EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs

Machine learning models are primarily judged by predictive performance, especially in applied settings. Once a model reaches high accuracy, its explanation is often assumed to be correct and trustworthy. This assumption raises an overlooked question: when two models achieve high accuracy, do they rely on the same internal logic, or do they reach the same outcome via different and potentially competing mechanisms? We introduce EvoXplain, a diagnostic framework that measures the stability of model explanations across repeated training. Rather than analysing the explanation of a single trained model, EvoXplain treats explanations as samples drawn from the training and model selection pipeline itself, without aggregating predictions or constructing ensembles. It examines whether these samples form a single coherent explanatory basin or separate into multiple structured explanatory basins. We evaluate EvoXplain on the Adult Income and Breast Cancer datasets using deep neural networks and Logistic Regression. Although all models achieve high predictive accuracy, explanation stability differs across pipelines. Deep neural networks on Breast Cancer converge to a single explanatory basin, while the same architecture on Adult Income separates into distinct explanatory basins despite identical training conditions. Logistic Regression on Breast Cancer exhibits conditional multiplicity, where basin accessibility is controlled by regularisation configuration. EvoXplain does not attempt to select a correct explanation. Instead, it makes explanatory structure visible and quantifiable, revealing when single instance explanations obscure the existence of multiple admissible predictive mechanisms. More broadly, EvoXplain reframes interpretability as a property of the training pipeline under repeated instantiation, rather than of any single trained model.