Machine learning for quantifier selection in cvc5

In this work we considerably improve the real-time performance of state-of-the-art SMT solving on first-order quantified problems by efficient machine learning guidance of quantifier selection. Quantifiers represent a significant challenge for SMT and are technically a source of undecidability. In our approach, we train an efficient machine learning model that informs the solver which quantifiers should be instantiated and which not. Each quantifier may be instantiated multiple times and the set of the currently active quantifiers changes as the solving progresses. Therefore, we invoke the ML predictor many times, during the whole run of the solver. To make this efficient, we use fast ML models based on gradient boosted decision trees. We integrate our approach into the state-of-the-art cvc5 SMT solver and show a considerable increase of the system’s holdout-set performance after training it on large sets of first-order problems. The method is tested in several ways, using both single-strategy and portfolio approaches. The evaluation is done on two large formal verification corpora: first-order problems created from the Mizar Mathematical Library, and first-order problems created from the HOL4 standard library.