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

461 papers, page 6 of 10

251. ICML 2025Peri-LN: Revisiting Normalization Layer in the Transformer Architecture
252. ICML 2025PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction
253. ICML 2025Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models
254. ICML 2025Projection Pursuit Density Ratio Estimation
255. ICML 2025Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models
256. ICML 2025Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent
257. ICML 2025Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting
258. ICML 2025Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models
259. ICML 2025RepLoRA: Reparameterizing Low-rank Adaptation via the Perspective of Mixture of Experts
260. ICML 2025Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach
261. ICML 2025Rethinking the Bias of Foundation Model under Long-tailed Distribution
262. ICML 2025SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation
263. ICML 2025SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning
264. ICML 2025Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection
265. ICML 2025SeedLoRA: A Fusion Approach to Efficient LLM Fine-Tuning
266. ICML 2025Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation
267. ICML 2025SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
268. ICML 2025SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
269. ICML 2025SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics
270. ICML 2025TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
271. ICML 2025TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems
272. ICML 2025Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
273. ICML 2025Task-Agnostic Pre-training and Task-Guided Fine-tuning for Versatile Diffusion Planner
274. ICML 2025Test-time Adaptation on Graphs via Adaptive Subgraph-based Selection and Regularized Prototypes
275. ICML 2025Text-to-LoRA: Instant Transformer Adaption
276. ICML 2025The Double-Ellipsoid Geometry of CLIP
277. ICML 2025Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport
278. ICML 2025Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
279. ICML 2025Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
280. ICML 2025Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis
281. ICML 2025TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
282. ICML 2025Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
283. ICML 2025Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
284. ICML 2025WeGeFT: Weight‑Generative Fine‑Tuning for Multi‑Faceted Efficient Adaptation of Large Models
285. ICML 2025What Has a Foundation Model Found? Inductive Bias Reveals World Models
286. ICML 2025When Can Proxies Improve the Sample Complexity of Preference Learning?
287. ICML 2025Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models
288. NeurIPS 2025A Closer Look to Positive-Unlabeled Learning from Fine-grained Perspectives: An Empirical Study
289. NeurIPS 2025Activated LoRA: Fine-tuned LLMs for Intrinsics
290. NeurIPS 2025AdaLRS: Loss-Guided Adaptive Learning Rate Search for Efficient Foundation Model Pretraining
291. NeurIPS 2025AdaMSS: Adaptive Multi-Subspace Approach for Parameter-Efficient Fine-Tuning
292. NeurIPS 2025Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
293. NeurIPS 2025Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning
294. NeurIPS 2025AI Progress Should Be Measured by Capability-Per-Resource, Not Scale Alone: A Framework for Gradient-Guided Resource Allocation in LLMs
295. NeurIPS 2025AION-1: Omnimodal Foundation Model for Astronomical Sciences
296. NeurIPS 2025All You Need is One: Capsule Prompt Tuning with a Single Vector
297. NeurIPS 2025AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
298. NeurIPS 2025An Investigation of Memorization Risk in Healthcare Foundation Models
299. NeurIPS 2025Attack via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs
300. NeurIPS 2025AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping

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.