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

602 papers, page 4 of 13

151. ICLR 2026Escaping Policy Contraction: Contraction-Aware PPO (CaPPO) for Stable Language Model Fine-Tuning
152. ICLR 2026Explainable Token-level Noise Filtering for LLM Fine-tuning Datasets
153. ICLR 2026Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts
154. ICLR 2026Exploring the Basin-Like Loss Landscape in Large Language Models
155. ICLR 2026Fine-Grained Activation Steering: Steering Less, Achieving More
156. ICLR 2026Fine-tuning Behavioral Cloning Policies with Preference‑Based Reinforcement Learning
157. ICLR 2026Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
158. ICLR 2026Fine-tuning Done Right in Model Editing
159. ICLR 2026Fine-tuning Quantized Neural Networks with Zeroth-order Optimization
160. ICLR 2026FlexLoRA: Entropy-Guided Flexible Low-Rank Adaptation
161. ICLR 2026Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
162. ICLR 2026FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment
163. ICLR 2026FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics
164. ICLR 2026FoNE: Precise Single-Token Number Embeddings via Fourier Features
165. ICLR 2026FZOO: Fast Zeroth-Order Optimizer for Fine‑Tuning Large Language Models towards Adam‑Scale Speed
166. ICLR 2026GIT-BO: High-Dimensional Bayesian Optimization with Tabular Foundation Models
167. ICLR 2026Gradient Intrinsic Dimensionality Alignment：Narrowing The Gap Between Low-Rank Adaptation and Full Fine-Tuning
168. ICLR 2026Gradient-Sign Masking for Task Vector Transport Across Pre-Trained Models
169. ICLR 2026GranViT: A Fine-Grained Vision Model With Autoregressive Perception For MLLMs
170. ICLR 2026Heterogeneous Federated Fine-Tuning with Parallel One-Rank Adaptation
171. ICLR 2026High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning
172. ICLR 2026Holdout-Loss-Based Data Selection for LLM Finetuning via In-Context Learning
173. ICLR 2026Homeostatic Adaptation of Optimal Population Codes under Metabolic Stress
174. ICLR 2026How Base Frequency Shapes RoPE: An Analytical Study of Frequency-Band Formation
175. ICLR 2026HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs
176. ICLR 2026IA2: Alignment with ICL Activations improves Supervised Fine-Tuning
177. ICLR 2026IDEAL: Data Equilibrium Adaptation for Multi-Capability Language Model Alignment
178. ICLR 2026IGU-LoRA: Adaptive Rank Allocation via Integrated Gradients and Uncertainty-Aware Scoring
179. ICLR 2026Improving Block-Wise LLM Quantization by 4-bit Block-Wise Optimal Float (BOF4): Analysis and Variations
180. ICLR 2026IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation
181. ICLR 2026In-Context Learning of Temporal Point Processes with Foundation Inference Models
182. ICLR 2026Is Finer Better? The Limits of Microscaling Formats in Large Language Models
183. ICLR 2026Joint Adaptation of Uni-modal Foundation Models for Multi-modal Alzheimer's Disease Diagnosis
184. ICLR 2026KeepLoRA: Continual Learning with Residual Gradient Adaptation
185. ICLR 2026Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions
186. ICLR 2026LinguaMap: Which Layers of LLMs Speak Your Language and How to Tune Them?
187. ICLR 2026LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning
188. ICLR 2026Log-Linear Attention
189. ICLR 2026Long-tailed Test-Time Adaptation for Vision-Language Models
190. ICLR 2026LoRA meets Riemannion: Muon Optimizer for Parametrization-independent Low-Rank Adapters
191. ICLR 2026LoRA-S: An Efficient Low Rank Adaptation scheme via Sylvester equation
192. ICLR 2026Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?
193. ICLR 2026Low rank adaptation of chemical foundation models generate effective odorant representations
194. ICLR 2026Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization
195. ICLR 2026Memory-Free Continual Learning with Null Space Adaptation for Zero-Shot Vision-Language Models
196. ICLR 2026MergeTune: Continued Fine-Tuning of Vision-Language Models
197. ICLR 2026Meta-UCF: Unified Task-Conditioned LoRA Generation for Continual Learning in Large Language Models
198. ICLR 2026MetaVLA: Unified Meta Co-Training for Efficient Embodied Adaptation
199. ICLR 2026MiSS: Revisiting the Trade-off in LoRA with an Efficient Shard-Sharing Structure
200. ICLR 2026Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity

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.