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

531 papers, page 4 of 11

151. NeurIPS 2026SPIRIT: Speed-Driven Online Adaptation for Self-Speculative Decoding
152. NeurIPS 2026Stability Regimes for Framing-Sensitive Fine-Tuning in Language Models
153. NeurIPS 2026Tabular Foundation Model for Generative Modelling
154. NeurIPS 2026TACO: Towards Task-Consistent Open-Vocabulary Adaptation in Video Recognition
155. NeurIPS 2026Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning
156. NeurIPS 2026The Geometry of Alignment Collapse: When Fine-Tuning Breaks Safety
157. NeurIPS 2026The Optimal Control Foundation of Early Exits – Turnpikes and ResNets
158. NeurIPS 2026The Surprising Effectiveness of Deleting Weights in LLM Reasoning and Adaptation
159. NeurIPS 2026Towards Identifying Dominant Low-Rank Subspaces in Zeroth-Order Fine-Tuning
160. NeurIPS 2026Towards Reconstructing Geographically Diverse Architecture with 3D Foundation Models
161. NeurIPS 2026Trainable Sparse Attention via Hybrid Top-k+Top-p Masking and Distillation Fine-Tuning
162. NeurIPS 2026Trust the Direction, Search the Step: Zero-and-First-Order Methods for LLM Fine-Tuning
163. NeurIPS 2026TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning
164. NeurIPS 2026Tune-Up Open-Weight CLIP: Optimization Framework for Self-Supervised Fine-tuning of CLIP
165. NeurIPS 2026Tuning the Tuner
166. NeurIPS 2026U-MOF: Uncertainty-Guided Parameter-Efficient Multi-Objective Fine-Tuning for Long-Tailed Recognition
167. NeurIPS 2026Uncertainty-Aware Listwise Reinforcement Fine-Tuning for Fine-Grained Visual Classification
168. NeurIPS 2026Unified Panoramic Geometry Estimation via Multi-View Foundation Models
169. NeurIPS 2026Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation
170. NeurIPS 2026Vibe-Spike: An Energy-Preserving EEG Foundation Model Through the Landscape of Neural Coherence
171. NeurIPS 2026What DNA Foundation Models Learn Beyond Sequence Composition
172. NeurIPS 2026What do EEG Foundation Models Capture from Human Brain Signals?
173. NeurIPS 2026What Does an Observability Forecasting Foundation Model Know?
174. NeurIPS 2026What Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective
175. NeurIPS 2026When Catastrophic Inheritance Meets Forgetting in Continual Adaptation of Foundation Models
176. NeurIPS 2026When does LeJEPA learn a World Model?
177. NeurIPS 2026When the Merge Coefficient Stops Mattering: Proximity Regularized Merging for Continual LoRA Adaptation
178. NeurIPS 2026When to Trust a PFN: Detecting Harmful Shift in Tabular Foundation Models
179. NeurIPS 2026Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?
180. NeurIPS 2026Where You Backpropagate Matters: Token Hypothesis for Memory-Efficient Fine-Tuning
181. NeurIPS 2026Who Needs Labels? Adapting Vision Foundation Models With the Metadata You Already Have
182. NeurIPS 2026You CAN Teach an Old Model New Tricks: Domain Adaptation via Complementary Subspace Expansion
183. NeurIPS 2025A Closer Look to Positive-Unlabeled Learning from Fine-grained Perspectives: An Empirical Study
184. NeurIPS 2025Activated LoRA: Fine-tuned LLMs for Intrinsics
185. NeurIPS 2025AdaLRS: Loss-Guided Adaptive Learning Rate Search for Efficient Foundation Model Pretraining
186. NeurIPS 2025AdaMSS: Adaptive Multi-Subspace Approach for Parameter-Efficient Fine-Tuning
187. NeurIPS 2025Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
188. NeurIPS 2025Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning
189. NeurIPS 2025AI Progress Should Be Measured by Capability-Per-Resource, Not Scale Alone: A Framework for Gradient-Guided Resource Allocation in LLMs
190. NeurIPS 2025AION-1: Omnimodal Foundation Model for Astronomical Sciences
191. NeurIPS 2025All You Need is One: Capsule Prompt Tuning with a Single Vector
192. NeurIPS 2025AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
193. NeurIPS 2025An Investigation of Memorization Risk in Healthcare Foundation Models
194. NeurIPS 2025Attack via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs
195. NeurIPS 2025AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
196. NeurIPS 2025Axial Neural Networks for Dimension-Free Foundation Models
197. NeurIPS 2025Beyond Higher Rank: Token-wise Input-Output Projections for Efficient Low-Rank Adaptation
198. NeurIPS 2025Bigram Subnetworks: Mapping to Next Tokens in Transformer Language Models
199. NeurIPS 2025BlockScan: Detecting Anomalies in Blockchain Transactions
200. NeurIPS 2025Boosting Skeleton-based Zero-Shot Action Recognition with Training-Free Test-Time Adaptation

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.