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

142 papers, page 3 of 3

101. ICML 2026Rethinking the Flow-based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspective
102. ICML 2026SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference
103. ICML 2026Saving Foundation Flow-Matching Priors for Inverse Problems
104. ICML 2026Scaling Behavior in Model Fine-tuning for Audio DeepFake Detection
105. ICML 2026ScaLoRA: Optimally Scaled Low-Rank Adaptation for Efficient High-Rank Fine-Tuning
106. ICML 2026scDEBART: Predicting in silico Single-Cell Perturbation Responses via Large-Scale Differential Expression Learning
107. ICML 2026See More, Forecast Better and Faster: Enhancing Time Series Foundation Models via Inference-Time Plug-and-Play Downsampling
108. ICML 2026SHERPA: Fine-tuning Segment Anything Models with Task-relevant Guidance
109. ICML 2026SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass
110. ICML 2026SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures
111. ICML 2026SliceFine: The Universal Winning-Slice Hypothesis for Pretrained Networks
112. ICML 2026SpanNorm: Reconciling Training Stability and Performance in Deep Transformers
113. ICML 2026SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance–Diversity Data Selection
114. ICML 2026Spectral Imbalance Causes Forgetting in Low-Rank Continual Adaptation
115. ICML 2026SPR: A Structured Prompt Refinement Network for Modality Missing
116. ICML 2026StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars
117. ICML 2026Subgroup Discovery with the Cox Model
118. ICML 2026SURGE: Surrogate Gradient Adaptation in Binary Neural Networks
119. ICML 2026Surgery: Mitigating Harmful Fine-Tuning for Large Language Models via Attention Sink
120. ICML 2026TabICLv2: A Better, Faster, Scalable, and Open Tabular Foundation Model
121. ICML 2026TabMGP: Martingale Posterior with TabPFN
122. ICML 2026TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention
123. ICML 2026TarGATE: Target-Aware Data Selection via Token-Attenuation Gates
124. ICML 2026The Geometry of Narrow Fine-Tuning Degradation: Trajectory Lock-in and Spectral Bifurcation
125. ICML 2026The Geometry of Sequential Learning: Lie-Bracket Prediction of Transfer Order
126. ICML 2026Time-PEFT: Temporal and Multichannel Complexity-Based Fine-Tuning for Time-Series Foundation Models
127. ICML 2026Towards Fine-Grained Robustness: Attention-Guided Test-Time Prompt Tuning for Vision-Language Models
128. ICML 2026Towards Understanding the Dynamics of Low-Rank Adaptation
129. ICML 2026Trust It or Not: Evidential Uncertainty for Feed-Forward 3D Reconstruction with Trust3R
130. ICML 2026TuneAhead: Predicting Fine-tuning Performance Before Training Begins
131. ICML 2026UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models
132. ICML 2026Uncovering the Latent Potential of Deep Intermediate Representations
133. ICML 2026Universal Redundancies in Time Series Foundation Models
134. ICML 2026Unveiling Prior-Data Fitted Networks on Causal Effect Estimation: Pre-Training or Fine-Tuning?
135. ICML 2026Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
136. ICML 2026Walrus: A Cross-domain Foundation Model for Continuum Dynamics
137. ICML 2026Weight Updates as Activation Shifts: A Principled Framework for Steering
138. ICML 2026When Do Graph Foundation Models Transfer? A Data-Centric Theory
139. ICML 2026When More Data Doesn't Help: Limits of Adaptation in Multitask Learning
140. ICML 2026When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
141. ICML 2026WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting
142. ICML 2026Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis

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.