In the past decade, incorporating data-driven AI techniques in system design has become mainstream in almost all branches of science and technology. Typically, systems powered by AI tend to be rather complex, far beyond human understanding. Naturally, one cannot always develop trust in such complex, so-called black-box systems, restricting their widespread use in safety-critical domains.To generate trust in systems, a standard approach in Explainable AI is to build simple explanations using human-understandable models. A number of recent works (Neider & Gavran, 2018) (Camacho & McIlraith, 2019) (Roy et al., 2020 have identified models in temporal logic to be both formal and explainable. Among temporal logics-Linear Temporal Logic (LTL)-arguably the most widely used temporal logic, has received particular focus due to its resemblance to natural language. Moreover, LTL is a de-facto standard in several fields of computer science, including model-checking, program analysis, and motion planning for robotics.Scarlet is one of the most competitive tools for learning explainable models in LTL (Raha et al., 2022). To learn such models, it relies on positive (or desirable) and negative (or undesirable) executions of the system under consideration. Based on the executions, it learns a concise model in LTL that is consistent with the given executions.