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 172 篇，第 2 / 4 页

51. ICLR 2026Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
52. ICLR 2026Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentation
53. ICLR 2026REAP the Experts: Why Pruning Prevails for One-Shot MoE compression
54. ICLR 2026Revisit Visual Prompt Tuning: The Expressiveness of Prompt Experts
55. ICLR 2026RLAP-CLIP: Continual Multimodal Learning with Prototype Adaptation and Difficulty-Aware Routing
56. ICLR 2026Routing Manifold Alignment Improves Generalization of Mixture-of-Experts LLMs
57. ICLR 2026Routing Matters in MoE: Scaling Diffusion Transformers with Explicit Routing Guidance
58. ICLR 2026SafeMoE: Safe Fine-Tuning for MoE LLMs by Aligning Harmful Input Routing
59. ICLR 2026Sem-MoE: Semantic-aware Model-Data Collaborative Scheduling for Efficient MoE Inference
60. ICLR 2026SERE: Similarity-based Expert Re-routing for Efficient Batch Decoding in MoE Models
61. ICLR 2026SNaX: sparse narrow accelerated mixture of experts
62. ICLR 2026Steering MoE LLMs via Expert (De)Activation
63. ICLR 2026StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning
64. ICLR 2026Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
65. ICLR 2026TD-MoE: Tensor Decomposition for MoE Models
66. ICLR 2026Test-Time Mixture of World Models for Embodied Agents in Dynamic Environments
67. ICLR 2026Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models
68. ICLR 2026Transformers Learn Latent Mixture Models In-Context via Mirror Descent
69. ICLR 2026Understanding Routing Mechanism in Mixture-of-Experts Language Models
70. ICLR 2026Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel
71. ICLR 2026UNITE: Universal kNowledge Integration from Task-specific Experts
72. ICLR 2026Unveiling Super Experts in Mixture-of-Experts Large Language Models
73. ICLR 2026Why Ask One When You Can Ask $k$? Learning-to-Defer to the Top-$k$ Experts
74. ICLR 2025A Sanity Check for AI-generated Image Detection
75. ICLR 2025Ada-K Routing: Boosting the Efficiency of MoE-based LLMs
76. ICLR 2025Balancing Act: Diversity and Consistency in Large Language Model Ensembles
77. ICLR 2025BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts
78. ICLR 2025CAMEx: Curvature-aware Merging of Experts
79. ICLR 2025Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
80. ICLR 2025Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL
81. ICLR 2025Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization
82. ICLR 2025Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models
83. ICLR 2025Dynamic Modeling of Patients, Modalities and Tasks via Multi-modal Multi-task Mixture of Experts
84. ICLR 2025Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
85. ICLR 2025EC-DIT: Scaling Diffusion Transformers with Adaptive Expert-Choice Routing
86. ICLR 2025Efficient Dictionary Learning with Switch Sparse Autoencoders
87. ICLR 2025Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning
88. ICLR 2025Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping
89. ICLR 2025Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts
90. ICLR 2025ELBOing Stein: Variational Bayes with Stein Mixture Inference
91. ICLR 2025End-to-end Learning of Gaussian Mixture Priors for Diffusion Sampler
92. ICLR 2025Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models
93. ICLR 2025Filtered not Mixed: Filtering-Based Online Gating for Mixture of Large Language Models
94. ICLR 2025Gaussian Mixture Counterfactual Generator
95. ICLR 2025Generalization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priors
96. ICLR 2025GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
97. ICLR 2025Graph Sparsification via Mixture of Graphs
98. ICLR 2025HMoRA: Making LLMs More Effective with Hierarchical Mixture of LoRA Experts
99. ICLR 2025Intrinsic User-Centric Interpretability through Global Mixture of Experts
100. ICLR 2025Layerwise Recurrent Router 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。