Fair and Adventurous Enumeration of Quantifier Instantiations
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Abstract (arXiv)
SMT solvers generally tackle quantifiers by instantiating their variables with tuples of terms from the ground part of the formula. Recent enumerative approaches for quantifier instantiation consider tuples of terms in some heuristic order. This paper studies different strategies to order such tuples and their impact on performance. We decouple the ordering problem into two parts. First is the order of the sequence of terms to consider for each quantified variable, and second is the order of the instantiation tuples themselves. While the most and least preferred tuples, i.e. those with all variables assigned to the most or least preferred terms, are clear, the combinations in between allow flexibility in an implementation. We look at principled strategies of complete enumeration, where some strategies are more fair, meaning they treat all the variables the same but some strategies may be more adventurous, meaning that they may venture further down the preference list. We further describe new techniques for discarding irrelevant instantiations which are crucial for the performance of these strategies in practice. These strategies are implemented in the SMT solver cvc5, where they contribute to the diversification of the solver's configuration space, as shown by our experimental results.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 0 of its 9 references that are in the map. First posted on arXiv 28 May 2021.
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Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2024Solving Hard Mizar Problems with Instantiation and Strategy Inventioncited by 1
- 2024Machine Learning for Quantifier Selection in cvc5cited by 0
- 2025Quantifier Instantiations: To Mimic or To Revolt?cited by 0
- 2025Quantifier Instantiations: To Mimic or To Revolt?19 Aug 2025
- 2024Machine Learning for Quantifier Selection in cvc526 Aug 2024
- 2024Solving Hard Mizar Problems with Instantiation and Strategy Invention25 Jun 2024