By integrating hard constraints into neural network outputs, we not only improve the reliability of AI systems but also pave the way for meta-cognitive capabilities that ensure the alignment of predictions with domain-specific knowledge.

This topic has received a lot of attention, however, existing methods either impose the constraints in a “weak” form at training time, with no guarantees at inference, or fail to provide a general framework that supports different tasks and constraint types.

We tackle this open problem from a neuro-symbolic perspective, developing a pipeline that enhances a conventional neural predictor with a symbolic reasoning module capable of correcting structured prediction errors and a neural attention module that learns to direct the reasoning effort to focus on potential prediction errors, while keeping other outputs unchanged.

This framework provides an appealing trade-off between the efficiency of constraint-free neural inference and the prohibitive cost of exhaustive reasoning at inference time that satisfies the rigorous demands of meta-cognitive assurance.