让概念推理可解释，同时提升模型准确率和训练效率。
Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning
- 用有向无环图建模概念间逻辑关系，通过注意力选择规则。
- 在多个数据集上达到顶尖性能，且支持人工干预提升效果。
- 适合需要可解释性与知识注入的场景，如医疗诊断、金融风控。
基于概念的模型（CBMs）通过高层概念解释预测结果，先预测概念再完成下游任务。然而现有方法仅对最终任务预测提供可解释性，概念预测本身仍为黑箱。为此，本文提出分层概念记忆推理器（H-CMR），同时实现概念与任务预测的可解释性。H-CMR利用学习得到的有向无环图建模概念间的逻辑关系，边代表以其他概念定义当前概念的规则。推理时，神经注意力机制选取一组规则，按层级应用以预测所有概念及最终任务。实验表明，H-CMR在多个基准上达到先进性能，同时支持人类通过干预概念或模型提升准确率（推理时）或数据效率（训练时），尤其在具备背景知识时表现更优。
原文摘要 · Abstract (English)
Concept-Based Models (CBMs) are a class of deep learning models that provide interpretability by explaining predictions through high-level concepts. These models first predict concepts and then use them to perform a downstream task. However, current CBMs offer interpretability only for the final task prediction, while the concept predictions themselves are typically made via black-box neural networks. To address this limitation, we propose Hierarchical Concept Memory Reasoner (H-CMR), a new CBM that provides interpretability for both concept and task predictions. H-CMR models relationships between concepts using a learned directed acyclic graph, where edges represent logic rules that define concepts in terms of other concepts. During inference, H-CMR employs a neural attention mechanism to select a subset of these rules, which are then applied hierarchically to predict all concepts and the final task. Experimental results demonstrate that H-CMR matches state-of-the-art performance while enabling strong human interaction through concept and model interventions. The former can significantly improve accuracy at inference time, while the latter can enhance data efficiency during training when background knowledge is available.
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