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 7 of 17

301. ICLR 2026SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse–Linear Attention
302. ICLR 2026SpareTrain: Fault-Tolerant LLM Training via Low-Cost Dual Modular Redundancy
303. ICLR 2026Sparse Attention Adaptation for Long Reasoning
304. ICLR 2026SplitLoRA: Balancing Stability and Plasticity in Continual Learning Through Gradient Space Splitting
305. ICLR 2026Stable-LoRA: Stabilizing Feature Learning of Low-Rank Adaption
306. ICLR 2026STaMP: Sequence Transformation and Mixed Precision for Low-Precision Activation Quantization
307. ICLR 2026Study of Training Dynamics for Memory-Constrained Fine-Tuning
308. ICLR 2026SumRA: Parameter Efficient Fine-tuning with Singular Value Decomposition and Summed Orthogonal Basis
309. ICLR 2026Taming Momentum: Rethinking Optimizer States Through Low-Rank Approximation
310. ICLR 2026Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models
311. ICLR 2026The Power of Small Initialization in Noisy Low-Tubal-Rank Tensor Recovery
312. ICLR 2026The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning
313. ICLR 2026Three Forward, One Backward: Memory-Efficient Full-Rank Fine-Tuning of Large Models via Extra Forward Passes
314. ICLR 2026Tina: Tiny Reasoning Models via LoRA
315. ICLR 2026TiTok: Transfer Token-level Knowledge via Contrastive Excess to Transplant LoRA
316. ICLR 2026Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension
317. ICLR 2026Towards Understanding the Nature of Attention with Low-Rank Sparse Decomposition
318. ICLR 2026TRAC: Tensor-Train based Across-layer Compression for Parameter-Efficient Fine-Tuning
319. ICLR 2026Tractability via Low Dimensionality: The Parameterized Complexity of Training Quantized Neural Networks
320. ICLR 2026ULD-Net: Enabling Ultra-Low-Degree Fully Polynomial Networks for Homomorphically Encrypted Inference
321. ICLR 2026Unbalanced Soft-Matching Distance For Neural Representational Comparison With Partial Unit Correspondence
322. ICLR 2026Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility
323. ICLR 2026UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs
324. ICLR 2026Video Unlearning via Low-Rank Refusal Vector
325. ICLR 2026VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers
326. ICLR 2026What Layers When: Learning to Skip Compute in LLMs with Residual Gates
327. ICLR 2026When and Where to Reset Matters for Long-Term Test-Time Adaptation
328. ICLR 2026Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
329. ICLR 2026Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention
330. ICLR 2026WSVD: Weighted Low-Rank Approximation for Fast and Efficient Execution of Low-Precision Vision-Language Models
331. ICLR 2026ZeroSiam: An Efficient Siamese for Test-Time Entropy Optimization without Collapse
332. ICML 20261-Bit Wonder: Improving QAT Performance in the Low-Bit Regime through K-Means Quantization
333. ICML 2026A Factorized Low-Rank RNN Framework for Uncovering Independent Neural Latent Dynamics and Connectivity
334. ICML 2026A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search
335. ICML 2026A3: an Analytical Low-Rank Approximation Framework for Attention
336. ICML 2026Accelerating Q-learning through Efficient Value-Sharing across Actions
337. ICML 2026Active Learning with Low-Rank Structure for Data Selection
338. ICML 2026Adaptive Utilization of Low-Rank Adaptation via Conditioned Gating
339. ICML 2026AgentVocab: Structure-Aware Vocabulary Adaptation for Efficient LLM Agents
340. ICML 2026AGZO: Activation-Guided Zeroth-Order Optimization for LLM Fine-Tuning
341. ICML 2026ANTiC: Adaptive Neural Temporal In Situ Compressor
342. ICML 2026AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference
343. ICML 2026AOEPT: Breaking the Implicit Modality-Reduction Bottleneck in Modality-Missing Prompt Tuning
344. ICML 2026Approximating f -Divergences with Rank Statistics
345. ICML 2026AutoQRA: Joint Optimization of Mixed-Precision Quantization and Low-rank Adapters for Efficient LLM Fine-Tuning
346. ICML 2026Balanced LoRA: Removing Parameter Invariance to Accelerate Convergence
347. ICML 2026BAS: Bridging Adam and SignSGD for Memory-Efficient LLM Training
348. ICML 2026Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings
349. ICML 2026Bayesian Tensor Decomposition with Diffusion Model Prior
350. ICML 2026Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models

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.