Foundation Model Adaptation papers, 2023–2026

Key findings

• Foundation Model Adaptation went from 159 papers in 2023 to 602 in 2026, roughly 3.8x in three years.
• NeurIPS publishes the most Foundation Model Adaptation work: 531 papers from 2023 to 2026, 36% of the four-venue total.
• The fastest-growing overlap is with Deep learning · foundation models: papers tagged with both rose from 7 in 2023 to 66 in 2026.
• Among the 5 topics under Meta-Learning and Transfer, it ranks #1 by 2026 paper count; the fastest grower in the group, Continual Learning, is up 1.7x versus 3.8x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Foundation Model Adaptation papers

1,492 papers, page 24 of 30

1151. ICLR 2024Neural Fine-Tuning Search for Few-Shot Learning
1152. ICLR 2024OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning
1153. ICLR 2024Pre-Training and Fine-Tuning Generative Flow Networks
1154. ICLR 2024Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting
1155. ICLR 2024Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features
1156. ICLR 2024QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
1157. ICLR 2024Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives
1158. ICLR 2024The False Promise of Imitating Proprietary Language Models
1159. ICLR 2024Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
1160. ICLR 2024Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
1161. ICLR 2024Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation
1162. ICLR 2024Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition
1163. ICLR 2024Two-stage LLM Fine-tuning with Less Specialization and More Generalization
1164. ICLR 2024UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science
1165. ICLR 2024VeRA: Vector-based Random Matrix Adaptation
1166. ICLR 2024When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
1167. ICLR 2024WizardCoder: Empowering Code Large Language Models with Evol-Instruct
1168. ICLR 2024WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions
1169. ICLR 2024Zero-Shot Robustification of Zero-Shot Models
1170. ICML 2024A Closer Look at the Limitations of Instruction Tuning
1171. ICML 2024A decoder-only foundation model for time-series forecasting
1172. ICML 2024A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
1173. ICML 2024Accelerating Transformer Pre-training with 2:4 Sparsity
1174. ICML 2024Active Label Correction for Semantic Segmentation with Foundation Models
1175. ICML 2024Asymmetry in Low-Rank Adapters of Foundation Models
1176. ICML 2024Batch Singular Value Polarization and Weighted Semantic Augmentation for Universal Domain Adaptation
1177. ICML 2024Bayesian Adaptation of Network Depth and Width for Continual Learning
1178. ICML 2024Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models
1179. ICML 2024By Tying Embeddings You Are Assuming the Distributional Hypothesis
1180. ICML 2024Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
1181. ICML 2024Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations
1182. ICML 2024Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language Models
1183. ICML 2024Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
1184. ICML 2024Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
1185. ICML 2024Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
1186. ICML 2024DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
1187. ICML 2024Differentially Private Bias-Term Fine-tuning of Foundation Models
1188. ICML 2024Differentially Private Domain Adaptation with Theoretical Guarantees
1189. ICML 2024DoRA: Weight-Decomposed Low-Rank Adaptation
1190. ICML 2024DPZero: Private Fine-Tuning of Language Models without Backpropagation
1191. ICML 2024Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning
1192. ICML 2024ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections
1193. ICML 2024Fine-grained Local Sensitivity Analysis of Standard Dot-Product Self-Attention
1194. ICML 2024Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
1195. ICML 2024FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
1196. ICML 2024Foundation Policies with Hilbert Representations
1197. ICML 2024From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning
1198. ICML 2024Getting the most out of your tokenizer for pre-training and domain adaptation
1199. ICML 2024Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method
1200. ICML 2024Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions

FAQ

How many Foundation Model Adaptation papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,492 in total: 159 in 2023, 270 in 2024, 461 in 2025, 602 in 2026.

How many Foundation Model Adaptation papers are at NeurIPS 2026?

NeurIPS 2026 has 182 Foundation Model Adaptation papers, versus 174 the year before.

How many Foundation Model Adaptation papers are at ICLR 2026?

ICLR 2026 has 187 Foundation Model Adaptation papers, versus 149 the year before.

How many Foundation Model Adaptation papers are at ICML 2026?

ICML 2026 has 142 Foundation Model Adaptation papers, versus 76 the year before.

How many Foundation Model Adaptation papers are at CVPR 2026?

CVPR 2026 has 91 Foundation Model Adaptation papers, versus 62 the year before.

Which conference publishes the most Foundation Model Adaptation papers?

NeurIPS, with 531 papers from 2023 to 2026.