  The advent of powerful neural classifiers has increased interest in problems
that require both learning and reasoning. These problems are critical for
understanding important properties of models, such as trustworthiness,
generalization, interpretability, and compliance to safety and structural
constraints. However, recent research observed that tasks requiring both
learning and reasoning on background knowledge often suffer from reasoning
shortcuts (RSs): predictors can solve the downstream reasoning task without
associating the correct concepts to the high-dimensional data. To address this
issue, we introduce rsbench, a comprehensive benchmark suite designed to
systematically evaluate the impact of RSs on models by providing easy access to
highly customizable tasks affected by RSs. Furthermore, rsbench implements
common metrics for evaluating concept quality and introduces novel formal
verification procedures for assessing the presence of RSs in learning tasks.
Using rsbench, we highlight that obtaining high quality concepts in both purely
neural and neuro-symbolic models is a far-from-solved problem. rsbench is
available at: https://unitn-sml.github.io/rsbench.
