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 174 篇，第 4 / 4 页

151. NeurIPS 2025The Primacy of Magnitude in Low-Rank Adaptation
152. NeurIPS 2025This Time is Different: An Observability Perspective on Time Series Foundation Models
153. NeurIPS 2025To Think or Not To Think: A Study of Thinking in Rule-Based Visual Reinforcement Fine-Tuning
154. NeurIPS 2025Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper
155. NeurIPS 2025Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement
156. NeurIPS 2025Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs
157. NeurIPS 2025Towards Robust Parameter-Efficient Fine-Tuning for Federated Learning
158. NeurIPS 2025Towards the Resistance of Neural Network Fingerprinting to Fine-tuning
159. NeurIPS 2025Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning
160. NeurIPS 2025Triplets Better Than Pairs: Towards Stable and Effective Self-Play Fine-Tuning for LLMs
161. NeurIPS 2025TS-MOF: Two-Stage Multi-Objective Fine-tuning for Long-Tailed Recognition
162. NeurIPS 2025UFT: Unifying Supervised and Reinforcement Fine-Tuning
163. NeurIPS 2025UGM2N: An Unsupervised and Generalizable Mesh Movement Network via M-Uniform Loss
164. NeurIPS 2025Understanding Prompt Tuning and In-Context Learning via Meta-Learning
165. NeurIPS 2025Uni-LoRA: One Vector is All You Need
166. NeurIPS 2025Unified Multimodal Chain-of-Thought Reward Model through Reinforcement Fine-Tuning
167. NeurIPS 2025Unified Transferability Metrics for Time Series Foundation Models
168. NeurIPS 2025UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces
169. NeurIPS 2025Unleashing Foundation Vision Models: Adaptive Transfer for Diverse Data-Limited Scientific Domains
170. NeurIPS 2025VESSA: Video-based objEct-centric Self-Supervised Adaptation for Visual Foundation Models
171. NeurIPS 2025VIPAMIN: Visual Prompt Initialization via Embedding Selection and Subspace Expansion
172. NeurIPS 2025Walking the Tightrope: Autonomous Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuning
173. NeurIPS 2025What Matters in Data for DPO?
174. NeurIPS 2025Whose Instructions Count? Resolving Preference Bias in Instruction Fine-Tuning

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。