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

1,492 papers, page 27 of 30

1301. NeurIPS 2024Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
1302. NeurIPS 2024Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
1303. NeurIPS 2024QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
1304. NeurIPS 2024RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations for Universal Robustness
1305. NeurIPS 2024Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models
1306. NeurIPS 2024Retrieval & Fine-Tuning for In-Context Tabular Models
1307. NeurIPS 2024Risk-Averse Fine-tuning of Large Language Models
1308. NeurIPS 2024S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
1309. NeurIPS 2024Segment Any Change
1310. NeurIPS 2024Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models
1311. NeurIPS 2024Shaping the distribution of neural responses with interneurons in a recurrent circuit model
1312. NeurIPS 2024SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
1313. NeurIPS 2024SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models
1314. NeurIPS 2024Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases
1315. NeurIPS 2024Spectral Adapter: Fine-Tuning in Spectral Space
1316. NeurIPS 2024Stepping Forward on the Last Mile
1317. NeurIPS 2024Stress-Testing Capability Elicitation With Password-Locked Models
1318. NeurIPS 2024Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation
1319. NeurIPS 2024SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
1320. NeurIPS 2024Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models
1321. NeurIPS 2024Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
1322. NeurIPS 2024The Impact of Initialization on LoRA Finetuning Dynamics
1323. NeurIPS 2024The Representation Landscape of Few-Shot Learning and Fine-Tuning in Large Language Models
1324. NeurIPS 2024Towards Calibrated Robust Fine-Tuning of Vision-Language Models
1325. NeurIPS 2024Uncertainty-aware Fine-tuning of Segmentation Foundation Models
1326. NeurIPS 2024Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective
1327. NeurIPS 2024UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via Visual Foundation Models Prior
1328. NeurIPS 2024Unveiling LoRA Intrinsic Ranks via Salience Analysis
1329. NeurIPS 2024Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
1330. NeurIPS 2024VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks
1331. NeurIPS 2024Vision Foundation Model Enables Generalizable Object Pose Estimation
1332. NeurIPS 2024What does guidance do? A fine-grained analysis in a simple setting
1333. NeurIPS 2024WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks
1334. CVPR 2023A-La-Carte Prompt Tuning (APT): Combining Distinct Data via Composable Prompting
1335. CVPR 2023Boosting Transductive Few-Shot Fine-Tuning With Margin-Based Uncertainty Weighting and Probability Regularization
1336. CVPR 2023Divide and Adapt: Active Domain Adaptation via Customized Learning
1337. CVPR 2023Fine-Grained Face Swapping via Regional GAN Inversion
1338. CVPR 2023Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation
1339. CVPR 2023Guided Recommendation for Model Fine-Tuning
1340. CVPR 2023Hint-Aug: Drawing Hints From Foundation Vision Transformers Towards Boosted Few-Shot Parameter-Efficient Tuning
1341. CVPR 2023Integral Neural Networks
1342. CVPR 2023Integrally Pre-Trained Transformer Pyramid Networks
1343. CVPR 2023Masked Images Are Counterfactual Samples for Robust Fine-Tuning
1344. CVPR 2023MELTR: Meta Loss Transformer for Learning To Fine-Tune Video Foundation Models
1345. CVPR 2023MetaPortrait: Identity-Preserving Talking Head Generation With Fast Personalized Adaptation
1346. CVPR 2023MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation
1347. CVPR 2023MOT: Masked Optimal Transport for Partial Domain Adaptation
1348. CVPR 2023Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
1349. CVPR 2023Policy Adaptation From Foundation Model Feedback
1350. CVPR 2023Prompt, Generate, Then Cache: Cascade of Foundation Models Makes Strong Few-Shot Learners

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.