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

531 papers, page 9 of 11

401. NeurIPS 2024Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
402. NeurIPS 2024LoFiT: Localized Fine-tuning on LLM Representations
403. NeurIPS 2024Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation
404. NeurIPS 2024LoQT: Low-Rank Adapters for Quantized Pretraining
405. NeurIPS 2024LoRA-GA: Low-Rank Adaptation with Gradient Approximation
406. NeurIPS 2024MeshXL: Neural Coordinate Field for Generative 3D Foundation Models
407. NeurIPS 2024MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map
408. NeurIPS 2024Model Fusion through Bayesian Optimization in Language Model Fine-Tuning
409. NeurIPS 2024Multi-modal Transfer Learning between Biological Foundation Models
410. NeurIPS 2024Order-Independence Without Fine Tuning
411. NeurIPS 2024PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
412. NeurIPS 2024Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
413. NeurIPS 2024PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
414. NeurIPS 2024Poseidon: Efficient Foundation Models for PDEs
415. NeurIPS 2024Pre-training Differentially Private Models with Limited Public Data
416. NeurIPS 2024Predicting the Performance of Foundation Models via Agreement-on-the-Line
417. NeurIPS 2024PrivAuditor: Benchmarking Data Protection Vulnerabilities in LLM Adaptation Techniques
418. NeurIPS 2024Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
419. NeurIPS 2024Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
420. NeurIPS 2024QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
421. NeurIPS 2024RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations for Universal Robustness
422. NeurIPS 2024Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models
423. NeurIPS 2024Retrieval & Fine-Tuning for In-Context Tabular Models
424. NeurIPS 2024Risk-Averse Fine-tuning of Large Language Models
425. NeurIPS 2024S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
426. NeurIPS 2024Segment Any Change
427. NeurIPS 2024Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models
428. NeurIPS 2024Shaping the distribution of neural responses with interneurons in a recurrent circuit model
429. NeurIPS 2024SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
430. NeurIPS 2024SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models
431. NeurIPS 2024Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases
432. NeurIPS 2024Spectral Adapter: Fine-Tuning in Spectral Space
433. NeurIPS 2024Stepping Forward on the Last Mile
434. NeurIPS 2024Stress-Testing Capability Elicitation With Password-Locked Models
435. NeurIPS 2024Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation
436. NeurIPS 2024SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
437. NeurIPS 2024Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models
438. NeurIPS 2024Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
439. NeurIPS 2024The Impact of Initialization on LoRA Finetuning Dynamics
440. NeurIPS 2024The Representation Landscape of Few-Shot Learning and Fine-Tuning in Large Language Models
441. NeurIPS 2024Towards Calibrated Robust Fine-Tuning of Vision-Language Models
442. NeurIPS 2024Uncertainty-aware Fine-tuning of Segmentation Foundation Models
443. NeurIPS 2024Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective
444. NeurIPS 2024UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via Visual Foundation Models Prior
445. NeurIPS 2024Unveiling LoRA Intrinsic Ranks via Salience Analysis
446. NeurIPS 2024Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
447. NeurIPS 2024VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks
448. NeurIPS 2024Vision Foundation Model Enables Generalizable Object Pose Estimation
449. NeurIPS 2024What does guidance do? A fine-grained analysis in a simple setting
450. 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.