-Distribution Data
66. ICML 2023The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
67. ICML 2023Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables
68. ICML 2023Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts
69. ICML 2023Under-Counted Tensor Completion with Neural Incorporation of Attributes
70. ICML 2023Understanding Oversquashing in GNNs through the Lens of Effective Resistance
71. ICML 2023Understanding the Impact of Adversarial Robustness on Accuracy Disparity
72. ICML 2023Unit Scaling: Out-of-the-Box Low-Precision Training
73. ICML 2023Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
74. ICML 2023Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
75. ICML 2023Variational Mixture of HyperGenerators for Learning Distributions over Functions
76. ICML 2023When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction

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。