We present graph backtracking, a novel, fine-grained backtracking scheme for CDCL-based SAT solving, parametrized by a user-defined weight function. For conflict repair, we challenge the decision level abstraction and use the implication graph as a precise guiding structure to minimize the weight of literals that are unassigned. Graph backtracking is sound, complete, and terminating. We show that it is a generalization of chronological and non-chronological backtracking by simulating them with specific weight functions. Our approach is implemented in the experimental solver NapSAT. Empirical results show that graph backtracking requires fewer literal propagations than standard approaches, leading to improved solver runtime.

Abstract The Model Constructing Satisfiability (MCSat) approach to the SMT problem extends the ideas of CDCL from the SAT level to the theory level. Like SAT, its search is driven by incrementally constructing a model by assigning concrete values to theory variables and performing theory-level reasoning to learn lemmas when conflicts arise. Therefore, the selection of values can significantly impact the search process and the solver’s performance. In this work, we propose guiding the MCSat search by utilizing assignment values discovered through local search. First, we present a theory-agnostic framework to seamlessly integrate local search techniques within the MCSat framework. Then, we highlight how to use the framework to design a search procedure for (quantifier-free) Nonlinear Integer Arithmetic ( $$\mathcal {NIA}$$ NIA ), utilizing accelerated hill-climbing and a new operation called feasible-sets jumping . We implement the proposed approach in the MCSat engine of the Yices2 solver, and empirically evaluate its performance over the $$\mathcal {NIA}$$ NIA benchmarks of SMT-LIB.

Abstract The Model Constructing Satisfiability (MCSat) approach to Satisfiability Modulo Theories (SMT) has demonstrated strong performance when handling complex theories such as nonlinear arithmetic. Despite being in development for over a decade, there has been limited research on the heuristics utilized by MCSat solvers as in Yices2. In this paper, we discuss the decision heuristics employed in the MCSat approach of Yices2 and empirically show their significance on QF_NRA and QF_NIA benchmarks. Additionally, we propose new ideas to enhance these heuristics by leveraging theory-specific reasoning and drawing inspiration from recent advancements in SAT solvers. Our new version of the MCSat Yices2 solver not only solves more nonlinear arithmetic benchmarks than before but is also more efficient compared to other leading SMT solvers.

Abstract This system description introduces an enhancement to the Yices2 SMT solver, enabling it to reason over non-linear polynomial systems over finite fields. Our reasoning approach fits into the model-constructing satisfiability (MCSat) framework and is based on zero decomposition techniques, which find finite basis explanations for theory conflicts over finite fields. As the MCSat solver within Yices2 can support (and combine) several theories via theory plugins, we implemented our reasoning approach as a new plugin for finite fields and extended Yices2 ’s frontend to parse finite field problems, making our implementation the first MCSat-based reasoning engine for finite fields. We present its evaluation on finite field benchmarks, comparing it against cvc5. Additionally, our work leverages the modular architecture of the MCSat solver in Yices2 to provide a foundation for the rapid implementation of further reasoning techniques for this theory.

Non-linear polynomial systems over finite fields are used to model functional behavior of cryptosystems, with applications in system security, computer cryptography, and post- quantum cryptography. Solving polynomial systems is also one of the most difficult problems in mathematics. In this paper, we propose an automated reasoning procedure for deciding the satisfiability of a system of non-linear equations over finite fields. We introduce zero decomposition techniques to prove that polynomial constraints over finite fields yield finite basis explanation functions. We use these explanation functions in model constructing satisfiability solving, allowing us to equip a CDCL-style search procedure with tailored theory reasoning in SMT solving over finite fields. We implemented our approach and provide a novel and effective reasoning prototype for non-linear arithmetic over finite fields.