上下文压缩论文趋势 2023–2026

主要结论

• 上下文压缩论文从 2023 年的 39 篇增长到 2026 年的 136 篇，三年间扩大到约 3.5 倍。
• NeurIPS 是发上下文压缩论文最多的会议，2023–2026 年共 123 篇，占四个会议总量的 34%。
• 交叉方向里涨得最快的是深度学习 · large language models：同时属于两个主题的论文从 2023 年的 1 篇增至 2026 年的 13 篇。
• 在「长上下文与记忆」的 5 个主题里，它的 2026 年论文数排第 4；同组增长最快的检索记忆是 6.6 倍，上下文压缩是 3.5 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 Free Lunch in LLM Compression: Revisiting Retraining after Pruning
• ICML 2026A Unified Sparse Attention via Multi-Granularity Compression
• ICLR 2026An evolutionary perspective on modes of learning in Transformers
• CVPR 2026AstraNav-Memory Contexts Compression for Long Memory
• NeurIPS 2025AdmTree: Compressing Lengthy Context with Adaptive Semantic Trees
• ICML 2025Does learning the right latent variables necessarily improve in-context learning?
• ICLR 2025Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding
• CVPR 2025ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression
• NeurIPS 2024StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses
• ICML 2024Agnostic Sample Compression Schemes for Regression
• ICLR 2024Compressed Context Memory for Online Language Model Interaction
• CVPR 2024Diversity-aware Channel Pruning for StyleGAN Compression
• NeurIPS 2023Do Not Marginalize Mechanisms, Rather Consolidate!
• ICML 2023Private Federated Learning with Autotuned Compression
• ICLR 2023$k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference
• CVPR 2023AutoAD: Movie Description in Context
• NeurIPS 2026A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression
• ICML 2026Context-Driven Incremental Compression for Multi-Turn Dialogue Generation
• ICLR 2026Autoencoding-Free Context Compression for LLMs via Contextual Semantic Anchors
• CVPR 2026Featurising Pixels from Dynamic 3D Scenes with Linear In-Context Learners

全部上下文压缩论文

共 27 篇，第 1 / 1 页

1. NeurIPS 2024Base of RoPE Bounds Context Length
2. NeurIPS 2024Causal Context Adjustment Loss for Learned Image Compression
3. NeurIPS 2024ContextGS : Compact 3D Gaussian Splatting with Anchor Level Context Model
4. NeurIPS 2024DiTFastAttn: Attention Compression for Diffusion Transformer Models
5. NeurIPS 2024DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
6. NeurIPS 2024Efficient Large Multi-modal Models via Visual Context Compression
7. NeurIPS 2024Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective
8. NeurIPS 2024ESPACE: Dimensionality Reduction of Activations for Model Compression
9. NeurIPS 2024Euclidean distance compression via deep random features
10. NeurIPS 2024Exploring Context Window of Large Language Models via Decomposed Positional Vectors
11. NeurIPS 2024In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
12. NeurIPS 2024KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
13. NeurIPS 2024Learning Partitions from Context
14. NeurIPS 2024MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization
15. NeurIPS 2024Make Your LLM Fully Utilize the Context
16. NeurIPS 2024MeLLoC: Lossless Compression with High-order Mechanism Learning
17. NeurIPS 2024MiniCache: KV Cache Compression in Depth Dimension for Large Language Models
18. NeurIPS 2024One-Layer Transformer Provably Learns One-Nearest Neighbor In Context
19. NeurIPS 2024Random Cycle Coding: Lossless Compression of Cluster Assignments via Bits-Back Coding
20. NeurIPS 2024S2HPruner: Soft-to-Hard Distillation Bridges the Discretization Gap in Pruning
21. NeurIPS 2024SlimGPT: Layer-wise Structured Pruning for Large Language Models
22. NeurIPS 2024SlimSAM: 0.1% Data Makes Segment Anything Slim
23. NeurIPS 2024StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses
24. NeurIPS 2024Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
25. NeurIPS 2024Universal In-Context Approximation By Prompting Fully Recurrent Models
26. NeurIPS 2024Vision Model Pre-training on Interleaved Image-Text Data via Latent Compression Learning
27. NeurIPS 2024xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token

常见问题

2023–2026 年四大会议一共有多少篇上下文压缩论文？

共 365 篇，按年份分别是 2023 年 39 篇、2024 年 81 篇、2025 年 109 篇、2026 年 136 篇。

NeurIPS 2026 有多少篇上下文压缩论文？

NeurIPS 2026 有 50 篇上下文压缩论文，上一届是 38 篇。

ICLR 2026 有多少篇上下文压缩论文？

ICLR 2026 有 28 篇上下文压缩论文，上一届是 32 篇。

ICML 2026 有多少篇上下文压缩论文？

ICML 2026 有 39 篇上下文压缩论文，上一届是 20 篇。

CVPR 2026 有多少篇上下文压缩论文？

CVPR 2026 有 19 篇上下文压缩论文，上一届是 19 篇。

哪个会议的上下文压缩论文最多？

NeurIPS，2023–2026 年共 123 篇。