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Random Models and Guarded Logic

Authors: Oskar Fiuk

Published in: LIPIcs, Volume 364, 43rd International Symposium on Theoretical Aspects of Computer Science (STACS 2026)


Abstract
Building on ideas of Gurevich and Shelah for the Gödel Class, we present a new probabilistic proof of the finite model property for the Guarded Fragment of First-Order Logic. Our proof is conceptually simple and yields the optimal doubly-exponential upper bound on the size of minimal models. We precisely analyse the obtained bound, up to constant factors in the exponents, and construct sentences that enforce models of tightly matching size. The probabilistic approach adapts naturally to the Triguarded Fragment, an extension of the Guarded Fragment that also subsumes the Two-Variable Fragment. Finally, we derandomise the probabilistic proof by providing an explicit model construction which replaces randomness with deterministic hash functions.

Cite as

Oskar Fiuk. Random Models and Guarded Logic. In 43rd International Symposium on Theoretical Aspects of Computer Science (STACS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 364, pp. 37:1-37:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)


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@InProceedings{fiuk:LIPIcs.STACS.2026.37,
  author =	{Fiuk, Oskar},
  title =	{{Random Models and Guarded Logic}},
  booktitle =	{43rd International Symposium on Theoretical Aspects of Computer Science (STACS 2026)},
  pages =	{37:1--37:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-412-3},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{364},
  editor =	{Mahajan, Meena and Manea, Florin and McIver, Annabelle and Thắng, Nguy\~{ê}n Kim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2026.37},
  URN =		{urn:nbn:de:0030-drops-255269},
  doi =		{10.4230/LIPIcs.STACS.2026.37},
  annote =	{Keywords: guarded fragment, finite model property, probabilistic method}
}
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