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

94 papers, page 2 of 2

51. NeurIPS 2024MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map
52. NeurIPS 2024Model Fusion through Bayesian Optimization in Language Model Fine-Tuning
53. NeurIPS 2024Multi-modal Transfer Learning between Biological Foundation Models
54. NeurIPS 2024Order-Independence Without Fine Tuning
55. NeurIPS 2024PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
56. NeurIPS 2024Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
57. NeurIPS 2024PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
58. NeurIPS 2024Poseidon: Efficient Foundation Models for PDEs
59. NeurIPS 2024Pre-training Differentially Private Models with Limited Public Data
60. NeurIPS 2024Predicting the Performance of Foundation Models via Agreement-on-the-Line
61. NeurIPS 2024PrivAuditor: Benchmarking Data Protection Vulnerabilities in LLM Adaptation Techniques
62. NeurIPS 2024Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
63. NeurIPS 2024Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
64. NeurIPS 2024QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
65. NeurIPS 2024RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations for Universal Robustness
66. NeurIPS 2024Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models
67. NeurIPS 2024Retrieval & Fine-Tuning for In-Context Tabular Models
68. NeurIPS 2024Risk-Averse Fine-tuning of Large Language Models
69. NeurIPS 2024S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
70. NeurIPS 2024Segment Any Change
71. NeurIPS 2024Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models
72. NeurIPS 2024Shaping the distribution of neural responses with interneurons in a recurrent circuit model
73. NeurIPS 2024SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
74. NeurIPS 2024SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models
75. NeurIPS 2024Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases
76. NeurIPS 2024Spectral Adapter: Fine-Tuning in Spectral Space
77. NeurIPS 2024Stepping Forward on the Last Mile
78. NeurIPS 2024Stress-Testing Capability Elicitation With Password-Locked Models
79. NeurIPS 2024Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation
80. NeurIPS 2024SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
81. NeurIPS 2024Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models
82. NeurIPS 2024Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
83. NeurIPS 2024The Impact of Initialization on LoRA Finetuning Dynamics
84. NeurIPS 2024The Representation Landscape of Few-Shot Learning and Fine-Tuning in Large Language Models
85. NeurIPS 2024Towards Calibrated Robust Fine-Tuning of Vision-Language Models
86. NeurIPS 2024Uncertainty-aware Fine-tuning of Segmentation Foundation Models
87. NeurIPS 2024Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective
88. NeurIPS 2024UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via Visual Foundation Models Prior
89. NeurIPS 2024Unveiling LoRA Intrinsic Ranks via Salience Analysis
90. NeurIPS 2024Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
91. NeurIPS 2024VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks
92. NeurIPS 2024Vision Foundation Model Enables Generalizable Object Pose Estimation
93. NeurIPS 2024What does guidance do? A fine-grained analysis in a simple setting
94. NeurIPS 2024WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks

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.