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 88 篇，第 2 / 2 页

51. ICLR 2025ReAttention: Training-Free Infinite Context with Finite Attention Scope
52. ICLR 2025Rethinking Invariance in In-context Learning
53. ICLR 2025Streamlining Redundant Layers to Compress Large Language Models
54. ICLR 2025ThinK: Thinner Key Cache by Query-Driven Pruning
55. ICLR 2025Training Free Exponential Context Extension via Cascading KV Cache
56. ICLR 2025Transformers are Universal In-context Learners
57. ICLR 2025Unsupervised Meta-Learning via In-Context Learning
58. ICLR 2025Why Does the Effective Context Length of LLMs Fall Short?
59. ICLR 2025Why In-Context Learning Models are Good Few-Shot Learners?
60. ICLR 2025You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning
61. ICLR 2024ACRF: Compressing Explicit Neural Radiance Fields via Attribute Compression
62. ICLR 2024CausalLM is not optimal for in-context learning
63. ICLR 2024Compressed Context Memory for Online Language Model Interaction
64. ICLR 2024Context is Environment
65. ICLR 2024Context-Aware Meta-Learning
66. ICLR 2024How do Language Models Bind Entities in Context?
67. ICLR 2024HyperAttention: Long-context Attention in Near-Linear Time
68. ICLR 2024IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
69. ICLR 2024In defense of parameter sharing for model-compression
70. ICLR 2024In-context Autoencoder for Context Compression in a Large Language Model
71. ICLR 2024In-Context Learning Learns Label Relationships but Is Not Conventional Learning
72. ICLR 2024Information Bottleneck Analysis of Deep Neural Networks via Lossy Compression
73. ICLR 2024Language Modeling Is Compression
74. ICLR 2024RECOMP: Improving Retrieval-Augmented LMs with Context Compression and Selective Augmentation
75. ICLR 2024Towards image compression with perfect realism at ultra-low bitrates
76. ICLR 2023$k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference
77. ICLR 2023A Unified Framework for Soft Threshold Pruning
78. ICLR 2023Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats
79. ICLR 2023Artificial Neuronal Ensembles with Learned Context Dependent Gating
80. ICLR 2023CrAM: A Compression-Aware Minimizer
81. ICLR 2023DFPC: Data flow driven pruning of coupled channels without data.
82. ICLR 2023Generate rather than Retrieve: Large Language Models are Strong Context Generators
83. ICLR 2023How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression?
84. ICLR 2023MIMT: Masked Image Modeling Transformer for Video Compression
85. ICLR 2023Pruning Deep Neural Networks from a Sparsity Perspective
86. ICLR 2023RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
87. ICLR 2023TVSPrune - Pruning Non-discriminative filters via Total Variation separability of intermediate representations without fine tuning
88. ICLR 2023What learning algorithm is in-context learning? Investigations with linear models

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。