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

404 papers, page 7 of 9

301. ICLR 2025SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation
302. ICLR 2025Scaling Diffusion Language Models via Adaptation from Autoregressive Models
303. ICLR 2025Scaling Wearable Foundation Models
304. ICLR 2025SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning
305. ICLR 2025SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection
306. ICLR 2025Second-Order Fine-Tuning without Pain for LLMs: A Hessian Informed Zeroth-Order Optimizer
307. ICLR 2025Selective Aggregation for Low-Rank Adaptation in Federated Learning
308. ICLR 2025Semantic Loss Guided Data Efficient Supervised Fine Tuning for Safe Responses in LLMs
309. ICLR 2025Semi-Supervised CLIP Adaptation by Enforcing Semantic and Trapezoidal Consistency
310. ICLR 2025Should VLMs be Pre-trained with Image Data?
311. ICLR 2025SMT: Fine-Tuning Large Language Models with Sparse Matrices
312. ICLR 2025Sparse autoencoders reveal selective remapping of visual concepts during adaptation
313. ICLR 2025Specialized Foundation Models Struggle to Beat Supervised Baselines
314. ICLR 2025Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows
315. ICLR 2025SRSA: Skill Retrieval and Adaptation for Robotic Assembly Tasks
316. ICLR 2025STAFF: Speculative Coreset Selection for Task-Specific Fine-tuning
317. ICLR 2025STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
318. ICLR 2025Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling
319. ICLR 2025TC-MoE: Augmenting Mixture of Experts with Ternary Expert Choice
320. ICLR 2025Teaching LLMs How to Learn with Contextual Fine-Tuning
321. ICLR 2025Tell me about yourself: LLMs are aware of their learned behaviors
322. ICLR 2025To Code or Not To Code? Exploring Impact of Code in Pre-training
323. ICLR 2025Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
324. ICLR 2025Towards Foundation Models for Mixed Integer Linear Programming
325. ICLR 2025Training-Free Activation Sparsity in Large Language Models
326. ICLR 2025Transformer-Squared: Self-adaptive LLMs
327. ICLR 2025Unleashing the Power of Task-Specific Directions in Parameter Efficient Fine-tuning
328. ICLR 2025Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning
329. ICLR 2025Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs
330. ICLR 2025VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation
331. ICLR 2025Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors
332. ICLR 2025When Attention Sink Emerges in Language Models: An Empirical View
333. ICLR 2025X-Fi: A Modality-Invariant Foundation Model for Multimodal Human Sensing
334. ICLR 2025Zero-shot Imputation with Foundation Inference Models for Dynamical Systems
335. ICLR 2025Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity
336. ICLR 2025ZooProbe: A Data Engine for Evaluating, Exploring, and Evolving Large-scale Training Data for Multimodal LLMs
337. ICLR 2024A Foundation Model for Error Correction Codes
338. ICLR 2024A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
339. ICLR 2024Adapting to Distribution Shift by Visual Domain Prompt Generation
340. ICLR 2024Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness
341. ICLR 2024An Emulator for Fine-tuning Large Language Models using Small Language Models
342. ICLR 2024AutoLoRa: An Automated Robust Fine-Tuning Framework
343. ICLR 2024AutoVP: An Automated Visual Prompting Framework and Benchmark
344. ICLR 2024Batched Low-Rank Adaptation of Foundation Models
345. ICLR 2024Bayesian Low-rank Adaptation for Large Language Models
346. ICLR 2024BrainLM: A foundation model for brain activity recordings
347. ICLR 2024C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion
348. ICLR 2024CLAP: Collaborative Adaptation for Patchwork Learning
349. ICLR 2024Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
350. ICLR 2024DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning

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.