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

404 papers, page 3 of 9

101. ICLR 2026Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?
102. ICLR 2026Low rank adaptation of chemical foundation models generate effective odorant representations
103. ICLR 2026Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization
104. ICLR 2026Memory-Free Continual Learning with Null Space Adaptation for Zero-Shot Vision-Language Models
105. ICLR 2026MergeTune: Continued Fine-Tuning of Vision-Language Models
106. ICLR 2026Meta-UCF: Unified Task-Conditioned LoRA Generation for Continual Learning in Large Language Models
107. ICLR 2026MetaVLA: Unified Meta Co-Training for Efficient Embodied Adaptation
108. ICLR 2026MiSS: Revisiting the Trade-off in LoRA with an Efficient Shard-Sharing Structure
109. ICLR 2026Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity
110. ICLR 2026Modality-free Graph In-context Alignment
111. ICLR 2026MoRA: Missing Modality Low-Rank Adaptation for Visual Recognition
112. ICLR 2026MoSA: Mosaic Shared Adaptation of Large Language Models
113. ICLR 2026Multi-Domain Transferable Graph Gluing for Building Graph Foundation Models
114. ICLR 2026NEO — No-Optimization Test-Time Adaptation through Latent Re-Centering
115. ICLR 2026New Hybrid Fine-Tuning Paradigm for LLMs: Algorithm Design and Convergence Analysis Framework
116. ICLR 2026NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching
117. ICLR 2026No outlier channels but with outlier blocks
118. ICLR 2026Noise Stability of Transformer Models
119. ICLR 2026Not All Documents Are What You Need for Extracting Instruction Tuning Data
120. ICLR 2026Not All Models Suit Expert Offloading: On Local Routing Consistency of Mixture-of-Expert Models
121. ICLR 2026On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
122. ICLR 2026On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs
123. ICLR 2026Optimistic Task Inference for Behavior Foundation Models
124. ICLR 2026PARD: Accelerating LLM Inference with Low‑Cost PARallel Draft Model Adaptation
125. ICLR 2026PAS: Estimating the target accuracy before domain adaptation
126. ICLR 2026pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models
127. ICLR 2026Pi-CCA: Prompt-Invariant CCA Certificates for Replay-Free Continual Multimodal Learning
128. ICLR 2026PiCa: Parameter-Efficient Fine-Tuning with Column Space Projection
129. ICLR 2026Plug-and-Play Compositionality for Boosting Continual Learning with Foundation Models
130. ICLR 2026Pre-training LLM without Learning Rate Decay Enhances Supervised Fine-Tuning
131. ICLR 2026PrefixMemory-Tuning: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention
132. ICLR 2026Preserve and Sculpt: Manifold-Aligned Fine-tuning of Vision-Language Models for Few-Shot Learning
133. ICLR 2026Prompt-MII: Meta-Learning Instruction Induction for LLMs
134. ICLR 2026Proximal Supervised Fine-Tuning
135. ICLR 2026Pusa V1.0: Unlocking Temporal Control in Pretrained Video Diffusion Models via Vectorized Timestep Adaptation
136. ICLR 2026Q-Learning with Fine-Grained Gap-Dependent Regret
137. ICLR 2026Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization
138. ICLR 2026QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models
139. ICLR 2026Reconstruct Anything Model a lightweight foundation model for computational imaging
140. ICLR 2026Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
141. ICLR 2026Repurposing Foundation Model for Generalizable Medical Time Series Classification
142. ICLR 2026Revisiting the Past: Data Unlearning with Model State History
143. ICLR 2026Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling
144. ICLR 2026Rote Learning Considered Useful: Generalizing over Memorized Data in LLMs
145. ICLR 2026Safety Subspaces are Not Linearly Distinct: A Fine-Tuning Case Study
146. ICLR 2026SeeDNorm: Self-Rescaled Dynamic Normalization
147. ICLR 2026SELF-HARMONY: LEARNING TO HARMONIZE SELF-SUPERVISION AND SELF-PLAY IN TEST-TIME REINFORCEMENT LEARNING
148. ICLR 2026SFT Doesn’t Always Hurt General Capabilities: Revisiting Domain-Specific Fine-Tuning in LLMs
149. ICLR 2026SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA
150. ICLR 2026Sparse Attention Adaptation for Long Reasoning

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.