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 4 of 9

151. ICLR 2026Sparsity Forcing: Reinforcing Token Sparsity of MLLMs
152. ICLR 2026Sparsity-promoting Fine-tuning for Equivariant Materials Foundation Model
153. ICLR 2026Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models
154. ICLR 2026Speculative Speculative Decoding
155. ICLR 2026ssToken: Self-modulated and Semantic-aware Token Selection for LLM Fine-tuning
156. ICLR 2026Stable-LoRA: Stabilizing Feature Learning of Low-Rank Adaption
157. ICLR 2026StochasTok: Improving Fine-Grained Subword Understanding in LLMs
158. ICLR 2026Study of Training Dynamics for Memory-Constrained Fine-Tuning
159. ICLR 2026SumRA: Parameter Efficient Fine-tuning with Singular Value Decomposition and Summed Orthogonal Basis
160. ICLR 2026Sysformer: Safeguarding Frozen Large Language Models with Adaptive System Prompts
161. ICLR 2026Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models
162. ICLR 2026Task-Aware Data Selection via Proxy-Label Enhanced Distribution Matching for LLM Finetuning
163. ICLR 2026TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation
164. ICLR 2026Test-Time Matching: Unlocking Compositional Reasoning in Multimodal Models
165. ICLR 2026The Forecast After the Forecast: A Post-Processing Shift in Time Series
166. ICLR 2026The Softmax Bottleneck Does Not Limit the Probabilities of the Most Likely Tokens
167. ICLR 2026Thompson Sampling via Fine-Tuning of LLMs
168. ICLR 2026Three Forward, One Backward: Memory-Efficient Full-Rank Fine-Tuning of Large Models via Extra Forward Passes
169. ICLR 2026Token-level Data Selection for Safe LLM Fine-tuning
170. ICLR 2026TokenSeek: Memory Efficient Fine Tuning via Instance-Aware Token Ditching
171. ICLR 2026TokMem: Tokenized Procedural Memory for Large Language Models
172. ICLR 2026Towards a Foundation Model for Crowdsourced Label Aggregation
173. ICLR 2026TRAC: Tensor-Train based Across-layer Compression for Parameter-Efficient Fine-Tuning
174. ICLR 2026Train-before-Test Harmonizes Language Model Rankings
175. ICLR 2026Training Large Language Models To Reason In Parallel With Global Forking Tokens
176. ICLR 2026Training-Free Loosely Speculative Decoding: Accepting Semantically Correct Drafts Beyond Exact Match
177. ICLR 2026Transducing Language Models
178. ICLR 2026TS$^2$: Training with Sparsemax+, Testing with Softmax for Accurate and Diverse LLM Fine-Tuning
179. ICLR 2026TSPulse: Tiny Pre-Trained Models with Disentangled Representations for Rapid Time-Series Analysis
180. ICLR 2026Type-Compliant Adaptation Cascades
181. ICLR 2026Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning
182. ICLR 2026UniCA: Unified Covariate Adaptation for Time Series Foundation Model
183. ICLR 2026Universal Properties of Activation Sparsity in Modern Large Language Models
184. ICLR 2026VER: Vision Expert Transformer for Robot Learning via Foundation Distillation and Dynamic Routing
185. ICLR 2026Why Attention Patterns Exist: A Unifying Temporal Perspective Analysis
186. ICLR 2026Why Reinforcement Fine-Tuning Enables MLLMs Preserve Prior Knowledge Better: A Data Perspective
187. ICLR 2026Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics
188. ICLR 2025A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning
189. ICLR 2025AdaRankGrad: Adaptive Gradient Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning
190. ICLR 2025Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models
191. ICLR 2025ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
192. ICLR 2025Agent Skill Acquisition for Large Language Models via CycleQD
193. ICLR 2025Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs
194. ICLR 2025An Auditing Test to Detect Behavioral Shift in Language Models
195. ICLR 2025Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models
196. ICLR 2025As Simple as Fine-tuning: LLM Alignment via Bidirectional Negative Feedback Loss
197. ICLR 2025Better autoregressive regression with LLMs via regression-aware fine-tuning
198. ICLR 2025Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation
199. ICLR 2025CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
200. ICLR 2025ComLoRA: A Competitive Learning Approach for Enhancing LoRA

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.