The field of verification and automata learning is moving towards the integration of neural networks and formal methods to address scalability challenges. Researchers are exploring the potential of neural approaches to learn complex languages and automate the verification process. Notably, studies have shown that recurrent neural networks can generalize to omega-regular languages, and new algorithms have been proposed for learning one-counter automata. Additionally, novel automata models, such as AP-observation automata, have been introduced to improve abstraction-based verification of continuous-time systems.

Some noteworthy papers include: RNN Generalization to Omega-Regular Languages, which demonstrates the feasibility of neural approaches for learning complex omega-regular languages. Scalable Learning of One-Counter Automata via State-Merging Algorithms, which proposes an effective algorithm for learning deterministic real-time one-counter automata. AP-observation Automata for Abstraction-based Verification of Continuous-time Systems, which introduces a novel automata model for abstraction-based verification.