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 5 of 30

201. ICLR 2026Modality-free Graph In-context Alignment
202. ICLR 2026MoRA: Missing Modality Low-Rank Adaptation for Visual Recognition
203. ICLR 2026MoSA: Mosaic Shared Adaptation of Large Language Models
204. ICLR 2026Multi-Domain Transferable Graph Gluing for Building Graph Foundation Models
205. ICLR 2026NEO — No-Optimization Test-Time Adaptation through Latent Re-Centering
206. ICLR 2026New Hybrid Fine-Tuning Paradigm for LLMs: Algorithm Design and Convergence Analysis Framework
207. ICLR 2026NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching
208. ICLR 2026No outlier channels but with outlier blocks
209. ICLR 2026Noise Stability of Transformer Models
210. ICLR 2026Not All Documents Are What You Need for Extracting Instruction Tuning Data
211. ICLR 2026Not All Models Suit Expert Offloading: On Local Routing Consistency of Mixture-of-Expert Models
212. ICLR 2026On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
213. ICLR 2026On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs
214. ICLR 2026Optimistic Task Inference for Behavior Foundation Models
215. ICLR 2026PARD: Accelerating LLM Inference with Low‑Cost PARallel Draft Model Adaptation
216. ICLR 2026PAS: Estimating the target accuracy before domain adaptation
217. ICLR 2026pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models
218. ICLR 2026Pi-CCA: Prompt-Invariant CCA Certificates for Replay-Free Continual Multimodal Learning
219. ICLR 2026PiCa: Parameter-Efficient Fine-Tuning with Column Space Projection
220. ICLR 2026Plug-and-Play Compositionality for Boosting Continual Learning with Foundation Models
221. ICLR 2026Pre-training LLM without Learning Rate Decay Enhances Supervised Fine-Tuning
222. ICLR 2026PrefixMemory-Tuning: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention
223. ICLR 2026Preserve and Sculpt: Manifold-Aligned Fine-tuning of Vision-Language Models for Few-Shot Learning
224. ICLR 2026Prompt-MII: Meta-Learning Instruction Induction for LLMs
225. ICLR 2026Proximal Supervised Fine-Tuning
226. ICLR 2026Pusa V1.0: Unlocking Temporal Control in Pretrained Video Diffusion Models via Vectorized Timestep Adaptation
227. ICLR 2026Q-Learning with Fine-Grained Gap-Dependent Regret
228. ICLR 2026Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization
229. ICLR 2026QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models
230. ICLR 2026Reconstruct Anything Model a lightweight foundation model for computational imaging
231. ICLR 2026Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
232. ICLR 2026Repurposing Foundation Model for Generalizable Medical Time Series Classification
233. ICLR 2026Revisiting the Past: Data Unlearning with Model State History
234. ICLR 2026Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling
235. ICLR 2026Rote Learning Considered Useful: Generalizing over Memorized Data in LLMs
236. ICLR 2026Safety Subspaces are Not Linearly Distinct: A Fine-Tuning Case Study
237. ICLR 2026SeeDNorm: Self-Rescaled Dynamic Normalization
238. ICLR 2026SELF-HARMONY: LEARNING TO HARMONIZE SELF-SUPERVISION AND SELF-PLAY IN TEST-TIME REINFORCEMENT LEARNING
239. ICLR 2026SFT Doesn’t Always Hurt General Capabilities: Revisiting Domain-Specific Fine-Tuning in LLMs
240. ICLR 2026SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA
241. ICLR 2026Sparse Attention Adaptation for Long Reasoning
242. ICLR 2026Sparsity Forcing: Reinforcing Token Sparsity of MLLMs
243. ICLR 2026Sparsity-promoting Fine-tuning for Equivariant Materials Foundation Model
244. ICLR 2026Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models
245. ICLR 2026Speculative Speculative Decoding
246. ICLR 2026ssToken: Self-modulated and Semantic-aware Token Selection for LLM Fine-tuning
247. ICLR 2026Stable-LoRA: Stabilizing Feature Learning of Low-Rank Adaption
248. ICLR 2026StochasTok: Improving Fine-Grained Subword Understanding in LLMs
249. ICLR 2026Study of Training Dynamics for Memory-Constrained Fine-Tuning
250. ICLR 2026SumRA: Parameter Efficient Fine-tuning with Singular Value Decomposition and Summed Orthogonal Basis

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.