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 5 of 6

201. NeurIPS 2026Regret-Optimal Wasserstein-Robust Regression
202. NeurIPS 2026Relative Energy Barriers for Copyright-Aware Language Model Adaptation
203. NeurIPS 2026Reset Dependence Is Embodied: Training Protocol Changes Which Morphologies Appear Optimal
204. NeurIPS 2026Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data
205. NeurIPS 2026Rest-Tuning: Data-Efficient Adaptation of EEG Foundation Models to Individuals via Resting-State Signals
206. NeurIPS 2026Rethinking LLM Fine-Tuning via Weight Space Reparameterization: Preserving Safety during Downstream Adaptation
207. NeurIPS 2026Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorization
208. NeurIPS 2026RIZZ: Routing Interactions to Near Zero-Interference Zones for Continual Adaptation of Black-Box Agents
209. NeurIPS 2026Robust Noisy Inductive Matrix Completion with Local Linear Convergence
210. NeurIPS 2026Route-Consistent Adaptation for Stable Quantization of Mixture-of-Experts Models with Theoretical Guarantees
211. NeurIPS 2026RubiConv - Efficient Boundary-Respecting Convolutions
212. NeurIPS 2026Safeguarding Mutual Correction in Source-Free Domain Adaptation via Cut Statistics
213. NeurIPS 2026SaMA: Morpho Adaptation via Asymmetric Expansion of Kronecker Product
214. NeurIPS 2026SARA: Step-Adaptive Rank Adjustment for Diffusion Inference Acceleration
215. NeurIPS 2026Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters
216. NeurIPS 2026SD-LoRA: Training-Time Structural Distillation into LoRA for Few-Shot Vision-Language Adaptation
217. NeurIPS 2026SDS-LoRA: Overcoming Anisotropic Gradient Scaling in Low-Rank Adaptation
218. NeurIPS 2026Seeking the Unfamiliar but Memorable: Conceptual Creativity as Meta-Learning
219. NeurIPS 2026Self-Tuning Graph Filters via State-Dependent Operator Composition
220. NeurIPS 2026Semantic-Statistical Prior Banks for Federated Low-Shot Learning under Non-IID Clients
221. NeurIPS 2026Sliced Wasserstein Meets Quantum Optics: Provable Wavefunctions Tomography with Scarce Noisy Measurements
222. NeurIPS 2026SpecBlock: Block-Iterative Speculative Decoding with Dynamic Tree Drafting
223. NeurIPS 2026SpecDrop: Parameter-Free Category-Conditioned Routing for Modular Specialization
224. NeurIPS 2026Spectral Alignment in Forward–Backward Representations via Temporal Abstraction
225. NeurIPS 2026Spectral Energy Allocation Enables Source-Free Domain Adaptation in Time Series Forecasting
226. NeurIPS 2026Spectral Rank Calibration for Continual LoRA Merging in Multimodal Large Language Models
227. NeurIPS 2026Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting
228. NeurIPS 2026SPHQuant: Efficient extreme low bit weight quantization for Vision-Language Models
229. NeurIPS 2026Spike-SFT: Selective Parameter Enhancement and Fusion for Efficient Spiking Neural Networks
230. NeurIPS 2026SPIRIT: Speed-Driven Online Adaptation for Self-Speculative Decoding
231. NeurIPS 2026SR-GRPO: Stable Rank as an Intrinsic Geometric Reward for Large Language Model Alignment
232. NeurIPS 2026Stabilizing the Dynamic Low-Rank Training
233. NeurIPS 2026STAC-R: Subspace-Tracked Activation Compression with Residuals
234. NeurIPS 2026State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
235. NeurIPS 2026Stealthy World Model Manipulation via Data Poisoning
236. NeurIPS 2026TailAdapt: Heavy-Tailed Sparse Variational Adaptation for Long-Tailed Class Incremental Learning
237. NeurIPS 2026Temporal Smoothness Constraints on Efficient Neurobiological Codes Imply Temporal Specialization
238. NeurIPS 2026Test-time Risk Adaptation with Mixture of Agents
239. NeurIPS 2026The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
240. NeurIPS 2026The Hidden Power of Scaling Factor in LoRA Optimization
241. NeurIPS 2026The Illusion of Forgetting: Rank Leakage in Knowledge Editing and Its Mitigation
242. NeurIPS 2026The Override Gap: A Magnitude Account of Knowledge Conflict Failure in Hypernetwork-Based Instant LLM Adaptation
243. NeurIPS 2026The sublevel Flood bifiltration: towards scalable 2-parameter persistent homology
244. NeurIPS 2026The Surprising Effectiveness of Deleting Weights in LLM Reasoning and Adaptation
245. NeurIPS 2026TILT: Target-induced loss tilting under covariate shift
246. NeurIPS 2026Time-Frequency Decoupled Cross-Scale Partial Optimal Transport for Time Series Domain Adaptation
247. NeurIPS 2026TopoFisher: Learning Topological Summary Statistics by Maximizing Fisher Information
248. NeurIPS 2026Towards Identifying Dominant Low-Rank Subspaces in Zeroth-Order Fine-Tuning
249. NeurIPS 2026Towards Unified Memory Adaptation for LLM Agents: Textual, Latent, and Parametric Pathways
250. NeurIPS 2026Trainable Sparse Attention via Hybrid Top-k+Top-p Masking and Distillation Fine-Tuning

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.