Context Compression papers, 2023–2026

Key findings

• Context Compression went from 39 papers in 2023 to 136 in 2026, roughly 3.5x in three years.
• NeurIPS publishes the most Context Compression work: 123 papers from 2023 to 2026, 34% of the four-venue total.
• The fastest-growing overlap is with Deep learning · large language models: papers tagged with both rose from 1 in 2023 to 13 in 2026.
• Among the 5 topics under Long Context and Memory, it ranks #4 by 2026 paper count; the fastest grower in the group, Retrieval Memory, is up 6.6x versus 3.5x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Context Compression papers

81 papers, page 2 of 2

51. ICML 2024Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context
52. ICML 2024Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape
53. ICML 2024Why Larger Language Models Do In-context Learning Differently?
54. ICML 2024xT: Nested Tokenization for Larger Context in Large Images
55. NeurIPS 2024Base of RoPE Bounds Context Length
56. NeurIPS 2024Causal Context Adjustment Loss for Learned Image Compression
57. NeurIPS 2024ContextGS : Compact 3D Gaussian Splatting with Anchor Level Context Model
58. NeurIPS 2024DiTFastAttn: Attention Compression for Diffusion Transformer Models
59. NeurIPS 2024DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
60. NeurIPS 2024Efficient Large Multi-modal Models via Visual Context Compression
61. NeurIPS 2024Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective
62. NeurIPS 2024ESPACE: Dimensionality Reduction of Activations for Model Compression
63. NeurIPS 2024Euclidean distance compression via deep random features
64. NeurIPS 2024Exploring Context Window of Large Language Models via Decomposed Positional Vectors
65. NeurIPS 2024In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
66. NeurIPS 2024KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
67. NeurIPS 2024Learning Partitions from Context
68. NeurIPS 2024MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization
69. NeurIPS 2024Make Your LLM Fully Utilize the Context
70. NeurIPS 2024MeLLoC: Lossless Compression with High-order Mechanism Learning
71. NeurIPS 2024MiniCache: KV Cache Compression in Depth Dimension for Large Language Models
72. NeurIPS 2024One-Layer Transformer Provably Learns One-Nearest Neighbor In Context
73. NeurIPS 2024Random Cycle Coding: Lossless Compression of Cluster Assignments via Bits-Back Coding
74. NeurIPS 2024S2HPruner: Soft-to-Hard Distillation Bridges the Discretization Gap in Pruning
75. NeurIPS 2024SlimGPT: Layer-wise Structured Pruning for Large Language Models
76. NeurIPS 2024SlimSAM: 0.1% Data Makes Segment Anything Slim
77. NeurIPS 2024StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses
78. NeurIPS 2024Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
79. NeurIPS 2024Universal In-Context Approximation By Prompting Fully Recurrent Models
80. NeurIPS 2024Vision Model Pre-training on Interleaved Image-Text Data via Latent Compression Learning
81. NeurIPS 2024xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token

FAQ

How many Context Compression papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

365 in total: 39 in 2023, 81 in 2024, 109 in 2025, 136 in 2026.

How many Context Compression papers are at NeurIPS 2026?

NeurIPS 2026 has 50 Context Compression papers, versus 38 the year before.

How many Context Compression papers are at ICLR 2026?

ICLR 2026 has 28 Context Compression papers, versus 32 the year before.

How many Context Compression papers are at ICML 2026?

ICML 2026 has 39 Context Compression papers, versus 20 the year before.

How many Context Compression papers are at CVPR 2026?

CVPR 2026 has 19 Context Compression papers, versus 19 the year before.

Which conference publishes the most Context Compression papers?

NeurIPS, with 123 papers from 2023 to 2026.