流式推理论文趋势 2023–2026

主要结论

• 流式推理论文从 2023 年的 13 篇增长到 2026 年的 41 篇，三年间扩大到约 3.2 倍。
• NeurIPS 是发流式推理论文最多的会议，2023–2026 年共 35 篇，占四个会议总量的 39%。
• 交叉方向里涨得最快的是三维视觉：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 10 篇。
• 在「边缘与物联网」的 5 个主题里，它的 2026 年论文数排第 3；同组增长最快的传感融合是 3.8 倍，流式推理是 3.2 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 2026MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound
• ICML 2026Estimating Correlation Clustering Cost in Node-Arrival Stream
• ICLR 2026Near-Optimal Online Deployment and Routing for Streaming LLMs
• CVPR 2026Color When It Counts Grayscale-Guided Online Triggering for Always-On Streaming
• NeurIPS 2025Learning-Augmented Streaming Algorithms for Correlation Clustering
• ICML 2025Deep Streaming View Clustering
• ICLR 2025Learning-Augmented Frequent Directions
• CVPR 2025Learning from Streaming Video with Orthogonal Gradients
• NeurIPS 2024Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data
• ICML 2024Fast White-Box Adversarial Streaming Without a Random Oracle
• ICLR 2024Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning
• CVPR 2024Streaming Dense Video Captioning
• NeurIPS 2023Streaming PCA for Markovian Data
• ICML 2023Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
• ICLR 2023Continual Transformers: Redundancy-Free Attention for Online Inference
• CVPR 2023System-Status-Aware Adaptive Network for Online Streaming Video Understanding
• NeurIPS 2026QueryStop: Dynamic Stop Signals for Efficient Streaming Inference
• ICML 2026SFedPO: Streaming Federated Learning with a Prediction Oracle under Temporal Shifts
• CVPR 2026Event Stream Filtering via Probability Flux Estimation
• NeurIPS 2025Online Functional Tensor Decomposition via Continual Learning for Streaming Data Completion

全部流式推理论文

共 24 篇，第 1 / 1 页

1. ICML 2026Estimating Correlation Clustering Cost in Node-Arrival Stream
2. ICML 2026Incremental Transformer Neural Processes
3. ICML 2026Intentional Updates for Streaming Reinforcement Learning
4. ICML 2026NExT-Guard: Training-Free Streaming Safeguard without Token-Level Labels
5. ICML 2026Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits
6. ICML 2026ProactiveLLM: Learning Active Interaction for Streaming Large Language Models
7. ICML 2026SFedPO: Streaming Federated Learning with a Prediction Oracle under Temporal Shifts
8. ICML 2026Squeezing More from the Stream : Learning Representation Online for Streaming Reinforcement Learning
9. ICML 2026Streaming Covariate Balancing via Discrepancy-Based Feature Coresets
10. ICML 2026Streaming Sliced Optimal Transport
11. ICML 2025A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
12. ICML 2025Deep Streaming View Clustering
13. ICML 2025SEAD: Unsupervised Ensemble of Streaming Anomaly Detectors
14. ICML 2025SHE: Streaming-media Hashing Retrieval
15. ICML 2024DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving
16. ICML 2024Fast Co-Training under Weak Dependence via Stream-Based Active Learning
17. ICML 2024Fast White-Box Adversarial Streaming Without a Random Oracle
18. ICML 2024High-Dimensional Geometric Streaming for Nearly Low Rank Data
19. ICML 2024Learning from Streaming Data when Users Choose
20. ICML 2024Online Isolation Forest
21. ICML 2023Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
22. ICML 2023Online Local Differential Private Quantile Inference via Self-normalization
23. ICML 2023Streaming Active Learning with Deep Neural Networks
24. ICML 2023Streaming Submodular Maximization with Differential Privacy

常见问题

2023–2026 年四大会议一共有多少篇流式推理论文？

共 90 篇，按年份分别是 2023 年 13 篇、2024 年 19 篇、2025 年 17 篇、2026 年 41 篇。

NeurIPS 2026 有多少篇流式推理论文？

NeurIPS 2026 有 15 篇流式推理论文，上一届是 8 篇。

ICLR 2026 有多少篇流式推理论文？

ICLR 2026 有 1 篇流式推理论文，上一届是 2 篇。

ICML 2026 有多少篇流式推理论文？

ICML 2026 有 10 篇流式推理论文，上一届是 4 篇。

CVPR 2026 有多少篇流式推理论文？

CVPR 2026 有 15 篇流式推理论文，上一届是 3 篇。

哪个会议的流式推理论文最多？

NeurIPS，2023–2026 年共 35 篇。