2026-07-20T05:38:51Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:23752 2025-11-12T13:00:09Z ddc:004 open_access

Towards Practical First-Order Model Counting Kidambi, Ananth K. Singh, Guramrit Dilkas, Paulius Meel, Kuldeep S. First-order model counting knowledge compilation lifted inference First-order model counting (FOMC) is the problem of counting the number of models of a sentence in first-order logic. Since lifted inference techniques rely on reductions to variants of FOMC, the design of scalable methods for FOMC has attracted attention from both theoreticians and practitioners over the past decade. Recently, a new approach based on first-order knowledge compilation was proposed. This approach, called Crane, instead of simply providing the final count, generates definitions of (possibly recursive) functions that can be evaluated with different arguments to compute the model count for any domain size. However, this approach is not fully automated, as it requires manual evaluation of the constructed functions. The primary contribution of this work is a fully automated compilation algorithm, called Crane2, which transforms the function definitions into C++ code equipped with arbitrary-precision arithmetic. These additions allow the new FOMC algorithm to scale to domain sizes over 500,000 times larger than the current state of the art, as demonstrated through experimental results. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Ananth K. Kidambi and Guramrit Singh and Paulius Dilkas and Kuldeep S. Meel 2025 Is Part Of LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SAT.2025.18 urn:nbn:de:0030-drops-237527 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.18 eng https://creativecommons.org/licenses/by/4.0/legalcode