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arXiv · 2601.20627

DIVERSE: Disagreement-Inducing Vector Evolution for Rashomon Set Exploration

Gilles Eerlings·Brent Zoomers·Jori Liesenborgs·Gustavo Rovelo Ruiz·Kris Luyten
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Abstract

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.

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Gilles Eerlings, Brent Zoomers, Jori Liesenborgs, Gustavo Rovelo Ruiz, Kris Luyten. 2026-01-28. DIVERSE: Disagreement-Inducing Vector Evolution for Rashomon Set Exploration. https://arxiv.org/abs/2601.20627

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