In this paper, we study the role of machine-learned predictions inofflineNP-hard problems. For offline problems, an algorithm has no information disadvantage compared to an optimal solution: thedisadvantage iscomputational.

However, most existing meta-learning approaches only can learn from tasks without any constraint.

Bridging logical reasoning and deep learning is crucial for advanced AI systems.