We propose DIVERSE, a framework for systematically exploring the Rashomon set of deep neural networks, the collection of models that match a reference model's accuracy while differing in their predictive behavior. DIVERSE augments a pretrained model with Feature-wise Linear Modulation (FiLM) layers and uses Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to search a latent modulation space, generating diverse model variants without retraining or gradient access. Across MNIST, PneumoniaMNIST, and CIFAR-10, DIVERSE uncovers multiple high-performing yet functionally distinct models. Our experiments show that DIVERSE offers a competitive and efficient exploration of the Rashomon set, making it feasible to construct diverse sets that maintain robustness and performance while supporting well-balanced model multiplicity. While retraining remains the baseline to generate Rashomon sets, DIVERSE achieves comparable diversity at reduced computational cost.
@article{arxiv.2601.20627,
title = {DIVERSE: Disagreement-Inducing Vector Evolution for Rashomon Set Exploration},
author = {Gilles Eerlings and Brent Zoomers and Jori Liesenborgs and Gustavo Rovelo Ruiz and Kris Luyten},
journal= {arXiv preprint arXiv:2601.20627},
year = {2026}
}