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 904 篇，第 5 / 19 页
- ICML 2026Regret Minimization With a Crowd of Awakening Experts
- ICML 2026ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference
- ICML 2026RepetitionCurse: Measuring and Understanding Router Imbalance in Mixture-of-Experts LLMs under DoS Stress
- ICML 2026Rethinking Convergence in MoE Training: The Role of Routing Sparsity
- ICML 2026Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate
- ICML 2026Rethinking LLM Ensembling from the Perspective of Mixture Models
- ICML 2026Rethinking Sparse Mixture of Experts from a Unified Perspective
- ICML 2026Revisiting Efficiency–Accuracy Scaling in Mixture-of-Experts Architectures
- ICML 2026Rewiring Experts on the Fly: Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert Models
- ICML 2026Robustness of Mixtures of Experts to Feature Noise
- ICML 2026RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing
- ICML 2026RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
- ICML 2026SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning
- ICML 2026Sampling and Identity-Testing Without Approximate Tensorization of Entropy
- ICML 2026ScaleMoE: Mixture-of-Experts for Scalable Continuous Control in Actor-Critic Reinforcement Learning
- ICML 2026Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts
- ICML 2026SCHUR-A*: Layer-wise Optimal Expert Pruning for MoEs via Schur-Complement Guided A* Search
- ICML 2026SD-MoE: Spectral Decomposition for Effective Expert Specialization
- ICML 2026SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacement
- ICML 2026Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws
- ICML 2026Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills
- ICML 2026SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs
- ICML 2026SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning
- ICML 2026Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs
- ICML 2026SpecMD: A Comprehensive Study On Speculative Expert Prefetching
- ICML 2026SPHERE: Mitigating the Loss of Spectral Plasticity in Mixture-of-Experts for Deep Reinforcement Learning
- ICML 2026Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers
- ICML 2026STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning
- ICML 2026Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization
- ICML 2026Task-Aware Mechanism: Hybrid MoE Vision Tower Towards Holistic Video Understanding
- ICML 2026TEAM: Temporal–Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration
- ICML 2026The Catastrophic Failure of *the* k-Means Algorithm in High Dimensions, and How Hartigan's Algorithm Avoids It
- ICML 2026The Cost of Commitment in Option-Based Hierarchical RL
- ICML 2026The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level
- ICML 2026TileQ: Efficient Low-Rank Quantization of Mixture-of-Experts with 2D Tiling
- ICML 2026TIME: Tensor-Factorized Mixture-of-Experts with Intrinsic Routing for Lifelong Multimodal Knowledge Editing
- ICML 2026TiME: Test-Time Mixture-of-Experts Routing via Asymmetric CO-Optimal Transport for Continual Test-Time Adaptation
- ICML 2026TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
- ICML 2026Toward Calibrated Mixture-of-Experts Under Distribution Shift
- ICML 2026Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts
- ICML 2026Towards a Unified Generative Model for Scarce Time Series with Domain Experts
- ICML 2026Towards One-for-All Anomaly Detection for Tabular Data
- ICML 2026TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning
- ICML 2026UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models
- ICML 2026Unified Multimodal Visual Tracking with Dual Mixture-of-Experts
- ICML 2026Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers
- ICML 2026WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems
- ICML 2026When Model Merging Breaks Routing: Training-Free Calibration for MoE
- ICML 2026When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
- ICML 2026X-MoGe: A Cross-Modal Adaptation Framework with Mixture-of-Experts and Geometry Guidance for Heterogeneous Collaborative Perception
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。