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

507 papers, page 3 of 11

101. ICLR 2026Low-Rank Few-Shot Node Classification by Node-Level Graph Diffusion
102. ICLR 2026Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba
103. ICLR 2026Memory-Free Continual Learning with Null Space Adaptation for Zero-Shot Vision-Language Models
104. ICLR 2026Merge before Forget: A Single LoRA Continual Learning via Continual Merging
105. ICLR 2026MergeTune: Continued Fine-Tuning of Vision-Language Models
106. ICLR 2026Meta-UCF: Unified Task-Conditioned LoRA Generation for Continual Learning in Large Language Models
107. ICLR 2026MetaVLA: Unified Meta Co-Training for Efficient Embodied Adaptation
108. ICLR 2026Metric $k$-clustering using only Weak Comparison Oracles
109. ICLR 2026MiSS: Revisiting the Trade-off in LoRA with an Efficient Shard-Sharing Structure
110. ICLR 2026MixLinear: Extreme Low Resource Multivariate Time Series Forecasting with $0.1K$ Parameters
111. ICLR 2026MoM: Linear Sequence Modeling with Mixture-of-Memories
112. ICLR 2026MoRA: Missing Modality Low-Rank Adaptation for Visual Recognition
113. ICLR 2026MoSA: Mosaic Shared Adaptation of Large Language Models
114. ICLR 2026Multi-Head Low-Rank Attention
115. ICLR 2026Multi-Task Low-Rank Model Adaptation
116. ICLR 2026Not All Documents Are What You Need for Extracting Instruction Tuning Data
117. ICLR 2026On Fairness of Task Arithmetic: The Role of Task Vectors
118. ICLR 2026On Predictability of Reinforcement Learning Dynamics for Large Language Models
119. ICLR 2026On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs
120. ICLR 2026Online Minimization of Polarization and Disagreement via Low-Rank Matrix Bandits
121. ICLR 2026Oracle-efficient Hybrid Learning with Constrained Adversaries
122. ICLR 2026PALC: Preference Alignment via Logit Calibration
123. ICLR 2026Parameter-Efficient Reinforcement Learning using Prefix Optimization
124. ICLR 2026PARD: Accelerating LLM Inference with Low‑Cost PARallel Draft Model Adaptation
125. ICLR 2026PAS: Estimating the target accuracy before domain adaptation
126. ICLR 2026PCLR: Progressively Compressed LoRA for Multimodal Continual Instruction Tuning
127. ICLR 2026PerFit: Exploring Personalization Shifts in Representation Space of LLMs
128. ICLR 2026Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
129. ICLR 2026PiCa: Parameter-Efficient Fine-Tuning with Column Space Projection
130. ICLR 2026PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models
131. ICLR 2026PrefixMemory-Tuning: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention
132. ICLR 2026Prima.cpp: Fast 30-70B LLM Inference on Heterogeneous and Low-Resource Home Clusters
133. ICLR 2026PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inference
134. ICLR 2026Quasi-Monte Carlo Methods Enable Extremely Low-Dimensional Deep Generative Models
135. ICLR 2026QuRL: Low-Precision Reinforcement Learning for Efficient Reasoning
136. ICLR 2026QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models
137. ICLR 2026Random Anchors with Low-rank Decorrelated Learning: A Minimalist Pipeline for Class-Incremental Medical Image Classification
138. ICLR 2026RESA: Bringing Back What Sparse Attention Ignores with Residual Estimation
139. ICLR 2026Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models
140. ICLR 2026Revisit Visual Prompt Tuning: The Expressiveness of Prompt Experts
141. ICLR 2026Revisiting Weight Regularization for Low-Rank Continual Learning
142. ICLR 2026Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling
143. ICLR 2026Robustness of Probabilistic Models to Low-Quality Data: A Multi-Perspective Analysis
144. ICLR 2026Saddle-To-Saddle Dynamics in Deep ReLU Networks: Low-Rank Bias in the First Saddle Escape
145. ICLR 2026SAES-SVD: Self-Adaptive Suppression of Accumulated and Local Errors for SVD-based LLM Compression
146. ICLR 2026Safety at One Shot: Patching Fine-Tuned LLMs with A Single Instance
147. ICLR 2026Scaling with Collapse: Efficient and Predictable Training of LLM Families
148. ICLR 2026Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning
149. ICLR 2026SecP-Tuning: Efficient Privacy-Preserving Prompt Tuning for Large Language Models via MPC
150. ICLR 2026Self-Destructive Language Models

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.