Inference Acceleration papers, 2023–2026

Key findings

• Inference Acceleration went from 54 papers in 2023 to 91 in 2026, roughly 1.7x in three years.
• NeurIPS publishes the most Inference Acceleration work: 90 papers from 2023 to 2026, 35% 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 9 in 2026.
• Among the 5 topics under Efficiency and Systems, it ranks #5 by 2026 paper count; the fastest grower in the group, Hardware-Aware ML, is up 4.4x versus 1.7x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Method-Class Divide in Sub-4-Bit Quantization:\\Iterative vs.\ Single-Pass Sensitivity to Data Composition
• ICML 2026Consistency Deep Equilibrium Models
• ICLR 2026OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
• CVPR 2026GeoRK2 Geometry-Guided Runge-Kutta Integration for Diffusion Transformer Acceleration
• NeurIPS 2025PolarQuant: Leveraging Polar Transformation for Key Cache Quantization and Decoding Acceleration
• ICML 2025RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers
• ICLR 2025Progressive distillation induces an implicit curriculum
• CVPR 2025Adapting Dense Matching for Homography Estimation with Grid-based Acceleration
• NeurIPS 2024Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks
• ICML 2024Diversified Batch Selection for Training Acceleration
• ICLR 2024Embarrassingly Simple Dataset Distillation
• CVPR 2024Data-Free Quantization via Pseudo-label Filtering
• NeurIPS 2023Continuous-time Analysis of Anchor Acceleration
• ICML 2023Auxiliary Modality Learning with Generalized Curriculum Distillation
• ICLR 2023Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats
• CVPR 2023NIPQ: Noise Proxy-Based Integrated Pseudo-Quantization
• NeurIPS 2026AcceleGrad#: Adaptive Geometry-Aware Acceleration
• ICML 2026PsumQuant: In-line Post-training Partial Sum Quantizer for Energy Efficient NPU Inference
• ICLR 2026NLI : Non-uniform Linear Interpolation Approximation of Nonlinear Operations for Efficient LLMs Inference
• CVPR 2026SODA Sensitivity-Oriented Dynamic Acceleration for Diffusion Transformer

All Inference Acceleration papers

259 papers, page 2 of 6

51. ICML 2026RSTR: Reducing SpatioTemporal Redundancy in Diffusion Transformers
52. ICML 2026RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models
53. ICML 2026Semantic Cache Distillation: Efficient State Transfer via Reuse and Selective Patching
54. ICML 2026SparseInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
55. ICML 2026UniSVQ: 2-bit Unified Scalar-Vector Quantization
56. ICML 2026WaterSIC: Information-Theoretically (Near) Optimal \\Linear Layer Quantization
57. ICML 2026WAVE: Window-Aware Vocabulary-Efficient Early-Exit for Training-Free LLM Acceleration
58. ICML 2026XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding
59. NeurIPS 2026A Method-Class Divide in Sub-4-Bit Quantization:\\Iterative vs.\ Single-Pass Sensitivity to Data Composition
60. NeurIPS 2026AcceleGrad#: Adaptive Geometry-Aware Acceleration
61. NeurIPS 2026AccelEval: A Whole-Program Benchmark for LLM-Generated CPU-to-GPU Code Acceleration
62. NeurIPS 2026Adaptive Conditional Gradient Sliding: Projection-Free and Line-Search-Free Acceleration
63. NeurIPS 2026AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization
64. NeurIPS 2026Benchmark Recovery Does Not Certify a Frozen Tool-Trigger Contract After Quantization
65. NeurIPS 2026Block Sphere Vector Quantization
66. NeurIPS 2026Cache the Future: Training-Free Self-Revision for Diffusion Transformer Acceleration
67. NeurIPS 2026Complexity of Classical Acceleration for $\ell_1$-Regularized PageRank
68. NeurIPS 2026Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization
69. NeurIPS 2026Disentangling the Good From the Bad: Quantization-Induced Flips Are Not Random
70. NeurIPS 2026Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation
71. NeurIPS 2026FADE: Fractional Anomalous Dynamics Extrapolation for Training-Free Diffusion Transformers Acceleration
72. NeurIPS 2026FlashControl: One-Step Controllable Generator via Distillation-Friendly Single-Stream Teachers.
73. NeurIPS 2026GDMD: Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learning
74. NeurIPS 2026InQuant: In-Place Mixed-Precision KV Cache Quantization via Saliency-Aware Neighbor-Slot Reuse
75. NeurIPS 2026Locking Pretrained Weights via Deep Low-Rank Residual Distillation
76. NeurIPS 2026Logical Distillation of Transformer Encoders
77. NeurIPS 2026Matched-Control Tests of Partition-Source Claims in One Routed Distillation Family
78. NeurIPS 2026MPQ: A Message-Passing View of Post-Training Quantization
79. NeurIPS 2026On the Memorization of Consistency Distillation for Diffusion Models
80. NeurIPS 2026Perfect Parallelization in Mini-Batch SGD with Classical Momentum Acceleration
81. NeurIPS 2026Provable Quantization with Randomized Hadamard Transform
82. NeurIPS 2026RaZeR: Pushing the Limits of NVFP4 Quantization with Redundant Zero Remapping
83. NeurIPS 2026Rethinking Gradient Approximation in Quantization: A Zeroth-Order Expectation Perspective
84. NeurIPS 2026SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion
85. NeurIPS 2026The Quantization Benefits of Residual-Free Transformers
86. NeurIPS 2026Tikhonov-Stabilized Bezier Representation Forecasting for Training-free Diffusion Acceleration
87. NeurIPS 2026Total Variation Distance Estimation in Autoregressive Models
88. NeurIPS 2026Towards Understanding Momentum Acceleration in River-Valley Loss Landscape
89. NeurIPS 2026Transferable SCF-Acceleration through Solver-Aligned Initialization Learning
90. NeurIPS 2026Transporting Quantiles to Codebook: Scalable Vector Quantization without Codebook Collapse
91. NeurIPS 2026UniPrefill: Universal Long-Context Prefill Acceleration via Block-wise Dynamic Sparsification
92. CVPR 2025Adapting Dense Matching for Homography Estimation with Grid-based Acceleration
93. CVPR 2025Attend to Not Attended: Structure-then-Detail Token Merging for Post-training DiT Acceleration
94. CVPR 2025Efficient ANN-Guided Distillation: Aligning Rate-based Features of Spiking Neural Networks through Hybrid Block-wise Replacement
95. CVPR 2025HOT: Hadamard-based Optimized Training
96. CVPR 2025Instant Adversarial Purification with Adversarial Consistency Distillation
97. CVPR 2025Libra-Merging: Importance-redundancy and Pruning-merging Trade-off for Acceleration Plug-in in Large Vision-Language Model
98. CVPR 2025Quantization without Tears
99. CVPR 2025RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories
100. CVPR 2025SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation

FAQ

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

259 in total: 54 in 2023, 50 in 2024, 64 in 2025, 91 in 2026.

How many Inference Acceleration papers are at NeurIPS 2026?

NeurIPS 2026 has 33 Inference Acceleration papers, versus 15 the year before.

How many Inference Acceleration papers are at ICLR 2026?

ICLR 2026 has 17 Inference Acceleration papers, versus 18 the year before.

How many Inference Acceleration papers are at ICML 2026?

ICML 2026 has 26 Inference Acceleration papers, versus 22 the year before.

How many Inference Acceleration papers are at CVPR 2026?

CVPR 2026 has 15 Inference Acceleration papers, versus 9 the year before.

Which conference publishes the most Inference Acceleration papers?

NeurIPS, with 90 papers from 2023 to 2026.