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 14 of 17

651. NeurIPS 2026Efficient Algorithms For Fully Dynamic Bipartite Matching In Metric Spaces
652. NeurIPS 2026Efficient algorithms for linear regression with heteroskedastic errors
653. NeurIPS 2026Efficient Collaborative LLM Fine-Tuning over Heterogeneous Mobile Devices via Many Backbones to One Side-Network Tuning
654. NeurIPS 2026Efficient Diffusion Policy Fine Tuning with Latent Noise Representation Bridging
655. NeurIPS 2026Efficient Fine-Tuning for Structured Sparsity Under Group Repartitioning
656. NeurIPS 2026Efficient Learning of Truncated Boolean Product Distributions: Influence to the Rescue
657. NeurIPS 2026Efficient LLM Adaptation with Forward-Only Passes
658. NeurIPS 2026Efficient Pre-Training with Token Superposition
659. NeurIPS 2026Efficient SAM 3 Adaptation for Multi-Class Semantic Segmentation via Dense Competitive Representations
660. NeurIPS 2026Efficient Test-Time Adaptation For Robot Policies
661. NeurIPS 2026Efficiently Aligning Draft Models via Parameter- and Data-Efficient Adaptation
662. NeurIPS 2026Escaping Parameter Space: Tight Generalization Bounds via Representation Quality
663. NeurIPS 2026ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
664. NeurIPS 2026EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample
665. NeurIPS 2026Exploring Lifelong Adaptation: In-Context Reinforcement Learning in Non-Stationary Environments
666. NeurIPS 2026Extending 3D Reconstruction Models to Any Camera
667. NeurIPS 2026FacePhys: State of the Heart Learning
668. NeurIPS 2026Fast-Slow Evolutionary Occupancy Prediction via Controlled Dynamics
669. NeurIPS 2026FAUST: Federated Asynchronous Update with Staggered Timescales for Low-Communication Foundation Model Training
670. NeurIPS 2026FedIBS: Federated Vision-Language Adaptation via Intrinsic Bias Selection
671. NeurIPS 2026FedLoVA: Value-Only Aggregation for Federated LoRA Fine-Tuning of Large Language Models
672. NeurIPS 2026FedReCall: Recalling Client-Specific Directions in Federated LoRA Fine-tuning
673. NeurIPS 2026FELPS: Fair and Efficient Scheduling for Multi-LoRA Serving System
674. NeurIPS 2026FiLoRA: Focus-and-Ignore LoRA for Controllable Feature Reliance
675. NeurIPS 2026Filter Banks: from Low-Rank Representations to Deep Models for Efficient Time Series Forecasting
676. NeurIPS 2026Flash-KMeans: Fast and Memory-Efficient Exact K-Means
677. NeurIPS 2026FLoRA-Chef: Making A Good LoRA Recipe in Federated Generalization
678. NeurIPS 2026Follow the Mean: Reference-Guided Flow Matching
679. NeurIPS 2026Frank-LoRA: Federated Rank-Aware LoRA for Fine-Tuning Large Models
680. NeurIPS 2026From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs
681. NeurIPS 2026From Next-Token to Next-Block: A Principled Adaptation Path for Diffusion LLMs
682. NeurIPS 2026From sequences to schemas: low-rank recurrent dynamics underlie abstract relational representations
683. NeurIPS 2026From Weeks to Hours: Fast and Principled SFT Curation for LLM
684. NeurIPS 2026Full Fine-Tuning Is Not the Problem: Why Adam Fails and SGD Succeeds in Few-Shot CLIP Adaptation
685. NeurIPS 2026FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning
686. NeurIPS 2026Generalizing the Geometry of Model Merging Through Fréchet Averages
687. NeurIPS 2026Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes
688. NeurIPS 2026GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling
689. NeurIPS 2026Hadamard Representation: Scaffolding Performance Across Model-free RL
690. NeurIPS 2026HAPS: Hierarchical LLM Routing with Joint Architecture and Parameter Search
691. NeurIPS 2026Heavy-Tailed Flow Matching via Random Clocks
692. NeurIPS 2026HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs
693. NeurIPS 2026HiLoRA: Adaptive Hierarchical LoRA Routing for Training-Free Domain Generalization
694. NeurIPS 2026Hint Tuning: Less Data Makes Better Reasoners
695. NeurIPS 2026How Finite-Rank Bottleneck Shape the Low-Rank Adaptation Landscape
696. NeurIPS 2026Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training
697. NeurIPS 2026Hyperbolic Displacement-Constrained Adaptation for CLIP-Based Class-Incremental Learning
698. NeurIPS 2026Improving Quantized Zeroth-Order Optimization through Reconstructed Low-Rank Structures
699. NeurIPS 2026InduceKV: Fixed-Footprint Continual Adaptation of Multimodal LLMs via Inducing KV Memories
700. NeurIPS 2026Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation 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.