OPTIMUS-Prime: Minimal and Sufficient Concept Explanations for Deep Vision Models

2026-06-05

AI Summary

Existing visual explanation methods often lack theoretical guarantees and struggle to balance interpretability with logical rigor. This work proposes OPTIMUS, a novel framework that introduces prime implicant theory into visual explanations for the first time, generating concept-based saliency maps that highlight regions logically sufficient and minimally necessary to support the model’s prediction. By integrating concept-based explanations, formal verification, and heatmap generation, OPTIMUS establishes an explainable AI system with formal correctness guarantees. Experimental results on visual classification benchmarks demonstrate that OPTIMUS accurately identifies the key concepts relied upon by the model, producing explanations that are both logically sound and visually coherent.

Abstract

The growing demand for transparency in automated decision-making has propelled eXplainable Artificial Intelligence (XAI) to the forefront of machine learning research. In computer vision, however, existing explanation methods often prioritize end-user accessibility at the expense of formal guarantees, leaving a critical gap between practical utility and theoretical rigor. In this paper, we address this gap by introducing OPTIMUS, a novel framework for generating concept-based visual explanations for deep classification models. OPTIMUS explanations take the form of visual heatmaps that not only remain interpretable to end users, but are grounded in the well-established theory of prime implicants, providing formal guarantees that have been largely absent from existing saliency-based methods. Specifically, OPTIMUS explanations satisfy two desirable properties: sufficiency, ensuring that the highlighted concepts provably guarantee the classifier's prediction, and minimality, ensuring that no strict subset of those concepts retains this guarantee. Together, these properties yield explanations that are both logically tight and visually coherent. We validate our approach on a visual classification benchmark, demonstrating that OPTIMUS heatmaps naturally and faithfully surface the decision-relevant concepts underlying model predictions.

Problem

• Explainable Artificial Intelligence
• concept-based explanations
• formal guarantees
• sufficiency
• minimality

Innovation

• prime implicants
• concept-based explanation
• sufficiency
• minimality
• explainable AI