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@inproceedings{KSDM25,
  title={Towards Practical First-Order Model Counting},
  author={
    Kidambi, Ananth K. and Singh, Guramrit and Dilkas, Paulius and Meel, Kuldeep
    S.
  },
  booktitle=SAT,
  pages={18:1--18:18},
  year={2025},
  month=aug,
  bib2html_rescat={Counting},
  bib2html_pubtype={Refereed Conference},
  bib2html_dl_pdf=
    {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.18},
  abstract={
    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.
  },
}