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 3 of 30

101. ICLR 2026All-day Multi-scenes Lifelong Vision-and-Language Navigation with Tucker Adaptation
102. ICLR 2026Anchored Supervised Fine-Tuning
103. ICLR 2026Antibody: Strengthening Defense Against Harmful Fine-Tuning for Large Language Models via Attenuating Harmful Gradient Influence
104. ICLR 2026Any-Order Any-Subset AutoRegressive Model
105. ICLR 2026Arbitrary-Order Block SignSGD for Memory-Efficient LLM Fine-Tuning
106. ICLR 2026ARM-FM: Automated Reward Machines via Foundation Models for Compositional Reinforcement Learning
107. ICLR 2026Avey-B
108. ICLR 2026BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models
109. ICLR 2026Bayesian Test-Time Adaptation via Dirichlet feature projection and GMM-Driven Inference for Motor Imagery EEG Decoding
110. ICLR 2026Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data Could Be Secretly Stolen!
111. ICLR 2026Benchmarking ECG Foundational Models: A Reality Check Across Clinical Tasks
112. ICLR 2026Beyond Accuracy: Are Time Series Foundation Models Well-Calibrated?
113. ICLR 2026BFM-Zero: A Promptable Behavioral Foundation Model for Humanoid Control Using Unsupervised Reinforcement Learning
114. ICLR 2026Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning Large-Scale Models
115. ICLR 2026Bilateral Information-aware Test-time Adaptation for Vision-Language Models
116. ICLR 2026BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Models
117. ICLR 2026Boomerang Distillation Enables Zero-Shot Model Size Interpolation
118. ICLR 2026BoRA: Towards More Expressive Low-Rank Adaptation with Block Diversity
119. ICLR 2026Brain-Semantoks: Learning Semantic Tokens of Brain Dynamics with a Self-Distilled Foundation Model
120. ICLR 2026Breaking Barriers: Do Reinforcement Fine-tuning Gains Transfer To Unseen Domains?
121. ICLR 2026Bridging Input Feature Spaces Towards Graph Foundation Models
122. ICLR 2026Bridging Radiology and Pathology Foundation Models via Concept-Based Multimodal Co-Adaptation
123. ICLR 2026CAR-LoRA: Training Compression-Aware and Robust LoRA Adapters for Evolving LLMs
124. ICLR 2026Cartridges: Lightweight and general-purpose long context representations via self-study
125. ICLR 2026Catching the Details: Self-Distilled RoI Predictors for Fine-Grained MLLM Perception
126. ICLR 2026CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
127. ICLR 2026Cautious Optimizers: Improving Training with One Line of Code
128. ICLR 2026CerebraGloss: Instruction-Tuning a Large Vision-Language Model for Fine-Grained Clinical EEG Interpretation
129. ICLR 2026COLD-Steer: Steering Large Language Models via In-Context One-step Learning Dynamics
130. ICLR 2026CoMem: Compositional Concept-Graph Memory for Vision–Language Adaptation
131. ICLR 2026COMPACT: COMPositional Atomic-to-Complex Visual Capability Tuning
132. ICLR 2026Comparing the learning dynamics of in-context learning and fine-tuning in language models
133. ICLR 2026Complexity- and Statistics-Guided Anomaly Detection in Time Series Foundation Models
134. ICLR 2026CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter
135. ICLR 2026CPiRi: Channel Permutation-Invariant Relational Interaction for Multivariate Time Series Forecasting
136. ICLR 2026CPQS-Tuning: A Model Self-Perception-Based Data Filtering Algorithm for Efficient Instruction Fine-Tuning
137. ICLR 2026Deep Hierarchical Learning with Nested Subspace Networks
138. ICLR 2026Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach
139. ICLR 2026Developmental Federated Tuning: A Cognitive-Inspired Paradigm for Efficient LLM Adaptation
140. ICLR 2026DiaBlo: Diagonal Blocks Are Sufficient For Finetuning
141. ICLR 2026Differential Fine-Tuning Large Language Models Towards Better Diverse Reasoning Abilities
142. ICLR 2026Discrete Adjoint Matching
143. ICLR 2026Discrete Latent Features Ablate Adversarial Attack: A Robust Prompt Tuning Framework for VLMs
144. ICLR 2026Distilling to Hybrid Attention Models via KL-Guided Layer Selection
145. ICLR 2026DMAP: A Distribution Map for Text
146. ICLR 2026DND: Boosting Large Language Models with Dynamic Nested Depth
147. ICLR 2026E²LoRA: Efficient and Effective Low-Rank Adaptation with Entropy-Guided Adaptive Sharing
148. ICLR 2026Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation
149. ICLR 2026Embodied Navigation Foundation Model
150. ICLR 2026Enabling Fine-Tuning of Direct Feedback Alignment via Feedback-Weight Matching

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.