AI RESEARCH
CUBE: Contrastive Understanding by Balanced Experiments
arXiv CS.AI
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ArXi:2509.10825v5 Announce Type: replace-cross Explaining a trained model requires a clear account of how explanatory evidence is generated. We propose CUBE, a post-hoc explanation framework that brings factorial experimental design to black-box model analysis. CUBE evaluates a trained predictor on balanced low--high probe combinations and summarizes the responses as factorial effects. Main effects and pairwise interactions are interpreted as controlled contrasts on a specified explanation region.