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Approximate SMT Counting Beyond Discrete Domains

arXiv 2507.18612 ↗First posted 24 Jul 2025cs.LO cs.AIcited by 0

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Abstract (arXiv)

Satisfiability Modulo Theory (SMT) solvers have advanced automated reasoning, solving complex formulas across discrete and continuous domains. Recent progress in propositional model counting motivates extending SMT capabilities toward model counting, especially for hybrid SMT formulas. Existing approaches, like bit-blasting, are limited to discrete variables, highlighting the challenge of counting solutions projected onto the discrete domain in hybrid formulas. We introduce pact, an SMT model counter for hybrid formulas that uses hashing-based approximate model counting to estimate solutions with theoretical guarantees. pact makes a logarithmic number of SMT solver calls relative to the projection variables, leveraging optimized hash functions. pact achieves significant performance improvements over baselines on a large suite of benchmarks. In particular, out of 3119 instances, pact successfully finished on 456 instances, while Baseline could finish on 83 instances.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 49 references that are in the map. First posted on arXiv 24 Jul 2025.

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References · 11

  1. 2019Quantitative Verification of Neural Networks And its Security Applicationscited by 56
  2. 2013A Scalable Approximate Model Countercited by 20
  3. 2014Approximate Counting in SMT and Value Estimation for Probabilistic Programscited by 6
  4. 2023Rounding Meets Approximate Model Countingcited by 6
  5. 2015Approximate Probabilistic Inference via Word-Level Countingcited by 5
  6. 2017Probabilistic Model Counting with Short XORscited by 5
  7. 2019solc-verify: A Modular Verifier for Solidity Smart Contractscited by 5
  8. 2015High Speed Hashing for Integers and Stringscited by 4
  9. 2017Bit-Vector Model Counting using Statistical Estimationcited by 2
  10. 2023Approximate Integer Solution Counts over Linear Arithmetic Constraintscited by 2
  11. 2017A New Probabilistic Algorithm for Approximate Model Countingcited by 1

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