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-oriented 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 the retrieved structural counterfactuals are semantically equivalent to, and often preferred over, exact GED-based ground-truth explanations.

翻译：随着人工智能模型日益复杂，可解释性对于建立信任至关重要，然而基于概念的反事实方法仍在表达力与效率之间面临权衡。将底层概念表示为原子集合虽然快速，但缺乏关系上下文；而完整的图表示虽然更忠实，但需要解决NP难的图编辑距离（GED）问题。我们提出U-CECE，一个统一的、模型无关的多分辨率概念反事实解释框架，能够根据数据规模和计算预算进行调整。U-CECE涵盖三个表达力层级：用于宽泛解释的原子概念、用于简单交互的关系集之集合，以及用于完整语义结构的结构化图。在结构化层级，既支持基于监督图神经网络（GNN）的精确导向的直推式模式，也支持基于无监督图自编码器（GAE）的可扩展归纳式模式。在结构上迥异的CUB和Visual Genome数据集上的实验刻画了各层级间效率与表达力的权衡，而人工调查和基于LVLM的评估表明，检索到的结构化反事实在语义上等同于——且往往优于——基于精确GED的真实解释。