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To this end, we learn a deterministic finite automaton as a <jats:italic>surrogate model<\/jats:italic> from a given RNN using active automata learning. This model may then be analyzed using <jats:italic>model checking<\/jats:italic> as a verification technique. The term <jats:italic>property-directed<\/jats:italic> reflects the idea that our procedure is guided and controlled by the given property rather than performing the two steps separately. We show that this not only allows us to discover <jats:italic>small<\/jats:italic> counterexamples fast, but also to generalize them by pumping toward faulty flows hinting at the underlying error in the RNN. 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