Low-Rank Adaptation papers, 2023–2026

Key findings

• Low-Rank Adaptation went from 282 papers in 2023 to 841 in 2026, roughly 3.0x in three years.
• NeurIPS publishes the most Low-Rank Adaptation work: 781 papers from 2023 to 2026, 34% of the four-venue total.
• The fastest-growing overlap is with Diffusion Models: papers tagged with both rose from 4 in 2023 to 61 in 2026.
• Among the 5 topics under Optimization and Scaling, it ranks #1 by 2026 paper count; the fastest grower in the group, Scaling Laws, is up 4.1x versus 3.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026$\delta$-Mem: : Efficient Online Memory For Large Language Models
• ICML 2026A3: an Analytical Low-Rank Approximation Framework for Attention
• ICLR 2026A Guardrail for Safety Preservation: When Safety-Sensitive Subspace Meets Harmful-Resistant Null-Space
• CVPR 20262-Shots in the Dark Low-Light Denoising with Minimal Data Acquisition
• NeurIPS 2025A geometric framework for momentum-based optimizers for low-rank training
• ICML 2025Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning
• ICLR 2025A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning
• CVPR 2025AdaDARE-gamma: Balancing Stability and Plasticity in Multi-modal LLMs through Efficient Adaptation
• NeurIPS 2024Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation
• ICML 2024A Tensor Decomposition Perspective on Second-order RNNs
• ICLR 2024A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
• CVPR 2024DePT: Decoupled Prompt Tuning
• NeurIPS 2023A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression
• ICML 2023A Three-regime Model of Network Pruning
• ICLR 2023A General Rank Preserving Framework for Asymmetric Image Retrieval
• CVPR 20231% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions
• NeurIPS 2026A low-rank decoder bottleneck bounds reliability in foundation-model perturbation prediction
• ICML 2026Adaptive Utilization of Low-Rank Adaptation via Conditioned Gating
• ICLR 2026ABBA-Adapters: Efficient and Expressive Fine-Tuning of Foundation Models
• CVPR 2026Basis-Oriented Low-rank Transfer for Few-Shot and Test-Time Adaptation

All Low-Rank Adaptation papers

241 papers, page 3 of 5

101. NeurIPS 2025How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime?
102. NeurIPS 2025Hybrid Re-matching for Continual Learning with Parameter-Efficient Tuning
103. NeurIPS 2025Hyperbolic Fine-Tuning for Large Language Models
104. NeurIPS 2025Identifiability of Deep Polynomial Neural Networks
105. NeurIPS 2025Interpretable and Parameter Efficient Graph Neural Additive Models with Random Fourier Features
106. NeurIPS 2025Irrational Complex Rotations Empower Low-bit Optimizers
107. NeurIPS 2025Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement
108. NeurIPS 2025Latent Space Factorization in LoRA
109. NeurIPS 2025LaX: Boosting Low-Rank Training of Foundation Models via Latent Crossing
110. NeurIPS 2025Learning Counterfactual Outcomes Under Rank Preservation
111. NeurIPS 2025Learning Grouped Lattice Vector Quantizers for Low-Bit LLM Compression
112. NeurIPS 2025Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics
113. NeurIPS 2025Learning Source-Free Domain Adaptation for Visible-Infrared Person Re-Identification
114. NeurIPS 2025Learning to Rank for In-Context Example Retrieval
115. NeurIPS 2025Lifelong Test-Time Adaptation via Online Learning in Tracked Low-Dimensional Subspace
116. NeurIPS 2025Linear Transformers Implicitly Discover Unified Numerical Algorithms
117. NeurIPS 2025Linearization Explains Fine-Tuning in Large Language Models
118. NeurIPS 2025LittleBit: Ultra Low-Bit Quantization via Latent Factorization
119. NeurIPS 2025LLM at Network Edge: A Layer-wise Efficient Federated Fine-tuning Approach
120. NeurIPS 2025Loquetier: A Virtualized Multi-LoRA Framework for Unified LLM Fine-tuning and Serving
121. NeurIPS 2025LoRA vs Full Fine-tuning: An Illusion of Equivalence
122. NeurIPS 2025LoRA-EnVar: Parameter-Efficient Hybrid Ensemble Variational Assimilation for Weather Forecasting
123. NeurIPS 2025LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades
124. NeurIPS 2025LoRATv2: Enabling Low-Cost Temporal Modeling in One-Stream Trackers
125. NeurIPS 2025LoRO: Real-Time on-Device Secure Inference for LLMs via TEE-Based Low Rank Obfuscation
126. NeurIPS 2025LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-Tuning
127. NeurIPS 2025Low Precision Streaming PCA
128. NeurIPS 2025Low Rank Gradients and Where to Find Them
129. NeurIPS 2025Low-degree evidence for computational transition of recovery rate in stochastic block model
130. NeurIPS 2025Low-Rank Graphon Learning for Networks
131. NeurIPS 2025Low-Rank Head Avatar Personalization with Registers
132. NeurIPS 2025LT-Soups: Bridging Head and Tail Classes via Subsampled Model Soups
133. NeurIPS 2025Luminance-Aware Statistical Quantization: Unsupervised Hierarchical Learning for Illumination Enhancement
134. NeurIPS 2025Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation
135. NeurIPS 2025Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
136. NeurIPS 2025Memory-Efficient Training with In-Place FFT Implementation
137. NeurIPS 2025MINGLE: Mixture of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging
138. NeurIPS 2025Minimax-Optimal Univariate Function Selection in Sparse Additive Models: Rates, Adaptation, and the Estimation-Selection Gap
139. NeurIPS 2025Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learning
140. NeurIPS 2025Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
141. NeurIPS 2025MixPrompt: Efficient Mixed Prompting for Multimodal Semantic Segmentation
142. NeurIPS 2025MODEL SHAPLEY: Find Your Ideal Parameter Player via One Gradient Backpropagation
143. NeurIPS 2025MokA: Multimodal Low-Rank Adaptation for MLLMs
144. NeurIPS 2025Monitoring Risks in Test-Time Adaptation
145. NeurIPS 2025MoORE: SVD-based Model MoE-ization for Conflict- and Oblivion-Resistant Multi-Task Adaptation
146. NeurIPS 2025MoRIC: A Modular Region-based Implicit Codec for Image Compression
147. NeurIPS 2025Nemotron-Flash: Towards Latency-Optimal Hybrid Small Language Models
148. NeurIPS 2025Noise-Robustness Through Noise: A Framework combining Asymmetric LoRA with Poisoning MoE
149. NeurIPS 2025Non-Convex Tensor Recovery from Tube-Wise Sensing
150. NeurIPS 2025Nonlinear Laplacians: Tunable principal component analysis under directional prior information

FAQ

How many Low-Rank Adaptation papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

2,273 in total: 282 in 2023, 518 in 2024, 632 in 2025, 841 in 2026.

How many Low-Rank Adaptation papers are at NeurIPS 2026?

NeurIPS 2026 has 269 Low-Rank Adaptation papers, versus 241 the year before.

How many Low-Rank Adaptation papers are at ICLR 2026?

ICLR 2026 has 184 Low-Rank Adaptation papers, versus 152 the year before.

How many Low-Rank Adaptation papers are at ICML 2026?

ICML 2026 has 240 Low-Rank Adaptation papers, versus 136 the year before.

How many Low-Rank Adaptation papers are at CVPR 2026?

CVPR 2026 has 148 Low-Rank Adaptation papers, versus 103 the year before.

Which conference publishes the most Low-Rank Adaptation papers?

NeurIPS, with 781 papers from 2023 to 2026.