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 6 of 7

251. ICML 2024Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning
252. ICML 2024Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
253. ICML 2024LoRA Training in the NTK Regime has No Spurious Local Minima
254. ICML 2024LoRA+: Efficient Low Rank Adaptation of Large Models
255. ICML 2024MOMENT: A Family of Open Time-series Foundation Models
256. ICML 2024Not all distributional shifts are equal: Fine-grained robust conformal inference
257. ICML 2024On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis
258. ICML 2024Open-Vocabulary Calibration for Fine-tuned CLIP
259. ICML 2024Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation
260. ICML 2024Parameter-Efficient Fine-Tuning with Controls
261. ICML 2024Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
262. ICML 2024Position: Foundation Agents as the Paradigm Shift for Decision Making
263. ICML 2024Position: Graph Foundation Models Are Already Here
264. ICML 2024Position: On the Societal Impact of Open Foundation Models
265. ICML 2024Position: Why Tabular Foundation Models Should Be a Research Priority
266. ICML 2024Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
267. ICML 2024Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis
268. ICML 2024Prompting a Pretrained Transformer Can Be a Universal Approximator
269. ICML 2024Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models
270. ICML 2024Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning
271. ICML 2024Recovering the Pre-Fine-Tuning Weights of Generative Models
272. ICML 2024Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation
273. ICML 2024Revisiting the Power of Prompt for Visual Tuning
274. ICML 2024Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models
275. ICML 2024Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models
276. ICML 2024RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation
277. ICML 2024Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models
278. ICML 2024SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation
279. ICML 2024Selecting Large Language Model to Fine-tune via Rectified Scaling Law
280. ICML 2024Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
281. ICML 2024Sparse is Enough in Fine-tuning Pre-trained Large Language Models
282. ICML 2024SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
283. ICML 2024SuDA: Support-based Domain Adaptation for Sim2Real Hinge Joint Tracking with Flexible Sensors
284. ICML 2024To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO
285. ICML 2024Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
286. ICML 2024Transferring Knowledge From Large Foundation Models to Small Downstream Models
287. ICML 2024Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
288. ICML 2024Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
289. ICML 2023A Kernel-Based View of Language Model Fine-Tuning
290. ICML 2023AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
291. ICML 2023Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations
292. ICML 2023Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions
293. ICML 2023ClimaX: A foundation model for weather and climate
294. ICML 2023CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks
295. ICML 2023Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models
296. ICML 2023Constant Matters: Fine-grained Error Bound on Differentially Private Continual Observation
297. ICML 2023Cross-Modal Fine-Tuning: Align then Refine
298. ICML 2023Curious Replay for Model-based Adaptation
299. ICML 2023Double-Weighting for Covariate Shift Adaptation
300. ICML 2023Efficient Personalized Federated Learning via Sparse Model-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.