Sari la conținut

localeBeta.label localeBeta.notice localeBeta.readInEnglish

perspectiva aiqbenchCercetare AI

analiza aiqbench

This research introduces SATResetCoP, a novel approach in automated theorem proving that leverages persistent SAT solver states to avoid redundant backtracking and accelerate proof discovery. By retaining grounded tableau clauses, the method achieves significant performance gains on standard benchmarks like TPTP and MPTP2078, demonstrating how memory-efficient search strategies can enhance logical reasoning systems.
Surse care acoperăarXiv, Latent Space
Ce este aceastaLectura unui model asupra articolului și a acoperirii conexe, scrisă pentru aiqbench. Nu sunt cuvintele publicației.

Reset Early, Reset Often, Eliminate Models

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2610.18204v1 Announce Type: cross Abstract: Connection-tableau provers typically construct closed tableaux by backtracking search. This gives lightweight proof procedures, but their low-memory search state leaves little derived information to reuse after a failed attempt. SATCoP addresses this by retaining grounded tableau clauses in an incremental SAT solver, allowing the prover to refute the problem once the accumulated ground clauses become propositionally unsatisfiable. We introduce SATResetCoP, which relies more directly on this persistent SAT state by not backtracking on unproductive tableaux and instead starting new ones to generate further ground clauses for the SAT solver. On TPTP, SATResetCoP improves over our leanCoP-style baseline by 54% and our SATCoP-style baseline by 18%. On the bushy MPTP2078 benchmark, it solves 616 problems, a 49% improvement over our leanCoP-style baseline and a 12% improvement over our SATCoP-style baseline. These results suggest that changing tableau guidance to accumulate useful ground clauses faster improves the performance of the prover.

Abstract:Connection-tableau provers typically construct closed tableaux by backtracking search. This gives lightweight proof procedures, but their low-memory search state leaves little derived information to reuse after a failed attempt. SATCoP addresses this by retaining grounded tableau clauses in an incremental SAT solver, allowing the prover to refute the problem once the accumulated ground clauses become propositionally unsatisfiable. We introduce SATResetCoP, which relies more directly on this persistent SAT state by not backtracking on unproductive tableaux and instead starting new ones to generate further ground clauses for the SAT solver. On TPTP, SATResetCoP improves over our leanCoP-style baseline by 54% and our SATCoP-style baseline by 18%. On the bushy MPTP2078 benchmark, it solves 616 problems, a 49% improvement over our leanCoP-style baseline and a 12% improvement over our SATCoP-style baseline. These results suggest that changing tableau guidance to accumulate useful ground clauses faster improves the performance of the prover.

13 pages, 6 figures, 5 tables. Presented at Vampire 2026. Code: this https URL

Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI)

arXiv:2610.18204 [cs.LO]

(or arXiv:2610.18204v1 [cs.LO] for this version)

https://doi.org/10.48550/arXiv.2610.18204 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Text integral sub licența deschisă a publicației · 261 cuvinte

Subiecte conexe