aiqbench analysis
This paper improves a local search method for satisfiability modulo nonlinear real arithmetic, a symbolic reasoning problem in automated reasoning and formal verification rather than machine learning. It is filed under cs.AI, so it sits within classical AI, but readers expecting learning-based systems should note that the contribution is algorithmic, and the reported gains are benchmark-level local search improvements.
A Hybrid SMT-NRA Solver: Integrating 2D Cell-Jump-Based Local Search, MCSAT and OpenCAD
arXiv:2507.00557v3 Announce Type: replace Abstract: In this paper, we propose a hybrid framework for Satisfiability Modulo the Theory of Nonlinear Real Arithmetic (SMT-NRA for short). First, we introduce a two-dimensional cell-jump move, called \emph{2D-cell-jump}, generalizing the key operation, cell-jump, of the local search method for SMT-NRA. In contrast to the existing cell-jump, which updates assignments exclusively along a given line, the 2D-cell-jump enables search over an arbitrarily selected plane. Then, we propose an extended local search framework, named \emph{2D-LS} (following the existing local search framework, LS, for SMT-NRA). Finally, we present a hybrid framework for SMT-NRA integrating 2D-LS, MCSAT and OpenCAD, to improve search efficiency through information exchange. The experimental results demonstrate improvements in local search performance, highlighting the effectiveness of the proposed methods.
Abstract:In this paper, we propose a hybrid framework for Satisfiability Modulo the Theory of Nonlinear Real Arithmetic (SMT-NRA for short). First, we introduce a two-dimensional cell-jump move, called \emph{2D-cell-jump}, generalizing the key operation, cell-jump, of the local search method for SMT-NRA. In contrast to the existing cell-jump, which updates assignments exclusively along a given line, the 2D-cell-jump enables search over an arbitrarily selected plane. Then, we propose an extended local search framework, named \emph{2D-LS} (following the existing local search framework, LS, for SMT-NRA). Finally, we present a hybrid framework for SMT-NRA integrating 2D-LS, MCSAT and OpenCAD, to improve search efficiency through information exchange. The experimental results demonstrate improvements in local search performance, highlighting the effectiveness of the proposed methods.
Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO); Symbolic Computation (cs.SC)
arXiv:2507.00557 [cs.AI]
(or arXiv:2507.00557v3 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2507.00557 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 219 words