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

70 papers, page 1 of 2

1. ICML 2024A Closer Look at the Limitations of Instruction Tuning
2. ICML 2024A decoder-only foundation model for time-series forecasting
3. ICML 2024A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
4. ICML 2024Accelerating Transformer Pre-training with 2:4 Sparsity
5. ICML 2024Active Label Correction for Semantic Segmentation with Foundation Models
6. ICML 2024Asymmetry in Low-Rank Adapters of Foundation Models
7. ICML 2024Batch Singular Value Polarization and Weighted Semantic Augmentation for Universal Domain Adaptation
8. ICML 2024Bayesian Adaptation of Network Depth and Width for Continual Learning
9. ICML 2024Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models
10. ICML 2024By Tying Embeddings You Are Assuming the Distributional Hypothesis
11. ICML 2024Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
12. ICML 2024Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations
13. ICML 2024Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language Models
14. ICML 2024Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
15. ICML 2024Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
16. ICML 2024Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
17. ICML 2024DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
18. ICML 2024Differentially Private Bias-Term Fine-tuning of Foundation Models
19. ICML 2024Differentially Private Domain Adaptation with Theoretical Guarantees
20. ICML 2024DoRA: Weight-Decomposed Low-Rank Adaptation
21. ICML 2024DPZero: Private Fine-Tuning of Language Models without Backpropagation
22. ICML 2024Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning
23. ICML 2024ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections
24. ICML 2024Fine-grained Local Sensitivity Analysis of Standard Dot-Product Self-Attention
25. ICML 2024Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
26. ICML 2024FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
27. ICML 2024Foundation Policies with Hilbert Representations
28. ICML 2024From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning
29. ICML 2024Getting the most out of your tokenizer for pre-training and domain adaptation
30. ICML 2024Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method
31. ICML 2024Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions
32. ICML 2024LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
33. ICML 2024Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning
34. ICML 2024Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
35. ICML 2024LoRA Training in the NTK Regime has No Spurious Local Minima
36. ICML 2024LoRA+: Efficient Low Rank Adaptation of Large Models
37. ICML 2024MOMENT: A Family of Open Time-series Foundation Models
38. ICML 2024Not all distributional shifts are equal: Fine-grained robust conformal inference
39. ICML 2024On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis
40. ICML 2024Open-Vocabulary Calibration for Fine-tuned CLIP
41. ICML 2024Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation
42. ICML 2024Parameter-Efficient Fine-Tuning with Controls
43. ICML 2024Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
44. ICML 2024Position: Foundation Agents as the Paradigm Shift for Decision Making
45. ICML 2024Position: Graph Foundation Models Are Already Here
46. ICML 2024Position: On the Societal Impact of Open Foundation Models
47. ICML 2024Position: Why Tabular Foundation Models Should Be a Research Priority
48. ICML 2024Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
49. ICML 2024Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis
50. ICML 2024Prompting a Pretrained Transformer Can Be a Universal Approximator

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.