# Extending AALpy with Passive Learning: A Generalized State-Merging Approach

arXiv: https://arxiv.org/abs/2506.06333 · HTML: https://mlsanity.com/paper/2506.06333/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 5 of 5)

- [Automata Learning meets Shielding](https://mlsanity.com/paper/2212.01838/) (2022)
- [Enhancing Automata Learning with Statistical Machine Learning: A Network Security Case Study](https://mlsanity.com/paper/2405.11141/) (2024)
- [FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata](https://mlsanity.com/paper/2203.16331/) (2022)
- [Probabilistic Black-Box Checking via Active MDP Learning](https://mlsanity.com/paper/2308.07930/) (2023)
- [A Framework for Streaming Event-Log Prediction in Business Processes](https://mlsanity.com/paper/2412.16032/) (2024)

## Built on by (most cited first, 3 of 3)

- [PrefixGuard: From LLM-Agent Traces to Online Failure-Warning Monitors](https://mlsanity.com/paper/2605.06455/) (2026)
- [Inferring Reward Machines and Transition Machines from Partially Observable Markov Decision Processes](https://mlsanity.com/paper/2508.01947/) (2025)
- [ATLAS: Discovering Agent Strategies through LLM-Guided Abstraction and Automata Learning](https://mlsanity.com/paper/2608.14352/) (2026)

## Abstract (arXiv)

AALpy is a well-established open-source automata learning library written in Python with a focus on active learning of systems with IO behavior. It provides a wide range of state-of-the-art algorithms for different automaton types ranging from fully deterministic to probabilistic automata. In this work, we present the recent addition of a generalized implementation of an important method from the domain of passive automata learning: state-merging in the red-blue framework. Using a common internal representation for different automaton types allows for a general and highly configurable implementation of the red-blue framework. We describe how to define and execute state-merging algorithms using AALpy, which reduces the implementation effort for state-merging algorithms mainly to the definition of compatibility criteria and scoring. This aids the implementation of both existing and novel algorithms. In particular, defining some existing state-merging algorithms from the literature with AALpy only takes a few lines of code.