混合专家论文趋势 2023–2026
MoE 路由与训练
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 904 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 138 | 16 | 76 | 6 | 236 |
| 2024 | 55 | 24 | 31 | 9 | 119 |
| 2025 | 86 | 59 | 57 | 21 | 223 |
| 2026 | 75 | 73 | 132 | 46 | 326 |
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 73 篇，第 2 / 2 页
- ICLR 2026Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
- ICLR 2026Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentation
- ICLR 2026REAP the Experts: Why Pruning Prevails for One-Shot MoE compression
- ICLR 2026Revisit Visual Prompt Tuning: The Expressiveness of Prompt Experts
- ICLR 2026RLAP-CLIP: Continual Multimodal Learning with Prototype Adaptation and Difficulty-Aware Routing
- ICLR 2026Routing Manifold Alignment Improves Generalization of Mixture-of-Experts LLMs
- ICLR 2026Routing Matters in MoE: Scaling Diffusion Transformers with Explicit Routing Guidance
- ICLR 2026SafeMoE: Safe Fine-Tuning for MoE LLMs by Aligning Harmful Input Routing
- ICLR 2026Sem-MoE: Semantic-aware Model-Data Collaborative Scheduling for Efficient MoE Inference
- ICLR 2026SERE: Similarity-based Expert Re-routing for Efficient Batch Decoding in MoE Models
- ICLR 2026SNaX: sparse narrow accelerated mixture of experts
- ICLR 2026Steering MoE LLMs via Expert (De)Activation
- ICLR 2026StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning
- ICLR 2026Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
- ICLR 2026TD-MoE: Tensor Decomposition for MoE Models
- ICLR 2026Test-Time Mixture of World Models for Embodied Agents in Dynamic Environments
- ICLR 2026Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models
- ICLR 2026Transformers Learn Latent Mixture Models In-Context via Mirror Descent
- ICLR 2026Understanding Routing Mechanism in Mixture-of-Experts Language Models
- ICLR 2026Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel
- ICLR 2026UNITE: Universal kNowledge Integration from Task-specific Experts
- ICLR 2026Unveiling Super Experts in Mixture-of-Experts Large Language Models
- ICLR 2026Why Ask One When You Can Ask $k$? Learning-to-Defer to the Top-$k$ Experts
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查混合专家在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "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。