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

142 papers, page 1 of 3

1. ICML 2026A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn’t)
2. ICML 2026A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search
3. ICML 2026A Risk Decomposition Framework for Pre-hoc Fine-tuning Prediction
4. ICML 2026A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning
5. ICML 2026ACO-MoE-LoRA: Evolving-while-Training for Adapting Segment Anything Model 2 to Specialized Domains
6. ICML 2026Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
7. ICML 2026AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments
8. ICML 2026Adaptive Utilization of Low-Rank Adaptation via Conditioned Gating
9. ICML 2026AGZO: Activation-Guided Zeroth-Order Optimization for LLM Fine-Tuning
10. ICML 2026Annotations Mitigate Post-Training Mode Collapse
11. ICML 2026Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
12. ICML 2026AutoQRA: Joint Optimization of Mixed-Precision Quantization and Low-rank Adapters for Efficient LLM Fine-Tuning
13. ICML 2026Baguan-TS: dual in-context learning model for time series forecasting with covariates
14. ICML 2026Balanced LoRA: Removing Parameter Invariance to Accelerate Convergence
15. ICML 2026Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models
16. ICML 2026Beyond Log Likelihood: Probability-Based Objectives for Supervised Fine-Tuning across the Model Capability Continuum
17. ICML 2026Beyond Static Allocation: Dynamic Sensitivity-Aware Fine-Tuning for Vision Transformers
18. ICML 2026Beyond Temperature: Hyperfitting as a Late-Stage Geometric Expansion
19. ICML 2026BIOARC: Discovering Optimal Neural Architectures for Biological Foundation Models
20. ICML 2026Black-Box Assisted Regression: Phase Transitions and Minimax Optimality
21. ICML 2026CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data
22. ICML 2026Can Muon Fine-tune Adam-Pretrained Models?
23. ICML 2026Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction
24. ICML 2026Causal Fine-Tuning under Latent Confounded Shift
25. ICML 2026ChaosNexus: A Foundation Model for ODE-based Chaotic System Forecasting with Hierarchical Multi-scale Awareness
26. ICML 2026COBRA: Contribution-Based Bayesian Rank Allocation for Parameter-Efficient Fine-Tuning
27. ICML 2026CoLA: Cross-Modal Low-rank Adaptation for Multimodal Downstream Tasks
28. ICML 2026Collaborative and Efficient Fine-tuning: Leveraging Task Similarity
29. ICML 2026Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models
30. ICML 2026ConFlux: Multivariate Time Series in Flux, One Unified Forecast in Confluence
31. ICML 2026Constrained Flow Optimization via Sequential Fine-Tuning for Molecular Design
32. ICML 2026Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm
33. ICML 2026CSPLoRA: Confidence-Guided Structure Planning for Low-Rank Adaptation
34. ICML 2026Cure-SFT: Diagnostic-Guided Data Curation for Instruction Tuning
35. ICML 2026Data Difficulty and the Generalization–Extrapolation Tradeoff in LLM Fine-Tuning
36. ICML 2026Decoupled Low-Rank Adaptation for Robust Federated Fine-Tuning
37. ICML 2026Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning
38. ICML 2026DiP-G: Discrete Prompting for Graph Neural Networks
39. ICML 2026Distilling Linearized Behavior into Non-linear Fine-Tuning for Effective Task Arithmetic
40. ICML 2026Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation
41. ICML 2026Diversity-aware Weight Perturbation Promotes Robust Adaptation
42. ICML 2026Don't Reinvent the Wheel, Just Realign the Spokes: Resource-Efficient Federated Fine-Tuning via Rank-Wise Expert Assembly
43. ICML 2026DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training
44. ICML 2026EDCO: Dynamic Curriculum Orchestration for Domain-specific Large Language Model Fine-tuning
45. ICML 2026EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models
46. ICML 2026EmBrace: A Collective Knowledge Fusion Framework Toward Unified EEG Foundation Models
47. ICML 2026End-to-End Compression for Tabular Foundation Models
48. ICML 2026Energy-Structured Low-Rank Adaptation for Continual Learning
49. ICML 2026Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
50. ICML 2026FedHera: Towards Drift-Resilient Federated Fine-tuning with Heterogeneous Resources

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.