\textit{FocaLogic}: Logic-Based Interpretation of Visual Model Decisions

• URL: http://arxiv.org/abs/2601.12049v1
• Date: Sat, 17 Jan 2026 13:28:02 GMT
• Title: \textit{FocaLogic}: Logic-Based Interpretation of Visual Model Decisions
• Authors: Chenchen Zhao, Muxi Chen, Qiang Xu,
• Abstract summary: FocaLogic is a model-agnostic framework designed to interpret and quantify visual model decision-making through logic-based representations.<n>FocaLogic identifies minimal interpretable subsets of visual regions-termed visual focuses.<n>It translates these visual focuses into precise and compact logical expressions, enabling transparent and structured interpretations.
• Score: 10.53822145558342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interpretability of modern visual models is crucial, particularly in high-stakes applications. However, existing interpretability methods typically suffer from either reliance on white-box model access or insufficient quantitative rigor. To address these limitations, we introduce FocaLogic, a novel model-agnostic framework designed to interpret and quantify visual model decision-making through logic-based representations. FocaLogic identifies minimal interpretable subsets of visual regions-termed visual focuses-that decisively influence model predictions. It translates these visual focuses into precise and compact logical expressions, enabling transparent and structured interpretations. Additionally, we propose a suite of quantitative metrics, including focus precision, recall, and divergence, to objectively evaluate model behavior across diverse scenarios. Empirical analyses demonstrate FocaLogic's capability to uncover critical insights such as training-induced concentration, increasing focus accuracy through generalization, and anomalous focuses under biases and adversarial attacks. Overall, FocaLogic provides a systematic, scalable, and quantitative solution for interpreting visual models.

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