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

149 papers, page 2 of 3

51. ICLR 2025Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models
52. ICLR 2025Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning
53. ICLR 2025Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models
54. ICLR 2025Intervening Anchor Token: Decoding Strategy in Alleviating Hallucinations for MLLMs
55. ICLR 2025Jamba: Hybrid Transformer-Mamba Language Models
56. ICLR 2025KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models
57. ICLR 2025Knowledge Graph Finetuning Enhances Knowledge Manipulation in Large Language Models
58. ICLR 2025Kolmogorov-Arnold Transformer
59. ICLR 2025Large Convolutional Model Tuning via Filter Subspace
60. ICLR 2025Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning
61. ICLR 2025Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation
62. ICLR 2025LiFT: Learning to Fine-Tune via Bayesian Parameter Efficient Meta Fine-Tuning
63. ICLR 2025LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning
64. ICLR 2025LoLCATs: On Low-Rank Linearizing of Large Language Models
65. ICLR 2025LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation
66. ICLR 2025LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
67. ICLR 2025LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation
68. ICLR 2025LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation models
69. ICLR 2025Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
70. ICLR 2025Maintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning
71. ICLR 2025MallowsPO: Fine-Tune Your LLM with Preference Dispersions
72. ICLR 2025MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba
73. ICLR 2025MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods
74. ICLR 2025Matryoshka Multimodal Models
75. ICLR 2025Merging LoRAs like Playing LEGO: Pushing the Modularity of LoRA to Extremes Through Rank-Wise Clustering
76. ICLR 2025MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models
77. ICLR 2025Mitigating Memorization in Language Models
78. ICLR 2025Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment
79. ICLR 2025Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN
80. ICLR 2025MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
81. ICLR 2025Model Equality Testing: Which Model is this API Serving?
82. ICLR 2025MoLEx: Mixture of Layer Experts for Fine-tuning with Sparse Upcycling
83. ICLR 2025Montessori-Instruct: Generate Influential Training Data Tailored for Student Learning
84. ICLR 2025MoS: Unleashing Parameter Efficiency of Low-Rank Adaptation with Mixture of Shards
85. ICLR 2025MTSAM: Multi-Task Fine-Tuning for Segment Anything Model
86. ICLR 2025Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning
87. ICLR 2025Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning
88. ICLR 2025Neural Phylogeny: Fine-Tuning Relationship Detection among Neural Networks
89. ICLR 2025nGPT: Normalized Transformer with Representation Learning on the Hypersphere
90. ICLR 2025NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals
91. ICLR 2025NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval
92. ICLR 2025On the Optimization Landscape of Low Rank Adaptation Methods for Large Language Models
93. ICLR 2025PaCA: Partial Connection Adaptation for Efficient Fine-Tuning
94. ICLR 2025PEARL: Towards Permutation-Resilient LLMs
95. ICLR 2025PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches
96. ICLR 2025Preserving Diversity in Supervised Fine-Tuning of Large Language Models
97. ICLR 2025ProteinBench: A Holistic Evaluation of Protein Foundation Models
98. ICLR 2025Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning
99. ICLR 2025QERA: an Analytical Framework for Quantization Error Reconstruction
100. ICLR 2025Quantized Spike-driven Transformer

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.