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 269 篇，第 2 / 6 页

51. NeurIPS 2026Communication-Efficient Personalized Adaptation via Federated-Local Model Merging
52. NeurIPS 2026Comp$^2$VLM: A Hybrid Framework Combining Quantization and Lossless Compression for Efficient Vision-Language Models
53. NeurIPS 2026Complementary Cache Guidance with Gradient Disentanglement for Continuous Test-Time Adaptation
54. NeurIPS 2026Complexity-Aware LoRA Aggregation for Modality-Heterogeneous Federated Person Re-identification
55. NeurIPS 2026Confidence-Based Decoding is Provably Efficient for Diffusion Language Models
56. NeurIPS 2026Context as Low-Rank Weights: Bounded Parametric Dynamic Memory for Unbounded Context
57. NeurIPS 2026Continuous Expert Assembly: Instance-Conditioned Low-Rank Residuals for All-in-One Image Restoration
58. NeurIPS 2026Controlling Temporal Pseudo-Label Marginals for Stable Online Test-Time Adaptation
59. NeurIPS 2026Convergence of Near-Linear Width ReLU Networks with Unbalanced Initialization
60. NeurIPS 2026CORA: Per-Slice Coherent Orthogonal Rotation for SVD-based Low-Rank Adaptation
61. NeurIPS 2026CPA: Efficient and Stable FP4 RL Training via Cross-Precision Alignment
62. NeurIPS 2026D-DOIT: Training-free Adaptation of Discrete Diffusion via Doob's h-Transform
63. NeurIPS 2026D$^2$Quant: Accurate Low-bit Post-Training Weight Quantization for LLMs
64. NeurIPS 2026DAGA: Dynamic Attention-Guided Adaptation for Self-Supervised Vision Transformers
65. NeurIPS 2026Decomposing the modulation of interactions between neuronal populations
66. NeurIPS 2026DEDCA: Test-Time Adaptation for Generalized AI-Generated Image Detection
67. NeurIPS 2026DeltaMomentum: A Key-Value based Anisotropic Momentum Update via Delta Rule
68. NeurIPS 2026Depth through Recurrence: Towards Ultra-Efficient On-Device ASR
69. NeurIPS 2026DiffATS: Diffusion in Aligned Tensor Space
70. NeurIPS 2026Diffusion Tree Search for Inference Time Adaptation of Material Foundation Models
71. NeurIPS 2026Dominant-Layer ZO: A Single Layer Dominates Zeroth-Order Fine-Tuning of LLMs
72. NeurIPS 2026Don’t Learn What You Can Compute: Arithmetic Residual Blocks for Exact Arithmetic in Transformers
73. NeurIPS 2026Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank
74. NeurIPS 2026DRIVE: Fine-tuning via Data Contribution- and Diversity-aware Weighting with Prior Regularization
75. NeurIPS 2026Dual-Pronged LoRA: Achieving Near-Zero Forgetting and High-Performance Adaptation for MLLMs
76. NeurIPS 2026Dynamic Model Merging Made Slim
77. NeurIPS 2026Dynamics of the Transformer Residual Stream: Coupling Spectral Geometry to Network Topology
78. NeurIPS 2026DyRA: Dynamic Residual Approximation for Efficient Matrix Multiplication in DNNs
79. NeurIPS 2026Editing Large Language Models with Geometry-Aware Regularization
80. NeurIPS 2026Efficient Algorithms For Fully Dynamic Bipartite Matching In Metric Spaces
81. NeurIPS 2026Efficient algorithms for linear regression with heteroskedastic errors
82. NeurIPS 2026Efficient Collaborative LLM Fine-Tuning over Heterogeneous Mobile Devices via Many Backbones to One Side-Network Tuning
83. NeurIPS 2026Efficient Diffusion Policy Fine Tuning with Latent Noise Representation Bridging
84. NeurIPS 2026Efficient Fine-Tuning for Structured Sparsity Under Group Repartitioning
85. NeurIPS 2026Efficient Learning of Truncated Boolean Product Distributions: Influence to the Rescue
86. NeurIPS 2026Efficient LLM Adaptation with Forward-Only Passes
87. NeurIPS 2026Efficient Pre-Training with Token Superposition
88. NeurIPS 2026Efficient SAM 3 Adaptation for Multi-Class Semantic Segmentation via Dense Competitive Representations
89. NeurIPS 2026Efficient Test-Time Adaptation For Robot Policies
90. NeurIPS 2026Efficiently Aligning Draft Models via Parameter- and Data-Efficient Adaptation
91. NeurIPS 2026Escaping Parameter Space: Tight Generalization Bounds via Representation Quality
92. NeurIPS 2026ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
93. NeurIPS 2026EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample
94. NeurIPS 2026Exploring Lifelong Adaptation: In-Context Reinforcement Learning in Non-Stationary Environments
95. NeurIPS 2026Extending 3D Reconstruction Models to Any Camera
96. NeurIPS 2026FacePhys: State of the Heart Learning
97. NeurIPS 2026Fast-Slow Evolutionary Occupancy Prediction via Controlled Dynamics
98. NeurIPS 2026FAUST: Federated Asynchronous Update with Staggered Timescales for Low-Communication Foundation Model Training
99. NeurIPS 2026FedIBS: Federated Vision-Language Adaptation via Intrinsic Bias Selection
100. NeurIPS 2026FedLoVA: Value-Only Aggregation for Federated LoRA Fine-Tuning of Large Language Models

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。