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

781 papers, page 9 of 16

401. NeurIPS 2025LT-Soups: Bridging Head and Tail Classes via Subsampled Model Soups
402. NeurIPS 2025Luminance-Aware Statistical Quantization: Unsupervised Hierarchical Learning for Illumination Enhancement
403. NeurIPS 2025Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation
404. NeurIPS 2025Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
405. NeurIPS 2025Memory-Efficient Training with In-Place FFT Implementation
406. NeurIPS 2025MINGLE: Mixture of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging
407. NeurIPS 2025Minimax-Optimal Univariate Function Selection in Sparse Additive Models: Rates, Adaptation, and the Estimation-Selection Gap
408. NeurIPS 2025Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learning
409. NeurIPS 2025Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
410. NeurIPS 2025MixPrompt: Efficient Mixed Prompting for Multimodal Semantic Segmentation
411. NeurIPS 2025MODEL SHAPLEY: Find Your Ideal Parameter Player via One Gradient Backpropagation
412. NeurIPS 2025MokA: Multimodal Low-Rank Adaptation for MLLMs
413. NeurIPS 2025Monitoring Risks in Test-Time Adaptation
414. NeurIPS 2025MoORE: SVD-based Model MoE-ization for Conflict- and Oblivion-Resistant Multi-Task Adaptation
415. NeurIPS 2025MoRIC: A Modular Region-based Implicit Codec for Image Compression
416. NeurIPS 2025Nemotron-Flash: Towards Latency-Optimal Hybrid Small Language Models
417. NeurIPS 2025Noise-Robustness Through Noise: A Framework combining Asymmetric LoRA with Poisoning MoE
418. NeurIPS 2025Non-Convex Tensor Recovery from Tube-Wise Sensing
419. NeurIPS 2025Nonlinear Laplacians: Tunable principal component analysis under directional prior information
420. NeurIPS 2025NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache
421. NeurIPS 2025OmniDraft: A cross-vocabulary, online adaptive drafter for on-device speculative decoding
422. NeurIPS 2025On Geometry-Enhanced Parameter-Efficient Fine-Tuning for 3D Scene Segmentation
423. NeurIPS 2025On the Expressive Power of Mixture-of-Experts for Structured Complex Tasks
424. NeurIPS 2025On the Role of Hidden States of Modern Hopfield Network in Transformer
425. NeurIPS 2025Optimal Nuisance Function Tuning for Estimating a Doubly Robust Functional under Proportional Asymptotics
426. NeurIPS 2025Optimal Regret Bounds via Low-Rank Structured Variation in Non-Stationary Reinforcement Learning
427. NeurIPS 2025Optimization Inspired Few-Shot Adaptation for Large Language Models
428. NeurIPS 2025Oracle-Efficient Combinatorial Semi-Bandits
429. NeurIPS 2025Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables
430. NeurIPS 2025Parameter Dynamics of Online Machine Learning and Test-time Adaptation
431. NeurIPS 2025Parameter Efficient Fine-tuning via Explained Variance Adaptation
432. NeurIPS 2025ParetoQ: Improving Scaling Laws in Extremely Low-bit LLM Quantization
433. NeurIPS 2025Pay Attention to Small Weights
434. NeurIPS 2025Perturbation Bounds for Low-Rank Inverse Approximations under Noise
435. NeurIPS 2025PoLAR: Polar-Decomposed Low-Rank Adapter Representation
436. NeurIPS 2025Position: AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift
437. NeurIPS 2025Principled Model Routing for Unknown Mixtures of Source Domains
438. NeurIPS 2025Progressive Data Dropout: An Embarrassingly Simple Approach to Train Faster
439. NeurIPS 2025Prompt Tuning Transformers for Data Memorization
440. NeurIPS 2025Provable Meta-Learning with Low-Rank Adaptations
441. NeurIPS 2025Provably Efficient Multi-Task Meta Bandit Learning via Shared Representations
442. NeurIPS 2025PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning
443. NeurIPS 2025PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
444. NeurIPS 2025Q3R: Quadratic Reweighted Rank Regularizer for Effective Low-Rank Training
445. NeurIPS 2025QBasicVSR: Temporal Awareness Adaptation Quantization for Video Super-Resolution
446. NeurIPS 2025QFFT, Question-Free Fine-Tuning for Adaptive Reasoning
447. NeurIPS 2025QSVD: Efficient Low-rank Approximation for Unified Query-Key-Value Weight Compression in Low-Precision Vision-Language Models
448. NeurIPS 2025Quadratic Coreset Selection: Certifying and Reconciling Sequence and Token Mining for Efficient Instruction Tuning
449. NeurIPS 2025Randomized-MLP Regularization Improves Domain Adaptation and Interpretability in DINOv2
450. NeurIPS 2025RankMatch: A Novel Approach to Semi-Supervised Label Distribution Learning Leveraging Rank Correlation between Labels

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.