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 12 of 13

551. NeurIPS 2025Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables
552. NeurIPS 2025Parameter Dynamics of Online Machine Learning and Test-time Adaptation
553. NeurIPS 2025Parameter Efficient Fine-tuning via Explained Variance Adaptation
554. NeurIPS 2025ParetoQ: Improving Scaling Laws in Extremely Low-bit LLM Quantization
555. NeurIPS 2025Pay Attention to Small Weights
556. NeurIPS 2025Perturbation Bounds for Low-Rank Inverse Approximations under Noise
557. NeurIPS 2025PoLAR: Polar-Decomposed Low-Rank Adapter Representation
558. NeurIPS 2025Position: AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift
559. NeurIPS 2025Principled Model Routing for Unknown Mixtures of Source Domains
560. NeurIPS 2025Progressive Data Dropout: An Embarrassingly Simple Approach to Train Faster
561. NeurIPS 2025Prompt Tuning Transformers for Data Memorization
562. NeurIPS 2025Provable Meta-Learning with Low-Rank Adaptations
563. NeurIPS 2025Provably Efficient Multi-Task Meta Bandit Learning via Shared Representations
564. NeurIPS 2025PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning
565. NeurIPS 2025PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
566. NeurIPS 2025Q3R: Quadratic Reweighted Rank Regularizer for Effective Low-Rank Training
567. NeurIPS 2025QBasicVSR: Temporal Awareness Adaptation Quantization for Video Super-Resolution
568. NeurIPS 2025QFFT, Question-Free Fine-Tuning for Adaptive Reasoning
569. NeurIPS 2025QSVD: Efficient Low-rank Approximation for Unified Query-Key-Value Weight Compression in Low-Precision Vision-Language Models
570. NeurIPS 2025Quadratic Coreset Selection: Certifying and Reconciling Sequence and Token Mining for Efficient Instruction Tuning
571. NeurIPS 2025Randomized-MLP Regularization Improves Domain Adaptation and Interpretability in DINOv2
572. NeurIPS 2025RankMatch: A Novel Approach to Semi-Supervised Label Distribution Learning Leveraging Rank Correlation between Labels
573. NeurIPS 2025Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning
574. NeurIPS 2025RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
575. NeurIPS 2025Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
576. NeurIPS 2025Reinforcement Learning Finetunes Small Subnetworks in Large Language Models
577. NeurIPS 2025ReMindRAG: Low-Cost LLM-Guided Knowledge Graph Traversal for Efficient RAG
578. NeurIPS 2025ReservoirTTA: Prolonged Test-time Adaptation for Evolving and Recurring Domains
579. NeurIPS 2025Restoring Pruned Large Language Models via Lost Component Compensation
580. NeurIPS 2025Rethinking Hebbian Principle: Low-Dimensional Structural Projection for Unsupervised Learning
581. NeurIPS 2025Ridge Boosting is Both Robust and Efficient
582. NeurIPS 2025RidgeLoRA: Matrix Ridge Enhanced Low-Rank Adaptation of Large Language Models
583. NeurIPS 2025Rising from Ashes: Generalized Federated Learning via Dynamic Parameter Reset
584. NeurIPS 2025RobIA: Robust Instance-aware Continual Test-time Adaptation for Deep Stereo
585. NeurIPS 2025Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA
586. NeurIPS 2025Robust Integrated Learning and Pauli Noise Mitigation for Parametrized Quantum Circuits
587. NeurIPS 2025RobustMerge: Parameter-Efficient Model Merging for MLLMs with Direction Robustness
588. NeurIPS 2025S'MoRE: Structural Mixture of Residual Experts for Parameter-Efficient LLM Fine-tuning
589. NeurIPS 2025SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training
590. NeurIPS 2025SALS: Sparse Attention in Latent Space for KV Cache Compression
591. NeurIPS 2025Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function
592. NeurIPS 2025Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models
593. NeurIPS 2025Seemingly Redundant Modules Enhance Robust Odor Learning in Fruit Flies
594. NeurIPS 2025Self Iterative Label Refinement via Robust Unlabeled Learning
595. NeurIPS 2025Self-Training with Dynamic Weighting for Robust Gradual Domain Adaptation
596. NeurIPS 2025Separating the 'what' and 'how' of compositional computation to enable reuse and continual learning
597. NeurIPS 2025Shallow Diffuse: Robust and Invisible Watermarking through Low-Dim Subspaces in Diffusion Models
598. NeurIPS 2025Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning
599. NeurIPS 2025Skrull: Towards Efficient Long Context Fine-tuning through Dynamic Data Scheduling
600. NeurIPS 2025Slow Transition to Low-Dimensional Chaos in Heavy-Tailed Recurrent Neural Networks

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.