低秩适配论文趋势 2023–2026

主要结论

• 低秩适配论文从 2023 年的 282 篇增长到 2026 年的 841 篇，三年间扩大到约 3.0 倍。
• NeurIPS 是发低秩适配论文最多的会议，2023–2026 年共 781 篇，占四个会议总量的 34%。
• 交叉方向里涨得最快的是扩散模型：同时属于两个主题的论文从 2023 年的 4 篇增至 2026 年的 61 篇。
• 在「优化与缩放」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的缩放定律是 4.1 倍，低秩适配是 3.0 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部低秩适配论文

共 518 篇，第 10 / 11 页

451. NeurIPS 2024OneBit: Towards Extremely Low-bit Large Language Models
452. NeurIPS 2024Online Adaptation of Language Models with a Memory of Amortized Contexts
453. NeurIPS 2024Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
454. NeurIPS 2024Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
455. NeurIPS 2024OTTER: Effortless Label Distribution Adaptation of Zero-shot Models
456. NeurIPS 2024PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
457. NeurIPS 2024Panacea: Pareto Alignment via Preference Adaptation for LLMs
458. NeurIPS 2024Parameter Competition Balancing for Model Merging
459. NeurIPS 2024Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts
460. NeurIPS 2024Parameter-free Clipped Gradient Descent Meets Polyak
461. NeurIPS 2024Personalized Federated Learning via Feature Distribution Adaptation
462. NeurIPS 2024PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
463. NeurIPS 2024Pre-Trained Multi-Goal Transformers with Prompt Optimization for Efficient Online Adaptation
464. NeurIPS 2024Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context
465. NeurIPS 2024PrivAuditor: Benchmarking Data Protection Vulnerabilities in LLM Adaptation Techniques
466. NeurIPS 2024Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
467. NeurIPS 2024Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
468. NeurIPS 2024QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
469. NeurIPS 2024Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models
470. NeurIPS 2024ReFT: Representation Finetuning for Language Models
471. NeurIPS 2024Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe
472. NeurIPS 2024Rethinking Misalignment in Vision-Language Model Adaptation from a Causal Perspective
473. NeurIPS 2024Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
474. NeurIPS 2024S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
475. NeurIPS 2024Safe LoRA: The Silver Lining of Reducing Safety Risks when Finetuning Large Language Models
476. NeurIPS 2024SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models
477. NeurIPS 2024Sample-Efficient Geometry Reconstruction from Euclidean Distances using Non-Convex Optimization
478. NeurIPS 2024Sample-Efficient Private Learning of Mixtures of Gaussians
479. NeurIPS 2024SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation
480. NeurIPS 2024SGD vs GD: Rank Deficiency in Linear Networks
481. NeurIPS 2024Shaping the distribution of neural responses with interneurons in a recurrent circuit model
482. NeurIPS 2024Sharing Key Semantics in Transformer Makes Efficient Image Restoration
483. NeurIPS 2024Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts
484. NeurIPS 2024Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information
485. NeurIPS 2024Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning
486. NeurIPS 2024SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining
487. NeurIPS 2024SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead
488. NeurIPS 2024Sparse High Rank Adapters
489. NeurIPS 2024Spectral Adapter: Fine-Tuning in Spectral Space
490. NeurIPS 2024SpeedLoader: An I/O efficient scheme for heterogeneous and distributed LLM operation
491. NeurIPS 2024Spiking Token Mixer: An event-driven friendly Former structure for spiking neural networks
492. NeurIPS 2024SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening
493. NeurIPS 2024Stepping Forward on the Last Mile
494. NeurIPS 2024Structured flexibility in recurrent neural networks via neuromodulation
495. NeurIPS 2024Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning
496. NeurIPS 2024SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
497. NeurIPS 2024Swift Sampler: Efficient Learning of Sampler by 10 Parameters
498. NeurIPS 2024Tensor-Based Synchronization and the Low-Rankness of the Block Trifocal Tensor
499. NeurIPS 2024Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line
500. NeurIPS 2024The Impact of Initialization on LoRA Finetuning Dynamics

常见问题

2023–2026 年四大会议一共有多少篇低秩适配论文？

共 2,273 篇，按年份分别是 2023 年 282 篇、2024 年 518 篇、2025 年 632 篇、2026 年 841 篇。

NeurIPS 2026 有多少篇低秩适配论文？

NeurIPS 2026 有 269 篇低秩适配论文，上一届是 241 篇。

ICLR 2026 有多少篇低秩适配论文？

ICLR 2026 有 184 篇低秩适配论文，上一届是 152 篇。

ICML 2026 有多少篇低秩适配论文？

ICML 2026 有 240 篇低秩适配论文，上一届是 136 篇。

CVPR 2026 有多少篇低秩适配论文？

CVPR 2026 有 148 篇低秩适配论文，上一届是 103 篇。

哪个会议的低秩适配论文最多？

NeurIPS，2023–2026 年共 781 篇。