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 142 篇，第 2 / 3 页

51. ICML 2026FedPissa: Towards Federated Personalized Adaptation of Foundation Models via LoRA Subspace Mapping
52. ICML 2026FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning
53. ICML 2026Fine-Tuning of Transformer models with Frames
54. ICML 2026Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models
55. ICML 2026Fingerprinting Pre-trained Encoders under Arbitrary Downstream Fine-Tuning via Adversarial Shifting
56. ICML 2026FIRE: Multi-Fidelity Regression with Distribution-Conditioned In-Context Learning Using Tabular Foundation Models
57. ICML 2026FiX: Introducing Fine-grained Forget Gate into Softmax Attention
58. ICML 2026Foundation Inference Models for Ordinary Differential Equations
59. ICML 2026From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning
60. ICML 2026Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in RLVR
61. ICML 2026GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning
62. ICML 2026GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
63. ICML 2026GPan-LoRA: Gaussian Process Amortized Networks for Bayesian Low-Rank Adaptation in Large Language Models
64. ICML 2026GR-LoRA: Gradient-Recycling Low-Rank Adaptation for Class-Incremental Learning
65. ICML 2026Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control
66. ICML 2026ICR-RL: Deep Reinforcement Learning via In-Context-Regression
67. ICML 2026iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis
68. ICML 2026Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models
69. ICML 2026JANUS-LORA: A Balanced Low-Rank Adaptation for Continual Learning
70. ICML 2026KromHC: Manifold-Constrained Hyper-Connections with Kronecker-Product Residual Matrices
71. ICML 2026Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs
72. ICML 2026Learning Graph Foundation Models on Riemannian Graph-of-Graphs
73. ICML 2026Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning
74. ICML 2026LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models
75. ICML 2026Low Kruskal-Rank Adaptation
76. ICML 2026LOZO+: Provably Efficient Zeroth-Order Fine-Tuning via Greedy Low-Rank Subspace Selection
77. ICML 2026Mantis: Lightweight Foundation Model for Time Series Classification
78. ICML 2026MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
79. ICML 2026Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective
80. ICML 2026MetaPerch: Learning from metadata for bioacoustics foundation models
81. ICML 2026Midtraining Bridges Pretraining and Posttraining Distributions
82. ICML 2026MODEL SOUPS NEED ONLY ONE INGREDIENT
83. ICML 2026Multi-Label Test-Time Adaptation with Bayesian Conditional Priors
84. ICML 2026MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning
85. ICML 2026NeuroCLUS: A Foundation Model with Functional Clustering for Intracranial Neural Decoding
86. ICML 2026No Need to Train Your RDB Foundation Model
87. ICML 2026Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density
88. ICML 2026Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model
89. ICML 2026On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models
90. ICML 2026Origo: Interpretable Multi-physics PDE Foundation Model through Neural Operator Splitting
91. ICML 2026PATCHCODE: Discrete Latent Predictive Learning for EEG Foundation Model
92. ICML 2026PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
93. ICML 2026PISA: Privacy-Preserving Split Adaptation with Model IP Protection
94. ICML 2026PixCLIP: Towards Fine-grained Vision-Language Understanding via Any-granularity Pixel-Text Alignment
95. ICML 2026PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning
96. ICML 2026PLoRA: Efficient Concurrent LoRA Training for Large Language Models
97. ICML 2026Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies
98. ICML 2026Progressive Graph Structure Adjustment for Homophily Shift Adaptation
99. ICML 2026Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles
100. ICML 2026Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting

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。