U-CECE: A Universal Multi-Resolution Framework for Conceptual Counterfactual Explanations

Abstract:As AI models grow more complex, explainability is essential for building trust, yet concept-based counterfactual methods still face a trade-off between expressivity and efficiency. Representing underlying concepts as atomic sets is fast but misses relational context, whereas full graph representations are more faithful but require solving the NP-hard Graph Edit Distance (GED) problem. We propose U-CECE, a unified, model-agnostic multi-resolution framework for conceptual counterfactual explanations that adapts to data regime and compute budget. U-CECE spans three levels of expressivity: atomic concepts for broad explanations, relational sets-of-sets for simple interactions, and structural graphs for full semantic structure. At the structural level, both a precision-{\omicron}riented transductive mode based on supervised Graph Neural Networks (GNNs) and a scalable inductive mode based on unsupervised graph autoencoders (GAEs) are supported. Experiments on the structurally divergent CUB and Visual Genome datasets characterize the efficiency-expressivity trade-off across levels, while human surveys and LVLM-based evaluation show that, on CUB, the retrieved structural counterfactuals are frequently judged semantically equivalent to, and often preferred over, reference deterministic GED-based explanations.

Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2604.08295 [cs.AI]

(or arXiv:2604.08295v4 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2604.08295 Focus to learn more arXiv-issued DOI via DataCite

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