AI 中文总结
研究神经符号系统易出现的推理捷径问题，提出基于矩阵的可微逻辑编程方法，通过统一编码规则和约束、连接模糊逻辑t-范数等减轻两种捷径类型，实验表明该方法能显著减少捷径，架构选择对减轻捷径起关键作用。
AI 中文摘要
神经符号（NeSy）系统将神经网络与逻辑推理相结合，以实现泛化性和可解释性，但最近的研究表明它们容易出现捷径推理行为。我们提出了一种使用基于矩阵的可微逻辑编程的新方法，以减轻两种现象中的推理捷径：约束满足捷径，即在未完成预期任务的情况下满足约束；认知捷径，即尽管推理逻辑合理，但有偏差的数据会导致语义上不正确的概念映射。基于最近基于矩阵的逻辑编程语义，我们引入了减轻捷径的设计元素，包括在单个矩阵中对规则和约束进行统一编码。我们还确定了与模糊逻辑t-范数的联系，并通过实验比较了它们的梯度流属性。通过在MNIST变体上精心设计的实验，我们表明，与依赖软概率分布的先前方法相比，将神经输出一对一地锚定到逻辑原子可显著减少两种捷径类型。然后，我们证实了在将符号知识与神经学习相结合时的架构选择在减轻捷径方面起着关键作用。
英文摘要
Neurosymbolic (NeSy) systems integrate neural networks with logical reasoning to achieve both generalization and interpretability, but recent work has shown they are susceptible to shortcut reasoning behaviors. We propose a novel method using matrix-based differentiable logic programming to mitigate reasoning shortcuts in two phenomena: constraint satisfaction shortcuts, where constraints are satisfied without achieving the intended task, and cognition shortcuts, where biased data leads to semantically incorrect concept mappings despite logically sound inference. Building on recent matrix-based logic programming semantics, we introduce design elements to mitigate shortcuts, including a unified encoding of rules and constraints in a single matrix. We also identify connections to fuzzy logic t-norms and empirically compare their gradient flow properties. Through carefully designed experiments on MNIST variants, we show that one-to-one grounding of neural outputs to logical atoms significantly reduces both shortcut types compared to previous methods that rely on soft probability distributions. We then confirm that architectural choices in coupling symbolic knowledge with neural learning play a critical role in shortcut mitigation.
CommentsIn Proceedings ICLP 2026, arXiv:2607.17707
Journal refEPTCS 450, 2026, pp. 29-51
