基础模型适配论文趋势 2023–2026

主要结论

• 基础模型适配论文从 2023 年的 159 篇增长到 2026 年的 602 篇，三年间扩大到约 3.8 倍。
• NeurIPS 是发基础模型适配论文最多的会议，2023–2026 年共 531 篇，占四个会议总量的 36%。
• 交叉方向里涨得最快的是深度学习 · foundation models：同时属于两个主题的论文从 2023 年的 7 篇增至 2026 年的 66 篇。
• 在「元学习与迁移」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的持续学习是 1.7 倍，基础模型适配是 3.8 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(Mis)generalization of Helpful-Only Fine-Tuning
• ICML 2026Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
• ICLR 2026Benchmarking ECG Foundational Models: A Reality Check Across Clinical Tasks
• CVPR 2026CD-Buffer Complementary Dual-Buffer Framework for Test-Time Adaptation in Adverse Weather
• NeurIPS 2025An Investigation of Memorization Risk in Healthcare Foundation Models
• ICML 2025A Bayesian Model Selection Criterion for Selecting Pretraining Checkpoints
• ICLR 2025Better autoregressive regression with LLMs via regression-aware fine-tuning
• CVPR 20255%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks
• NeurIPS 2024Instruction Tuning Large Language Models to Understand Electronic Health Records
• ICML 2024By Tying Embeddings You Are Assuming the Distributional Hypothesis
• ICLR 2024A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
• CVPR 2024DePT: Decoupled Prompt Tuning
• NeurIPS 2023A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
• ICML 2023Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations
• ICLR 2023A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias
• CVPR 2023A-La-Carte Prompt Tuning (APT): Combining Distinct Data via Composable Prompting
• NeurIPS 2026A Graph Foundation Model for Unified Clustering
• ICML 2026AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments
• ICLR 2026Bridging Radiology and Pathology Foundation Models via Concept-Based Multimodal Co-Adaptation
• CVPR 2026Fed-ADE Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift

全部基础模型适配论文

共 159 篇，第 2 / 4 页

51. ICML 2023Cross-Modal Fine-Tuning: Align then Refine
52. ICML 2023Curious Replay for Model-based Adaptation
53. ICML 2023Double-Weighting for Covariate Shift Adaptation
54. ICML 2023Efficient Personalized Federated Learning via Sparse Model-Adaptation
55. ICML 2023Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization
56. ICML 2023Exploring the Benefits of Training Expert Language Models over Instruction Tuning
57. ICML 2023Flash: Concept Drift Adaptation in Federated Learning
58. ICML 2023Flexible Model Aggregation for Quantile Regression
59. ICML 2023How Many Perturbations Break This Model? Evaluating Robustness Beyond Adversarial Accuracy
60. ICML 2023Human-Timescale Adaptation in an Open-Ended Task Space
61. ICML 2023In Search for a Generalizable Method for Source Free Domain Adaptation
62. ICML 2023LegendreTron: Uprising Proper Multiclass Loss Learning
63. ICML 2023Magneto: A Foundation Transformer
64. ICML 2023Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning
65. ICML 2023MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
66. ICML 2023Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning
67. ICML 2023Model Transferability with Responsive Decision Subjects
68. ICML 2023mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
69. ICML 2023On the Power of Foundation Models
70. ICML 2023On the Role of Attention in Prompt-tuning
71. ICML 2023Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice
72. ICML 2023RLSbench: Domain Adaptation Under Relaxed Label Shift
73. ICML 2023SinDDM: A Single Image Denoising Diffusion Model
74. ICML 2023Structural Re-weighting Improves Graph Domain Adaptation
75. ICML 2023Target-Aware Generative Augmentations for Single-Shot Adaptation
76. ICML 2023Task-Specific Skill Localization in Fine-tuned Language Models
77. ICML 2023Taxonomy-Structured Domain Adaptation
78. ICML 2023Test-time Adaptation with Slot-Centric Models
79. NeurIPS 2023A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
80. NeurIPS 2023A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm
81. NeurIPS 2023AdaVAE: Bayesian Structural Adaptation for Variational Autoencoders
82. NeurIPS 2023BayesTune: Bayesian Sparse Deep Model Fine-tuning
83. NeurIPS 2023Benchmarking Foundation Models with Language-Model-as-an-Examiner
84. NeurIPS 2023Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models
85. NeurIPS 2023Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations
86. NeurIPS 2023Brant: Foundation Model for Intracranial Neural Signal
87. NeurIPS 2023Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach
88. NeurIPS 2023Combinatorial Optimization with Policy Adaptation using Latent Space Search
89. NeurIPS 2023Composing Parameter-Efficient Modules with Arithmetic Operation
90. NeurIPS 2023Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement
91. NeurIPS 2023Context-guided Embedding Adaptation for Effective Topic Modeling in Low-Resource Regimes
92. NeurIPS 2023Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation
93. NeurIPS 2023Customizable Image Synthesis with Multiple Subjects
94. NeurIPS 2023Data Portraits: Recording Foundation Model Training Data
95. NeurIPS 2023Data Selection for Language Models via Importance Resampling
96. NeurIPS 2023Demographic Parity Constrained Minimax Optimal Regression under Linear Model
97. NeurIPS 2023Don’t Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner
98. NeurIPS 2023DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics
99. NeurIPS 2023Efficient Low-rank Backpropagation for Vision Transformer Adaptation
100. NeurIPS 2023EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models

常见问题

2023–2026 年四大会议一共有多少篇基础模型适配论文？

共 1,492 篇，按年份分别是 2023 年 159 篇、2024 年 270 篇、2025 年 461 篇、2026 年 602 篇。

NeurIPS 2026 有多少篇基础模型适配论文？

NeurIPS 2026 有 182 篇基础模型适配论文，上一届是 174 篇。

ICLR 2026 有多少篇基础模型适配论文？

ICLR 2026 有 187 篇基础模型适配论文，上一届是 149 篇。

ICML 2026 有多少篇基础模型适配论文？

ICML 2026 有 142 篇基础模型适配论文，上一届是 76 篇。

CVPR 2026 有多少篇基础模型适配论文？

CVPR 2026 有 91 篇基础模型适配论文，上一届是 62 篇。

哪个会议的基础模型适配论文最多？

NeurIPS，2023–2026 年共 531 篇。