mlsanityData to 14 Oct 2026

Model Reprogramming Demystified: A Neural Tangent Kernel Perspective

arXiv 2506.00620 ↗First posted 31 May 2025cs.LGcited by 2

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Abstract (arXiv)

Model Reprogramming (MR) is a resource-efficient framework that adapts large pre-trained models to new tasks with minimal additional parameters and data, offering a promising solution to the challenges of training large models for diverse tasks. Despite its empirical success across various domains such as computer vision and time-series forecasting, the theoretical foundations of MR remain underexplored. In this paper, we present a comprehensive theoretical analysis of MR through the lens of the Neural Tangent Kernel (NTK) framework. We demonstrate that the success of MR is governed by the eigenvalue spectrum of the NTK matrix on the target dataset and establish the critical role of the source model's effectiveness in determining reprogramming outcomes. Our contributions include a novel theoretical framework for MR, insights into the relationship between source and target models, and extensive experiments validating our findings.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 30 references that are in the map. First posted on arXiv 31 May 2025.

Builds on

24 of 30 references.

References · 24

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
  3. 2019Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descentcited by 738
  4. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  5. 2018Gradient Descent Finds Global Minima of Deep Neural Networkscited by 574
  6. 2019Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networkscited by 566
  7. 2022Exploring Visual Prompts for Adapting Large-Scale Modelscited by 274
  8. 2020Dataset Meta-Learning from Kernel Ridge-Regressioncited by 230
  9. 2021WARP: Word-level Adversarial ReProgrammingcited by 221
  10. 2019The Convergence Rate of Neural Networks for Learned Functions of Different Frequenciescited by 135
  11. 2018Adversarial Reprogramming of Neural Networkscited by 116
  12. 2019Dynamics of Deep Neural Networks and Neural Tangent Hierarchycited by 96
12 more
  1. 2021Voice2Series: Reprogramming Acoustic Models for Time Series Classificationcited by 91
  2. 2020Bayesian Deep Ensembles via the Neural Tangent Kernelcited by 90
  3. 2022Understanding and Improving Visual Prompting: A Label-Mapping Perspectivecited by 87
  4. 2022Model Reprogramming: Resource-Efficient Cross-Domain Machine Learningcited by 64
  5. 2021Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nystr"om Method, and Use of Kernels in Machine Learning: Tutorial and Surveycited by 37
  6. 2023AutoVP: An Automated Visual Prompting Framework and Benchmarkcited by 29
  7. 2023From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognitioncited by 23
  8. 2022Reprogramming Pretrained Language Models for Antibody Sequence Infillingcited by 20
  9. 2023Exploring the Benefits of Visual Prompting in Differential Privacycited by 17
  10. 2023Reprogramming Pretrained Language Models for Protein Sequence Representation Learningcited by 12
  11. 2022Adversarial Reprogramming Revisitedcited by 10
  12. 2023Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspectivecited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instructioncited by 4
  2. 2026Linearization Explains Fine-Tuning in Large Language Modelscited by 2