Baseline review of Formal Methods and Foundations of Artificial Intelligence

Kusper, Gábor (2026) Baseline review of Formal Methods and Foundations of Artificial Intelligence. ANNALES MATHEMATICAE ET INFORMATICAE, 63. pp. 65-72. ISSN 1787-6117

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Abstract

Artificial Intelligence (AI) is advancing rapidly, yet many successful models remain opaque and provide limited assurance about reliability, safety, and failure modes. This motivates renewed interest in formal methods and foundational perspectives that can support trustworthy AI beyond empirical testing. This paper presents a baseline review of the selected papers volume of the inaugural International Conference on Formal Methods and Foundations of Artificial Intelligence (FMF-AI 2025), published as Annales Mathematicae et Informaticae, Vol. 61 (2025). The goal is twofold: (i) to map and summarize the first FMF-AI “snapshot” as a starting point for the Hungarian research ecosystem, and (ii) to define a reproducible baseline that can serve as a reference for measuring topical and methodological shifts in subsequent FMF-AI editions. The review clusters the twenty selected papers into five thematic groups and records their relative prevalence. In addition, it introduces simple baseline metrics that can be recomputed in future FMF-AI editions to observe structural changes in the research landscape. The main pattern is a clear imbalance between verification-oriented contributions and papers that primarily use AI methods in application or optimization contexts.

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