# Property-Directed Verification of Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2009.10610 · HTML: https://mlsanity.com/paper/2009.10610/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Bridging Efficiency and Safety: Formal Verification of Neural Networks with Early Exits](https://mlsanity.com/paper/2512.20755/) (2025)
- [Talking with Verifiers: Automatic Specification Generation for Neural Network Verification](https://mlsanity.com/paper/2603.02235/) (2026)

## Abstract (arXiv)

This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a surrogate model from a given RNN using active automata learning. This model may then be analyzed using model checking as verification technique. The term property-directed 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 small counterexamples fast, but also to generalize them by pumping towards faulty flows hinting at the underlying error in the RNN.