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arXiv · 2610.40042

UPPAAi: Towards LLM-Assisted Modeling, Verification, Analysis and Synthesis

Peter Dolog·Kim G. Larsen·Marius Mikučionis·Frederik F. K. Rasmussen
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Abstract

For more than three decades, UPPAAL has supported the modelling and analysis of real-time and cyber-physical systems. Yet building a suitable model, formalising the intended properties, choosing analysis techniques and interpreting counterexamples remain iterative tasks that demand substantial expertise. Large language models (LLMs) can offer plausible explanations and even solutions, but they are not necessarily correct. We present UPPAAI, an extension of UPPAAL in which users construct, explain, debug and specify models in dialogue with an LLM. The guiding principle is that the user requests, the LLM proposes, explains and orchestrates, and UPPAAL verifies. UPPAAI exposes the UPPAAL editor, queries and verifier through a Tools API. We report on seven case studies, from explaining to a newcomer to learning a controller, covering timed automata, stochastic hybrid automata and strategy learning. LLMs proved to be extremely helpful in not just generating solutions but also pro-actively suggesting relevant UPPAAL features it was not asked about.

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Peter Dolog, Kim G. Larsen, Marius Mikučionis, Frederik F. K. Rasmussen. 2026-10-29. UPPAAi: Towards LLM-Assisted Modeling, Verification, Analysis and Synthesis. https://arxiv.org/abs/2610.40042

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