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

324 papers, page 5 of 7

201. ICML 2025TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
202. ICML 2025TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems
203. ICML 2025Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
204. ICML 2025Task-Agnostic Pre-training and Task-Guided Fine-tuning for Versatile Diffusion Planner
205. ICML 2025Test-time Adaptation on Graphs via Adaptive Subgraph-based Selection and Regularized Prototypes
206. ICML 2025Text-to-LoRA: Instant Transformer Adaption
207. ICML 2025The Double-Ellipsoid Geometry of CLIP
208. ICML 2025Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport
209. ICML 2025Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
210. ICML 2025Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
211. ICML 2025Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis
212. ICML 2025TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
213. ICML 2025Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
214. ICML 2025Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
215. ICML 2025WeGeFT: Weight‑Generative Fine‑Tuning for Multi‑Faceted Efficient Adaptation of Large Models
216. ICML 2025What Has a Foundation Model Found? Inductive Bias Reveals World Models
217. ICML 2025When Can Proxies Improve the Sample Complexity of Preference Learning?
218. ICML 2025Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models
219. ICML 2024A Closer Look at the Limitations of Instruction Tuning
220. ICML 2024A decoder-only foundation model for time-series forecasting
221. ICML 2024A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
222. ICML 2024Accelerating Transformer Pre-training with 2:4 Sparsity
223. ICML 2024Active Label Correction for Semantic Segmentation with Foundation Models
224. ICML 2024Asymmetry in Low-Rank Adapters of Foundation Models
225. ICML 2024Batch Singular Value Polarization and Weighted Semantic Augmentation for Universal Domain Adaptation
226. ICML 2024Bayesian Adaptation of Network Depth and Width for Continual Learning
227. ICML 2024Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models
228. ICML 2024By Tying Embeddings You Are Assuming the Distributional Hypothesis
229. ICML 2024Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
230. ICML 2024Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations
231. ICML 2024Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language Models
232. ICML 2024Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
233. ICML 2024Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
234. ICML 2024Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
235. ICML 2024DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
236. ICML 2024Differentially Private Bias-Term Fine-tuning of Foundation Models
237. ICML 2024Differentially Private Domain Adaptation with Theoretical Guarantees
238. ICML 2024DoRA: Weight-Decomposed Low-Rank Adaptation
239. ICML 2024DPZero: Private Fine-Tuning of Language Models without Backpropagation
240. ICML 2024Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning
241. ICML 2024ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections
242. ICML 2024Fine-grained Local Sensitivity Analysis of Standard Dot-Product Self-Attention
243. ICML 2024Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
244. ICML 2024FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
245. ICML 2024Foundation Policies with Hilbert Representations
246. ICML 2024From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning
247. ICML 2024Getting the most out of your tokenizer for pre-training and domain adaptation
248. ICML 2024Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method
249. ICML 2024Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions
250. ICML 2024LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views

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.