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

270 papers, page 5 of 6

201. NeurIPS 2024Fight Back Against Jailbreaking via Prompt Adversarial Tuning
202. NeurIPS 2024Fine-Tuning is Fine, if Calibrated
203. NeurIPS 2024Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
204. NeurIPS 2024Flaws can be Applause: Unleashing Potential of Segmenting Ambiguous Objects in SAM
205. NeurIPS 2024FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations
206. NeurIPS 2024FM-Delta: Lossless Compression for Storing Massive Fine-tuned Foundation Models
207. NeurIPS 2024Free Lunch in Pathology Foundation Model: Task-specific Model Adaptation with Concept-Guided Feature Enhancement
208. NeurIPS 2024Frozen-DETR: Enhancing DETR with Image Understanding from Frozen Foundation Models
209. NeurIPS 2024GFT: Graph Foundation Model with Transferable Tree Vocabulary
210. NeurIPS 2024HEMM: Holistic Evaluation of Multimodal Foundation Models
211. NeurIPS 2024Historical Test-time Prompt Tuning for Vision Foundation Models
212. NeurIPS 2024HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning
213. NeurIPS 2024Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation
214. NeurIPS 2024Instruction Tuning Large Language Models to Understand Electronic Health Records
215. NeurIPS 2024Interfacing Foundation Models' Embeddings
216. NeurIPS 2024Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models.
217. NeurIPS 2024Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates
218. NeurIPS 2024KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
219. NeurIPS 2024Learning from Offline Foundation Features with Tensor Augmentations
220. NeurIPS 2024LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
221. NeurIPS 2024Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
222. NeurIPS 2024LoFiT: Localized Fine-tuning on LLM Representations
223. NeurIPS 2024Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation
224. NeurIPS 2024LoQT: Low-Rank Adapters for Quantized Pretraining
225. NeurIPS 2024LoRA-GA: Low-Rank Adaptation with Gradient Approximation
226. NeurIPS 2024MeshXL: Neural Coordinate Field for Generative 3D Foundation Models
227. NeurIPS 2024MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map
228. NeurIPS 2024Model Fusion through Bayesian Optimization in Language Model Fine-Tuning
229. NeurIPS 2024Multi-modal Transfer Learning between Biological Foundation Models
230. NeurIPS 2024Order-Independence Without Fine Tuning
231. NeurIPS 2024PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
232. NeurIPS 2024Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
233. NeurIPS 2024PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
234. NeurIPS 2024Poseidon: Efficient Foundation Models for PDEs
235. NeurIPS 2024Pre-training Differentially Private Models with Limited Public Data
236. NeurIPS 2024Predicting the Performance of Foundation Models via Agreement-on-the-Line
237. NeurIPS 2024PrivAuditor: Benchmarking Data Protection Vulnerabilities in LLM Adaptation Techniques
238. NeurIPS 2024Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
239. NeurIPS 2024Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
240. NeurIPS 2024QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
241. NeurIPS 2024RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations for Universal Robustness
242. NeurIPS 2024Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models
243. NeurIPS 2024Retrieval & Fine-Tuning for In-Context Tabular Models
244. NeurIPS 2024Risk-Averse Fine-tuning of Large Language Models
245. NeurIPS 2024S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
246. NeurIPS 2024Segment Any Change
247. NeurIPS 2024Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models
248. NeurIPS 2024Shaping the distribution of neural responses with interneurons in a recurrent circuit model
249. NeurIPS 2024SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
250. NeurIPS 2024SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models

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.