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

2,272 papers, page 17 of 46

801. NeurIPS 2026SPIRIT: Speed-Driven Online Adaptation for Self-Speculative Decoding
802. NeurIPS 2026SR-GRPO: Stable Rank as an Intrinsic Geometric Reward for Large Language Model Alignment
803. NeurIPS 2026Stabilizing the Dynamic Low-Rank Training
804. NeurIPS 2026STAC-R: Subspace-Tracked Activation Compression with Residuals
805. NeurIPS 2026State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
806. NeurIPS 2026Stealthy World Model Manipulation via Data Poisoning
807. NeurIPS 2026TailAdapt: Heavy-Tailed Sparse Variational Adaptation for Long-Tailed Class Incremental Learning
808. NeurIPS 2026Temporal Smoothness Constraints on Efficient Neurobiological Codes Imply Temporal Specialization
809. NeurIPS 2026Test-time Risk Adaptation with Mixture of Agents
810. NeurIPS 2026The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
811. NeurIPS 2026The Hidden Power of Scaling Factor in LoRA Optimization
812. NeurIPS 2026The Illusion of Forgetting: Rank Leakage in Knowledge Editing and Its Mitigation
813. NeurIPS 2026The Override Gap: A Magnitude Account of Knowledge Conflict Failure in Hypernetwork-Based Instant LLM Adaptation
814. NeurIPS 2026The sublevel Flood bifiltration: towards scalable 2-parameter persistent homology
815. NeurIPS 2026The Surprising Effectiveness of Deleting Weights in LLM Reasoning and Adaptation
816. NeurIPS 2026TILT: Target-induced loss tilting under covariate shift
817. NeurIPS 2026Time-Frequency Decoupled Cross-Scale Partial Optimal Transport for Time Series Domain Adaptation
818. NeurIPS 2026TopoFisher: Learning Topological Summary Statistics by Maximizing Fisher Information
819. NeurIPS 2026Towards Identifying Dominant Low-Rank Subspaces in Zeroth-Order Fine-Tuning
820. NeurIPS 2026Towards Unified Memory Adaptation for LLM Agents: Textual, Latent, and Parametric Pathways
821. NeurIPS 2026Trainable Sparse Attention via Hybrid Top-k+Top-p Masking and Distillation Fine-Tuning
822. NeurIPS 2026TriAxialKV: Toward Extreme Low-Precision KV-Cache Quantization for Agentic Inference Tasks
823. NeurIPS 2026Tuning the Tuner
824. NeurIPS 2026U-MOF: Uncertainty-Guided Parameter-Efficient Multi-Objective Fine-Tuning for Long-Tailed Recognition
825. NeurIPS 2026Uncovering Hidden Propensities in Language Models via Limited-Parameter Finetuning
826. NeurIPS 2026Uniform Spectral Growth under Factor-wise Muon Orthogonalization in Matrix Factorization and LoRA
827. NeurIPS 2026Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation
828. NeurIPS 2026Valid Best-Model Identification for LLM Evaluation via Low-Rank Factorization
829. NeurIPS 2026VALOR: Vector-Aware Low-Rank Restructuring of Neural Networks for RISC-V Inference
830. NeurIPS 2026Values as Style: Disentangling Values from Semantics with One-Way Mixing for Low-Damage LLM Steering
831. NeurIPS 2026Virtual Head Attention
832. NeurIPS 2026What Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective
833. NeurIPS 2026When Are Semivalue-Based Decisions Identifiable? Robust Data Selection under Utility Ambiguity
834. NeurIPS 2026When Does Fine-Tuning Extract Stored Knowledge? A Relation-Covering Theory for One-Layer Transformers
835. NeurIPS 2026When Does Subspace Direction Matter for LoRA? Regime Analysis of the Magnitude Principle in Few-Shot Adaptation
836. NeurIPS 2026When the Merge Coefficient Stops Mattering: Proximity Regularized Merging for Continual LoRA Adaptation
837. NeurIPS 2026When the Same Coefficients Reach Different Places: Asymmetric Realizability in Transplanting Tokenizers across Large Language Models
838. NeurIPS 2026Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?
839. NeurIPS 2026Where You Backpropagate Matters: Token Hypothesis for Memory-Efficient Fine-Tuning
840. NeurIPS 2026You CAN Teach an Old Model New Tricks: Domain Adaptation via Complementary Subspace Expansion
841. CVPR 20253D Occupancy Prediction with Low-Resolution Queries via Prototype-aware View Transformation
842. CVPR 2025AdaDARE-gamma: Balancing Stability and Plasticity in Multi-modal LLMs through Efficient Adaptation
843. CVPR 2025Adaptive Parameter Selection for Tuning Vision-Language Models
844. CVPR 2025AdMiT: Adaptive Multi-Source Tuning in Dynamic Environments
845. CVPR 2025Bayesian Test-Time Adaptation for Vision-Language Models
846. CVPR 2025BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning
847. CVPR 2025Breaking the Low-Rank Dilemma of Linear Attention
848. CVPR 2025CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning
849. CVPR 2025CoA: Towards Real Image Dehazing via Compression-and-Adaptation
850. CVPR 2025COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection

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.