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

269 papers, page 4 of 6

151. NeurIPS 2026LSVD: Loss-Aware Low-Rank Approximation for Efficient Low-Precision Vision-Language Models
152. NeurIPS 2026Magnitude-preserving Layers Enable Efficient GANs
153. NeurIPS 2026Mamba Can Learn Low-Dimensional Targets In-Context via Test-Time Feature Learning
154. NeurIPS 2026MaPP: A Unified Marginalized Posterior-Predictive Framework for Data-Efficient RLVR
155. NeurIPS 2026MaRK: Markov-adapted Recurrent Kernels for Dynamic Operator Conditioning in State Space Models
156. NeurIPS 2026MARS: Harmonizing Multimodal Convergence via Adaptive Rank Search
157. NeurIPS 2026Match the Geometry, Skip the Surrogate: Extreme Low-Budget Optimization in High Dimensions
158. NeurIPS 2026Matrix Recovery Via Symmetric Rank-one Measurements With Random Unit-modulus Vectors
159. NeurIPS 2026Matrix-Free Stochastic Training of Low-Rank Spectral Graph Learning via Randomized Adaptive Spectral Estimation
160. NeurIPS 2026Metric Depth Estimation from Arbitrarily Degraded Low-Resolution Depth Prompts
161. NeurIPS 2026MIRA: Mutual Information guided calibration for Reliable Test-Time Adaptation
162. NeurIPS 2026Modulating Merging Strengths via Joint Loss Estimation for LoRA-based Continual Learning
163. NeurIPS 2026Morpho-Temporal Decoupling: How Primate Neurons Expand Dendrites Without Losing Speed
164. NeurIPS 2026NoTA: Normalized Tensor Adaptation for Parameter-Efficient Continual Learning
165. NeurIPS 2026NoTVLA: Semantics-Preserving Robot Adaptation via Narrative Action Interfaces
166. NeurIPS 2026NP-LoRA: Null Space Projection for Subject-Style LoRA Fusion
167. NeurIPS 2026On JEPA Isotropy
168. NeurIPS 2026On the Construction and Implications of Low-Loss Valleys in LoRA-based Bayesian Inference
169. NeurIPS 2026On the Geometry and Latent-Space Composition of Hypernetwork-Generated LoRAs
170. NeurIPS 2026On the Limits of Latent Reuse in Diffusion Models
171. NeurIPS 2026One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
172. NeurIPS 2026One Scan is Enough: Demonstration-Free Adaptation for Language-Guided Navigation
173. NeurIPS 2026OrbitLoRA: Learning Rotation-Aware Low-Data Adaptation of Vision Foundation Models
174. NeurIPS 2026Overcoming Rank Collapse in Feedback Alignment
175. NeurIPS 2026Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation
176. NeurIPS 2026Parameter symmetries determine representational geometry in overparameterized nonlinear networks
177. NeurIPS 2026Path Dependence under Adaptive AI Delegation
178. NeurIPS 2026Pattern-Based Matrix Analysis Under Permutations
179. NeurIPS 2026Permute-then-Adapt: Weak-to-Strong Contrastive Image--Text Adaptation
180. NeurIPS 2026PGSB: Pretrained-Guided Shared Basis for LoRA Model Merging
181. NeurIPS 2026Pixels to Tokens: Token Space Efficient Active Learning for Low-Budget Semantic Segmentation
182. NeurIPS 2026PM-LoRA: Scalable Continual Learning via Progressive Merging of Low Rank Adapters
183. NeurIPS 2026PoEM: Predicting New RL Outcomes from Existing Policies
184. NeurIPS 2026POISE: Instance-Specific Prompt Tuning under Latent Mixture Target Distributions
185. NeurIPS 2026Prefix-Tuning for Arbitrary Output Sequences on Pretrained Transformers
186. NeurIPS 2026PreFT: Prefill-only finetuning for inference efficiency
187. NeurIPS 2026Primal-Dual Representation Learning for Low-Rank Constrained MDPs
188. NeurIPS 2026PRISM: Principal Subspace Alignment for Parameter-Efficient Fine-Tuning
189. NeurIPS 2026PRISM: Spectral Pruning and Reconstruction for Parameter-Efficient Model Merging of MLLMs
190. NeurIPS 2026PROLA: Principal-Orthogonal Low-rank Adaptation for Predictive Spatiotemporal Weather Downscaling
191. NeurIPS 2026Provably Efficient Representation Learning for Low-Rank CMDPs
192. NeurIPS 2026ProxySearch: Decoupled Inference-Time Scaling for Diffusion Models via Asymmetric Noise-Rank Transfer
193. NeurIPS 2026Prysma: Efficient Modality Adaptation for SLO-aware LLM-based Video Question Answering
194. NeurIPS 2026Quantifying Concentration Phenomena of Mean-Field Transformers in the Low-Temperature Regime
195. NeurIPS 2026Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models
196. NeurIPS 2026Rank-Constrained Adaptation for Reliable Real-World Performance
197. NeurIPS 2026RankVQ: Low-Rank Parameterized Commutative Vector Quantization for KV Cache Compression
198. NeurIPS 2026RayTun3R: Online Camera Adaptation in 3D Foundation Models
199. NeurIPS 2026RECIPE: Learning to Rank Complete Precursor Sets for Inorganic Retrosynthesis
200. NeurIPS 2026Reconfiguring Procedural Knowledge for Compositional Robot Skill Adaptation

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.