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

193 papers, page 3 of 4

101. NeurIPS 2024LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search
102. NeurIPS 2024Low Degree Hardness for Broadcasting on Trees
103. NeurIPS 2024Low Precision Local Training is Enough for Federated Learning
104. NeurIPS 2024Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling
105. NeurIPS 2024LuSh-NeRF: Lighting up and Sharpening NeRFs for Low-light Scenes
106. NeurIPS 2024MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space
107. NeurIPS 2024Memory-Efficient LLM Training with Online Subspace Descent
108. NeurIPS 2024Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator
109. NeurIPS 2024Metric Transforms and Low Rank Representations of Kernels for Fast Attention
110. NeurIPS 2024MKGL: Mastery of a Three-Word Language
111. NeurIPS 2024Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation
112. NeurIPS 2024Multistable Shape from Shading Emerges from Patch Diffusion
113. NeurIPS 2024Neural collapse vs. low-rank bias: Is deep neural collapse really optimal?
114. NeurIPS 2024Neural Embeddings Rank: Aligning 3D latent dynamics with movements
115. NeurIPS 2024Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
116. NeurIPS 2024NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
117. NeurIPS 2024Nimbus: Secure and Efficient Two-Party Inference for Transformers
118. NeurIPS 2024No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO
119. NeurIPS 2024Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks
120. NeurIPS 2024Nuclear Norm Regularization for Deep Learning
121. NeurIPS 2024Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation
122. NeurIPS 2024On $f$-Divergence Principled Domain Adaptation: An Improved Framework
123. NeurIPS 2024On Socially Fair Low-Rank Approximation and Column Subset Selection
124. NeurIPS 2024On Sparse Canonical Correlation Analysis
125. NeurIPS 2024On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)
126. NeurIPS 2024OneBit: Towards Extremely Low-bit Large Language Models
127. NeurIPS 2024Online Adaptation of Language Models with a Memory of Amortized Contexts
128. NeurIPS 2024Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
129. NeurIPS 2024Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
130. NeurIPS 2024OTTER: Effortless Label Distribution Adaptation of Zero-shot Models
131. NeurIPS 2024PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
132. NeurIPS 2024Panacea: Pareto Alignment via Preference Adaptation for LLMs
133. NeurIPS 2024Parameter Competition Balancing for Model Merging
134. NeurIPS 2024Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts
135. NeurIPS 2024Parameter-free Clipped Gradient Descent Meets Polyak
136. NeurIPS 2024Personalized Federated Learning via Feature Distribution Adaptation
137. NeurIPS 2024PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
138. NeurIPS 2024Pre-Trained Multi-Goal Transformers with Prompt Optimization for Efficient Online Adaptation
139. NeurIPS 2024Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context
140. NeurIPS 2024PrivAuditor: Benchmarking Data Protection Vulnerabilities in LLM Adaptation Techniques
141. NeurIPS 2024Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
142. NeurIPS 2024Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
143. NeurIPS 2024QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
144. NeurIPS 2024Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models
145. NeurIPS 2024ReFT: Representation Finetuning for Language Models
146. NeurIPS 2024Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe
147. NeurIPS 2024Rethinking Misalignment in Vision-Language Model Adaptation from a Causal Perspective
148. NeurIPS 2024Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
149. NeurIPS 2024S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
150. NeurIPS 2024Safe LoRA: The Silver Lining of Reducing Safety Risks when Finetuning Large 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.