Extracting Robust Register Automata from Neural Networks over Data Sequences

TL;DR

This paper introduces a framework for extracting deterministic register automata from neural networks trained on data sequences, enabling interpretable analysis and robustness certification without white-box access.

Contribution

It develops a polynomial-time robustness checker for DRAs and combines it with automata learning algorithms to produce statistically robust surrogate models.

Findings

Successfully extracts accurate automata from neural networks

Provides formal robustness guarantees for neural network predictions

Enables local robustness certification and counterexample generation

Abstract

Automata extraction is a method for synthesising interpretable surrogates for black-box neural models that can be analysed symbolically. Existing techniques assume a finite input alphabet, and thus are not directly applicable to data sequences drawn from continuous domains. We address this challenge with deterministic register automata (DRAs), which extend finite automata with registers that store and compare numeric values. Our main contribution is a framework for robust DRA extraction from black-box models: we develop a polynomial-time robustness checker for DRAs with a fixed number of registers, and combine it with passive and active automata learning algorithms. This combination yields surrogate DRAs with statistical robustness and equivalence guarantees. As a key application, we use the extracted automata to assess the robustness of neural networks: for a given sequence and distance metric, the DRA either certifies local robustness or produces a concrete counterexample. Experiments on recurrent neural networks and transformer architectures show that our framework reliably learns accurate automata and enables principled robustness evaluation. Overall, our results demonstrate that robust DRA extraction effectively bridges neural network interpretability and formal reasoning without requiring white-box access to the underlying network.