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

461 papers, page 9 of 10

401. NeurIPS 2025Optimization Inspired Few-Shot Adaptation for Large Language Models
402. NeurIPS 2025Parameter Efficient Fine-tuning via Explained Variance Adaptation
403. NeurIPS 2025PathVQ: Reforming Computational Pathology Foundation Model for Whole Slide Image Analysis via Vector Quantization
404. NeurIPS 2025Pay Attention to Small Weights
405. NeurIPS 2025PaZO: Preconditioned Accelerated Zeroth-Order Optimization for Fine-Tuning LLMs
406. NeurIPS 2025PoLAR: Polar-Decomposed Low-Rank Adapter Representation
407. NeurIPS 2025Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward
408. NeurIPS 2025Prompt Tuning Transformers for Data Memorization
409. NeurIPS 2025Provable Meta-Learning with Low-Rank Adaptations
410. NeurIPS 2025Q3R: Quadratic Reweighted Rank Regularizer for Effective Low-Rank Training
411. NeurIPS 2025QBasicVSR: Temporal Awareness Adaptation Quantization for Video Super-Resolution
412. NeurIPS 2025Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning
413. NeurIPS 2025RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
414. NeurIPS 2025REVE: A Foundation Model for EEG - Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects
415. NeurIPS 2025Revisiting Semi-Supervised Learning in the Era of Foundation Models
416. NeurIPS 2025RidgeLoRA: Matrix Ridge Enhanced Low-Rank Adaptation of Large Language Models
417. NeurIPS 2025RoFt-Mol: Benchmarking Robust Fine-tuning with Molecular Graph Foundation Models
418. NeurIPS 2025RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
419. NeurIPS 2025S'MoRE: Structural Mixture of Residual Experts for Parameter-Efficient LLM Fine-tuning
420. NeurIPS 2025Seemingly Redundant Modules Enhance Robust Odor Learning in Fruit Flies
421. NeurIPS 2025Self-Improving Embodied Foundation Models
422. NeurIPS 2025SEMPO: Lightweight Foundation Models for Time Series Forecasting
423. NeurIPS 2025SensorLM: Learning the Language of Wearable Sensors
424. NeurIPS 2025Sheetpedia: A 300K-Spreadsheet Corpus for Spreadsheet Intelligence and LLM Fine-Tuning
425. NeurIPS 2025Single GPU Task Adaptation of Pathology Foundation Models for Whole Slide Image Analysis
426. NeurIPS 2025SPACE: Noise Contrastive Estimation Stabilizes Self-Play Fine-Tuning for Large Language Models
427. NeurIPS 2025Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning
428. NeurIPS 2025Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Bootstrapping
429. NeurIPS 2025StelLA: Subspace Learning in Low-rank Adaptation using Stiefel Manifold
430. NeurIPS 2025Synergy over Discrepancy: A Partition-Based Approach to Multi-Domain LLM Fine-Tuning
431. NeurIPS 2025TabDPT: Scaling Tabular Foundation Models on Real Data
432. NeurIPS 2025TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields
433. NeurIPS 2025Tabula: A Tabular Self-Supervised Foundation Model for Single-Cell Transcriptomics
434. NeurIPS 2025Test-Time Adaptive Object Detection with Foundation Model
435. NeurIPS 2025Test-Time Spectrum-Aware Latent Steering for Zero-Shot Generalization in Vision-Language Models
436. NeurIPS 2025The Best Instruction-Tuning Data are Those That Fit
437. NeurIPS 2025The Illusion of Progress? A Critical Look at Test-Time Adaptation for Vision-Language Models
438. NeurIPS 2025The Primacy of Magnitude in Low-Rank Adaptation
439. NeurIPS 2025This Time is Different: An Observability Perspective on Time Series Foundation Models
440. NeurIPS 2025To Think or Not To Think: A Study of Thinking in Rule-Based Visual Reinforcement Fine-Tuning
441. NeurIPS 2025Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper
442. NeurIPS 2025Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement
443. NeurIPS 2025Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs
444. NeurIPS 2025Towards Robust Parameter-Efficient Fine-Tuning for Federated Learning
445. NeurIPS 2025Towards the Resistance of Neural Network Fingerprinting to Fine-tuning
446. NeurIPS 2025Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning
447. NeurIPS 2025Triplets Better Than Pairs: Towards Stable and Effective Self-Play Fine-Tuning for LLMs
448. NeurIPS 2025TS-MOF: Two-Stage Multi-Objective Fine-tuning for Long-Tailed Recognition
449. NeurIPS 2025UFT: Unifying Supervised and Reinforcement Fine-Tuning
450. NeurIPS 2025UGM2N: An Unsupervised and Generalizable Mesh Movement Network via M-Uniform Loss

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.