Fine-grained explainability for complex inference chains

Achieve fine-grained explainability for complex inference chains in Neuro-Symbolic AI by making each inferential step and its dependence on symbolic rules and neural components interpretable.

Background

Although some systems integrate logic into loss functions or constraints to improve trustworthiness, the authors note that producing detailed, step-level explanations for multi-step inference remains an open problem.