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

632 papers, page 9 of 13

401. NeurIPS 2025AdaMSS: Adaptive Multi-Subspace Approach for Parameter-Efficient Fine-Tuning
402. NeurIPS 2025Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking
403. NeurIPS 2025Adaptive Fission: Post-training Encoding for Low-latency Spike Neural Networks
404. NeurIPS 2025Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
405. NeurIPS 2025Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning
406. NeurIPS 2025AegisGuard: RL-Guided Adapter Tuning for TEE-Based Efficient & Secure On-Device Inference
407. NeurIPS 2025AI Progress Should Be Measured by Capability-Per-Resource, Not Scale Alone: A Framework for Gradient-Guided Resource Allocation in LLMs
408. NeurIPS 2025All You Need is One: Capsule Prompt Tuning with a Single Vector
409. NeurIPS 2025AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
410. NeurIPS 2025Angles Don’t Lie: Unlocking Training‑Efficient RL Through the Model’s Own Signals
411. NeurIPS 2025AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
412. NeurIPS 2025Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment
413. NeurIPS 2025Bayes optimal learning of attention-indexed models
414. NeurIPS 2025Beyond Higher Rank: Token-wise Input-Output Projections for Efficient Low-Rank Adaptation
415. NeurIPS 2025Bilevel ZOFO: Efficient LLM Fine-Tuning and Meta-Training
416. NeurIPS 2025Block-Diagonal LoRA for Eliminating Communication Overhead in Tensor Parallel LoRA Serving
417. NeurIPS 2025Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretraining
418. NeurIPS 2025C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
419. NeurIPS 2025Cancer Survival Analysis via Zero-shot Tumor Microenvironment Segmentation on Low-resolution Whole Slide Pathology Images
420. NeurIPS 2025COALA: Numerically Stable and Efficient Framework for Context-Aware Low-Rank Approximation
421. NeurIPS 2025CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving
422. NeurIPS 2025CoIDO: Efficient Data Selection for Visual Instruction Tuning via Coupled Importance-Diversity Optimization
423. NeurIPS 2025Composing Linear Layers from Irreducibles
424. NeurIPS 2025Compress to Impress: Efficient LLM Adaptation Using a Single Gradient Step on 100 Samples
425. NeurIPS 2025Continual Knowledge Adaptation for Reinforcement Learning
426. NeurIPS 2025Controlled Visual Hallucination via Thalamus-Driven Decoupling Network for Domain Adaptation of Black-Box Predictors
427. NeurIPS 2025Correlated Low-Rank Adaptation for ConvNets
428. NeurIPS 2025CrossSpectra: Exploiting Cross-Layer Smoothness for Parameter-Efficient Fine-Tuning
429. NeurIPS 2025Curvature Tuning: Provable Training-free Model Steering From a Single Parameter
430. NeurIPS 2025D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction
431. NeurIPS 2025Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
432. NeurIPS 2025DEFT: Decompositional Efficient Fine-Tuning for Text-to-Image Models
433. NeurIPS 2025DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products
434. NeurIPS 2025Demystifying Language Model Forgetting with Low-rank Example Associations
435. NeurIPS 2025Dependency Parsing is More Parameter-Efficient with Normalization
436. NeurIPS 2025Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix
437. NeurIPS 2025Diffusion-Driven Two-Stage Active Learning for Low-Budget Semantic Segmentation
438. NeurIPS 2025Disentangling misreporting from genuine adaptation in strategic settings: a causal approach
439. NeurIPS 2025Distribution-Aligned Decoding for Efficient LLM Task Adaptation
440. NeurIPS 2025Distribution-Aware Tensor Decomposition for Compression of Convolutional Neural Networks
441. NeurIPS 2025DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment
442. NeurIPS 2025Dr. RAW: Towards General High-Level Vision from RAW with Efficient Task Conditioning
443. NeurIPS 2025Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
444. NeurIPS 2025Dual Prototype-Enhanced Contrastive Framework for Class-Imbalanced Graph Domain Adaptation
445. NeurIPS 2025DUO: No Compromise to Accuracy Degradation
446. NeurIPS 2025Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks
447. NeurIPS 2025ECO: Evolving Core Knowledge for Efficient Transfer
448. NeurIPS 2025Efficient $k$-Sparse Band–Limited Interpolation with Improved Approximation Ratio
449. NeurIPS 2025Efficient Data Selection at Scale via Influence Distillation
450. NeurIPS 2025Efficient Large Language Model Inference with Neural Block Linearization

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.