Researchers have developed a new method for training deterministic register automata (DRAs) on data from infinite alphabets. This approach involves using oracles that can answer membership, equivalence, and memorability queries to learn DRAs over ordered domains such as the rationals and integers. The study shows that these algorithms can be unified into a single framework, allowing for more efficient learning of DRAs. Additionally, this work demonstrates that minimization of DRAs over non-dense ordered domains is decidable. --- Why it matters: This research matters to AI engineers because it provides new insights into the learnability of deterministic register automata, which are crucial components in many AI systems, particularly those dealing with infinite data sets. The unified framework developed here can lead to more efficient and scalable learning of DRAs, enabling better performance in applications such as natural language processing and computer vision. Source: https://arxiv.org/abs/2608.18765