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 241 篇，第 1 / 5 页

1. NeurIPS 20253BASiL: An Algorithmic Framework for Sparse plus Low-Rank Compression of LLMs
2. NeurIPS 20253DPE-Gaze:Unlocking the Potential of 3D Facial Priors for Generalized Gaze Estimation
3. NeurIPS 2025A geometric framework for momentum-based optimizers for low-rank training
4. NeurIPS 2025A Novel General Framework for Sharp Lower Bounds in Succinct Stochastic Bandits
5. NeurIPS 2025A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone
6. NeurIPS 2025A*-Thought: Efficient Reasoning via Bidirectional Compression for Low-Resource Settings
7. NeurIPS 2025Accurate and Efficient Low-Rank Model Merging in Core Space
8. NeurIPS 2025Activated LoRA: Fine-tuned LLMs for Intrinsics
9. NeurIPS 2025Activity Pruning for Efficient Spiking Neural Networks
10. NeurIPS 2025AdaMSS: Adaptive Multi-Subspace Approach for Parameter-Efficient Fine-Tuning
11. NeurIPS 2025Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking
12. NeurIPS 2025Adaptive Fission: Post-training Encoding for Low-latency Spike Neural Networks
13. NeurIPS 2025Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
14. NeurIPS 2025Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning
15. NeurIPS 2025AegisGuard: RL-Guided Adapter Tuning for TEE-Based Efficient & Secure On-Device Inference
16. NeurIPS 2025AI Progress Should Be Measured by Capability-Per-Resource, Not Scale Alone: A Framework for Gradient-Guided Resource Allocation in LLMs
17. NeurIPS 2025All You Need is One: Capsule Prompt Tuning with a Single Vector
18. NeurIPS 2025AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
19. NeurIPS 2025Angles Don’t Lie: Unlocking Training‑Efficient RL Through the Model’s Own Signals
20. NeurIPS 2025AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
21. NeurIPS 2025Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment
22. NeurIPS 2025Bayes optimal learning of attention-indexed models
23. NeurIPS 2025Beyond Higher Rank: Token-wise Input-Output Projections for Efficient Low-Rank Adaptation
24. NeurIPS 2025Bilevel ZOFO: Efficient LLM Fine-Tuning and Meta-Training
25. NeurIPS 2025Block-Diagonal LoRA for Eliminating Communication Overhead in Tensor Parallel LoRA Serving
26. NeurIPS 2025Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretraining
27. NeurIPS 2025C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
28. NeurIPS 2025Cancer Survival Analysis via Zero-shot Tumor Microenvironment Segmentation on Low-resolution Whole Slide Pathology Images
29. NeurIPS 2025COALA: Numerically Stable and Efficient Framework for Context-Aware Low-Rank Approximation
30. NeurIPS 2025CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving
31. NeurIPS 2025CoIDO: Efficient Data Selection for Visual Instruction Tuning via Coupled Importance-Diversity Optimization
32. NeurIPS 2025Composing Linear Layers from Irreducibles
33. NeurIPS 2025Compress to Impress: Efficient LLM Adaptation Using a Single Gradient Step on 100 Samples
34. NeurIPS 2025Continual Knowledge Adaptation for Reinforcement Learning
35. NeurIPS 2025Controlled Visual Hallucination via Thalamus-Driven Decoupling Network for Domain Adaptation of Black-Box Predictors
36. NeurIPS 2025Correlated Low-Rank Adaptation for ConvNets
37. NeurIPS 2025CrossSpectra: Exploiting Cross-Layer Smoothness for Parameter-Efficient Fine-Tuning
38. NeurIPS 2025Curvature Tuning: Provable Training-free Model Steering From a Single Parameter
39. NeurIPS 2025D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction
40. NeurIPS 2025Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
41. NeurIPS 2025DEFT: Decompositional Efficient Fine-Tuning for Text-to-Image Models
42. NeurIPS 2025DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products
43. NeurIPS 2025Demystifying Language Model Forgetting with Low-rank Example Associations
44. NeurIPS 2025Dependency Parsing is More Parameter-Efficient with Normalization
45. NeurIPS 2025Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix
46. NeurIPS 2025Diffusion-Driven Two-Stage Active Learning for Low-Budget Semantic Segmentation
47. NeurIPS 2025Disentangling misreporting from genuine adaptation in strategic settings: a causal approach
48. NeurIPS 2025Distribution-Aligned Decoding for Efficient LLM Task Adaptation
49. NeurIPS 2025Distribution-Aware Tensor Decomposition for Compression of Convolutional Neural Networks
50. NeurIPS 2025DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment

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。