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

840 papers, page 9 of 17

401. ICML 2026FedPAT: Federated Test-Time Adaptation via Prototype Affinity Topology
402. ICML 2026FedPissa: Towards Federated Personalized Adaptation of Foundation Models via LoRA Subspace Mapping
403. ICML 2026FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning
404. ICML 2026FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning
405. ICML 2026Fine-Tuning of Transformer models with Frames
406. ICML 2026FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment
407. ICML 2026From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging
408. ICML 2026From Parameter Dynamics to Risk Scoring: Quantifying Sample-Level Safety Degradation in LLM Fine-tuning
409. ICML 2026From Parameters to Data: A Task-Parameter-Guided Fine-Tuning Pipeline for Efficient LLM Alignment
410. ICML 2026From Per-Image Low-Rank to Encoding Mismatch: Rethinking Feature Distillation in Vision Transformers
411. ICML 2026From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning
412. ICML 2026Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in RLVR
413. ICML 2026GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry
414. ICML 2026GPan-LoRA: Gaussian Process Amortized Networks for Bayesian Low-Rank Adaptation in Large Language Models
415. ICML 2026GR-LoRA: Gradient-Recycling Low-Rank Adaptation for Class-Incremental Learning
416. ICML 2026Grokking Finite-Dimensional Algebra
417. ICML 2026Head-in-Head in Linear Attention
418. ICML 2026HiCI: Hierarchical Construction–Integration for Long-Context Attention
419. ICML 2026Identifying Common Hubs in Multiple Gaussian Graphical Models
420. ICML 2026Identifying Connectivity Distributions from Neural Dynamics Using Flows
421. ICML 2026iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis
422. ICML 2026INDEXGUARD: Index-only Backdoor Vetting for Secure Federated PEFT of Large Language Models
423. ICML 2026IO-Adam: Rethinking Memory-Efficient Adaptive Optimizers from Gradient Computation
424. ICML 2026Jailbreak to Protect: Buffering and Reinforcing via Temporary Jailbreaking for Safe Fine-Tuning in Large Language Models
425. ICML 2026JANUS-LORA: A Balanced Low-Rank Adaptation for Continual Learning
426. ICML 2026L-SR1: Learned Symmetric-Rank-One Preconditioning
427. ICML 2026LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization
428. ICML 2026Learn to Merge: Meta-Learning for Adaptive Multi-Task Model Merging
429. ICML 2026Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning
430. ICML 2026Learning Rate Scaling across LoRA Ranks and Transfer to Full Finetuning
431. ICML 2026Learning syntax without semantics: Disentangled tiny language models
432. ICML 2026Learning to Rank by Directly Optimizing Full-Order Probabilities
433. ICML 2026Learning to Rank from Incomplete Rankings
434. ICML 2026LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models
435. ICML 2026LiteVSR: Lightweight Adaptation of Frozen Diffusion Transformers for Video Super-Resolution
436. ICML 2026Little By Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts
437. ICML 2026LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment
438. ICML 2026Log-Normal Multiplicative Dynamics for Stable Low-Precision Deep Learning
439. ICML 2026LoPhyDA: Low-Rank Tensor and Physics Gradient Guided Diffusion for Atmospheric Data Assimilation
440. ICML 2026LoRA-DA: Data-Aware Initialization for Low-Rank Adaptation via Asymptotic Analysis
441. ICML 2026LoRDO: Distributed Low-Rank Optimization with Infrequent Communication
442. ICML 2026Low Kruskal-Rank Adaptation
443. ICML 2026Low-Compute Watermark Removal via Dual-Domain Natural Projection
444. ICML 2026Low-cost Full Fine-tuning: Learning What to Update for LLMs
445. ICML 2026Low-dimensional topology of deep neural networks
446. ICML 2026Low-Rank and Sparsity Are All You Need: Exploring Robust Hierarchical Latent Subspaces for Transferable Adversarial Attack
447. ICML 2026LOZO+: Provably Efficient Zeroth-Order Fine-Tuning via Greedy Low-Rank Subspace Selection
448. ICML 2026MACKO: Sparse matrix-vector multiplication for low sparsity
449. ICML 2026MC-HNN: Learning Latent Structural Semantics and High-Rank Representations for Hypergraph Neural Networks
450. ICML 2026MergeMix: Optimizing Mid-Training Data Mixtures via Learnable Model Merging

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.