Many fields of computational science advance through improvements in the algorithms used
for solving key problems. These advancements are often facilitated by benchmarks and …

Satisfiability modulo theories (SMT) solvers implement a wide range of optimizations that are
often tailored to a particular class of problems, and that differ significantly between solvers …

This paper presents MachSMT, an algorithm selection tool for Satisfiability Modulo Theories
(SMT) solvers. MachSMT supports the entirety of the SMT-LIB language and standardized …

We present BanditFuzz, a multi-agent reinforcement learning (RL) guided performance
fuzzer for state-of-the-art Satisfiability Modulo Theories (SMT) solvers. BanditFuzz constructs …

This paper applies machine learning (ML) to solve quantified satisfiability modulo theories
(SMT) problems more efficiently. The motivating idea is that the solver should learn from …

SMT solvers are often used in the back end of different software engineering tools─ eg,
program verifiers, test generators, or program synthesizers. There are a plethora of …

Automated theorem provers and formal proof assistants are general reasoning systems that
are in theory capable of proving arbitrarily hard theorems, thus solving arbitrary problems …

This paper develops an approach to the scheduling of solvers in the domain of Satisfiability
Modulo Theories (SMT) using a Graph Neural Network (GNN). In contrast to related …

Many modern software engineering tools integrate SMT decision procedures and rely on the
accuracy and performance of SMT solvers. We describe four basic patterns for integrating …

We present a generic method to configure an automated reasoning solver in order to
increase its performance on selected target problems. We describe a strategy invention …