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 904 篇，第 2 / 19 页

51. ICLR 2026AMiD: Knowledge Distillation for LLMs with $\alpha$-mixture Assistant Distribution
52. ICLR 2026Balancing the Experts: Unlocking LoRA-MoE for GRPO via Mechanism-Aware Rewards
53. ICLR 2026Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts
54. ICLR 2026Catalog-Native LLM: Speaking Item-ID dialect with Less Entanglement for Recommendation
55. ICLR 2026CLIP-FMoE: Scalable CLIP via Fused Mixture-of-Experts with Enforced Specialization
56. ICLR 2026CodeQuant: Unified Clustering and Quantization for Enhanced Outlier Smoothing in Low-Precision Mixture-of-Experts
57. ICLR 2026CogMoE: Signal-Quality–Guided Multimodal MoE for Cognitive Load Prediction
58. ICLR 2026Combination-of-Experts with Knowledge Sharing for Cross-Task Vehicle Routing Problems
59. ICLR 2026Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss
60. ICLR 2026Decoupling Primitive with Experts: Dynamic Feature Alignment for Compositional Zero-Shot Learning
61. ICLR 2026Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts
62. ICLR 2026DirMoE: Dirichlet-Routed Mixture of Experts
63. ICLR 2026Diverse and Sparse Mixture-of-Experts for Causal Subgraph–Based Out-of-Distribution Graph Learning
64. ICLR 2026Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees
65. ICLR 2026Expert Divergence Learning for MoE-based Language Models
66. ICLR 2026Expert Merging in Sparse Mixture of Experts with Nash Bargaining
67. ICLR 2026Explainable Mixture Models through Differentiable Rule Learning
68. ICLR 2026Exposing and Defending the Achilles' Heel of Video Mixture-of-Experts
69. ICLR 2026Fast Data Mixture Optimization via Gradient Descent
70. ICLR 2026FlyPrompt: Brain-Inspired Random-Expanded Routing with Temporal-Ensemble Experts for General Continual Learning
71. ICLR 2026GUIDE: Gated Uncertainty-Informed Disentangled Experts for Long-tailed Recognition
72. ICLR 2026Guiding Mixture-of-Experts with Temporal Multimodal Interactions
73. ICLR 2026HEAPr: Hessian-based Efficient Atomic Expert Pruning in Output Space
74. ICLR 2026IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation
75. ICLR 2026KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
76. ICLR 2026Knowledge Distillation for Large Language Models through Residual Learning
77. ICLR 2026LD-MoLE: Learnable Dynamic Routing for Mixture of LoRA Experts
78. ICLR 2026Learning to Reason via Mixture-of-Thought for Logical Reasoning
79. ICLR 2026Libra: Effective yet Efficient Load Balancing for Large-scale MoE Inference
80. ICLR 2026LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing
81. ICLR 2026M$^3$E: Continual Vision-and-Language Navigation via Mixture of Macro and Micro Experts
82. ICLR 2026Mixture of Cognitive Reasoners: Modular Reasoning with Brain-Like Specialization
83. ICLR 2026Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning
84. ICLR 2026Mixture-of-Experts Can Surpass Dense LLMs Under Strictly Equal Resource
85. ICLR 2026MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
86. ICLR 2026MoE-GS: Mixture of Experts for Dynamic Gaussian Splatting
87. ICLR 2026MoEEdit: Efficient and Routing-Stable Knowledge Editing for Mixture-of-Experts LLMs
88. ICLR 2026MoGA: Mixture-of-Groups Attention for End-to-End Long Video Generation
89. ICLR 2026MOLM: Mixture of LoRA Markers
90. ICLR 2026MoNE: Replacing Redundant Experts with Lightweight Novices for Structured Pruning of MoE
91. ICLR 2026Multi-objective Large Language Model Alignment with Hierarchical Experts
92. ICLR 2026Multi-Subspace Multi-Modal Modeling for Diffusion Models: Estimation, Convergence and Mixture of Experts
93. ICLR 2026Multilingual Routing in Mixture-of-Experts
94. ICLR 2026Not All Models Suit Expert Offloading: On Local Routing Consistency of Mixture-of-Expert Models
95. ICLR 2026On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
96. ICLR 2026One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning
97. ICLR 2026Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
98. ICLR 2026Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentation
99. ICLR 2026REAP the Experts: Why Pruning Prevails for One-Shot MoE compression
100. ICLR 2026Revisit Visual Prompt Tuning: The Expressiveness of Prompt 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。