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

174 papers, page 1 of 4

1. NeurIPS 2025A Closer Look to Positive-Unlabeled Learning from Fine-grained Perspectives: An Empirical Study
2. NeurIPS 2025Activated LoRA: Fine-tuned LLMs for Intrinsics
3. NeurIPS 2025AdaLRS: Loss-Guided Adaptive Learning Rate Search for Efficient Foundation Model Pretraining
4. NeurIPS 2025AdaMSS: Adaptive Multi-Subspace Approach for Parameter-Efficient Fine-Tuning
5. NeurIPS 2025Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
6. NeurIPS 2025Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning
7. NeurIPS 2025AI Progress Should Be Measured by Capability-Per-Resource, Not Scale Alone: A Framework for Gradient-Guided Resource Allocation in LLMs
8. NeurIPS 2025AION-1: Omnimodal Foundation Model for Astronomical Sciences
9. NeurIPS 2025All You Need is One: Capsule Prompt Tuning with a Single Vector
10. NeurIPS 2025AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
11. NeurIPS 2025An Investigation of Memorization Risk in Healthcare Foundation Models
12. NeurIPS 2025Attack via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs
13. NeurIPS 2025AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
14. NeurIPS 2025Axial Neural Networks for Dimension-Free Foundation Models
15. NeurIPS 2025Beyond Higher Rank: Token-wise Input-Output Projections for Efficient Low-Rank Adaptation
16. NeurIPS 2025Bigram Subnetworks: Mapping to Next Tokens in Transformer Language Models
17. NeurIPS 2025BlockScan: Detecting Anomalies in Blockchain Transactions
18. NeurIPS 2025Boosting Skeleton-based Zero-Shot Action Recognition with Training-Free Test-Time Adaptation
19. NeurIPS 2025Brain Harmony: A Multimodal Foundation Model Unifying Morphology and Function into 1D Tokens
20. NeurIPS 2025BrainMoE: Cognition Joint Embedding via Mixture-of-Expert Towards Robust Brain Foundation Model
21. NeurIPS 2025BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals
22. NeurIPS 2025C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
23. NeurIPS 2025Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark
24. NeurIPS 2025Can LLMs Reason Over Non-Text Modalities in a Training-Free Manner? A Case Study with In-Context Representation Learning
25. NeurIPS 2025CF-VLM：CounterFactual Vision-Language Fine-tuning
26. NeurIPS 2025ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibiltiy Data
27. NeurIPS 2025Class-aware Domain Knowledge Fusion and Fission for Continual Test-Time Adaptation
28. NeurIPS 2025Compress to Impress: Efficient LLM Adaptation Using a Single Gradient Step on 100 Samples
29. NeurIPS 2025Correlated Low-Rank Adaptation for ConvNets
30. NeurIPS 2025CrossSpectra: Exploiting Cross-Layer Smoothness for Parameter-Efficient Fine-Tuning
31. NeurIPS 2025CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding
32. NeurIPS 2025Curvature Tuning: Provable Training-free Model Steering From a Single Parameter
33. NeurIPS 2025Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
34. NeurIPS 2025Deep Gaussian from Motion: Exploring 3D Geometric Foundation Models for Gaussian Splatting
35. NeurIPS 2025Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
36. NeurIPS 2025DEFT: Decompositional Efficient Fine-Tuning for Text-to-Image Models
37. NeurIPS 2025Demystifying Network Foundation Models
38. NeurIPS 2025Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix
39. NeurIPS 2025Distribution-Aligned Decoding for Efficient LLM Task Adaptation
40. NeurIPS 2025Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
41. NeurIPS 2025DLoFT: Gradient-Decoupled Fine-Tuning for Generalizable Long Chain-of-Thought Reasoning
42. NeurIPS 2025DOTA: Distributional Test-time Adaptation of Vision-Language Models
43. NeurIPS 2025DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment
44. NeurIPS 2025Dual Prototype-Enhanced Contrastive Framework for Class-Imbalanced Graph Domain Adaptation
45. NeurIPS 2025E-BATS: Efficient Backpropagation-Free Test-Time Adaptation for Speech Foundation Models
46. NeurIPS 2025Efficient Data Selection at Scale via Influence Distillation
47. NeurIPS 2025Efficient Randomized Experiments Using Foundation Models
48. NeurIPS 2025Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
49. NeurIPS 2025EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction
50. NeurIPS 2025Enhanced Expert Merging for Mixture-of-Experts in Graph Foundation Models

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.