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

182 papers, page 2 of 4

51. NeurIPS 2026EEG-X: Toward Device-Agnostic and Noise-Robust Foundation Models for EEG
52. NeurIPS 2026Efficient Collaborative LLM Fine-Tuning over Heterogeneous Mobile Devices via Many Backbones to One Side-Network Tuning
53. NeurIPS 2026Efficient LLM Adaptation with Forward-Only Passes
54. NeurIPS 2026Efficiently Aligning Draft Models via Parameter- and Data-Efficient Adaptation
55. NeurIPS 2026Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees
56. NeurIPS 2026Emergent Biological Capabilities in a Foundation Model for Molecular Interactions
57. NeurIPS 2026Enhancing Gaze Reasoning in Vision Foundation Models for Gaze Following
58. NeurIPS 2026ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
59. NeurIPS 2026Estimating and Orthogonalizing Unknown Pre-training Gradients for Continual Fine-tuning of Large Language Models
60. NeurIPS 2026Evaluating Uncertainty Calibration in Probabilistic Time Series Foundation Models
61. NeurIPS 2026Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks
62. NeurIPS 2026Exploiting Fine-Tuning Structures to Improve Adversarial Transferability on Downstream SAM
63. NeurIPS 2026Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons
64. NeurIPS 2026FedLoVA: Value-Only Aggregation for Federated LoRA Fine-Tuning of Large Language Models
65. NeurIPS 2026FedReCall: Recalling Client-Specific Directions in Federated LoRA Fine-tuning
66. NeurIPS 2026Fine-Grained Benchmark Generation for Comprehensive Evaluation of Foundation Models
67. NeurIPS 2026Fine-Tuning Improves Information Conveyance in Language Models
68. NeurIPS 2026Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG
69. NeurIPS 2026Fine-Tuning Language Models to Know What They Know
70. NeurIPS 2026FlatClip: Reusing Image Foundation Models for fMRI Representation Learning via Cortical Flatmaps
71. NeurIPS 2026FOCUS: Benchmarking Retinal Model Generalization from Foundation Vision Encoders to Multimodal LLMs
72. NeurIPS 2026FoldAbS: Repurposing the Protein Folding Model as a Foundation Encoder for Antibody Screening
73. NeurIPS 2026Follow the Mean: Reference-Guided Flow Matching
74. NeurIPS 2026Frank-LoRA: Federated Rank-Aware LoRA for Fine-Tuning Large Models
75. NeurIPS 2026From Next-Token to Next-Block: A Principled Adaptation Path for Diffusion LLMs
76. NeurIPS 2026Full Fine-Tuning Is Not the Problem: Why Adam Fails and SGD Succeeds in Few-Shot CLIP Adaptation
77. NeurIPS 2026GAPS: Gradient-Aware Adaptation-Gap Scoring for Time-Series Anomaly Detection with Foundation Models
78. NeurIPS 2026Glob3R: Global Structure-from-Motion with 3D Foundation Models
79. NeurIPS 2026GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring
80. NeurIPS 2026Guarding the Life Code: Preserving Membership Privacy in Genomic Foundation Models
81. NeurIPS 2026HandXFM: Semantic-Structural Distillation for Hand Radiograph Foundation Models
82. NeurIPS 2026High Performance Differentially Private Fine-Tuning using Dataset Distillation
83. NeurIPS 2026How Finite-Rank Bottleneck Shape the Low-Rank Adaptation Landscape
84. NeurIPS 2026How Much of a Model Do We Need? Redundancy and Slimmability in Remote Sensing Foundation Models
85. NeurIPS 2026Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training
86. NeurIPS 2026Hyperbolic Displacement-Constrained Adaptation for CLIP-Based Class-Incremental Learning
87. NeurIPS 2026ImmuVis: Hyperconvolutional Foundation Models for Imaging Mass Cytometry
88. NeurIPS 2026Inertia-1: An Open Exploration of Wearable Motion Foundation Models
89. NeurIPS 2026IRIS: Interpolative Rényi Iterative Self-play for Large Language Model Fine-Tuning
90. NeurIPS 2026Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models
91. NeurIPS 2026Language-Induced Priors for Domain Adaptation
92. NeurIPS 2026Latent Riemannian Flow Matching for Geometry-Grounded 3D Foundation Models
93. NeurIPS 2026LEAN: Library-Based Adaptation for Asynchronous, Federated Fine-Tuning
94. NeurIPS 2026Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning
95. NeurIPS 2026Lift, See, Act: Hierarchical Robot Policy Pretraining with 3D Foundation Models
96. NeurIPS 2026LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection
97. NeurIPS 2026Mask-Conditioned Gradient Masking for Fine-Tuning Mixture-of-Experts Diffusion Language Models
98. NeurIPS 2026MICE: Multi-animal Interaction Context Encoder — A Hierarchical Foundation Model for Mouse Behavior
99. NeurIPS 2026Monroe: A Molecular Foundation Model for In-context Probabilistic Inference
100. NeurIPS 2026Multimodal Foundation Agents Should Use Brain Data as Privileged Supervision

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.