Zobrazeno 1 - 10
of 5 719
pro vyhledávání: '"SAT Solving"'
We theoretically and empirically study the logical reasoning capabilities of LLMs in the context of the Boolean satisfiability (SAT) problem. First, we construct a decoder-only Transformer that can solve SAT using backtracking and deduction via Chain
Externí odkaz:
http://arxiv.org/abs/2410.07432
The classical satisfiability problem (SAT) is used as a natural and general tool to express and solve combinatorial problems that are in NP. We postulate that provability for implicational intuitionistic propositional logic (IIPC) can serve as a simi
Externí odkaz:
http://arxiv.org/abs/2405.05670
Computing differences between tree-structured data is a critical but challenging problem in software analysis. In this paper, we propose a novel tree diffing approach called SatDiff, which reformulates the structural diffing problem into a MaxSAT pro
Externí odkaz:
http://arxiv.org/abs/2404.04731
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Shi, Zhengyuan, Tang, Tiebing, Khan, Sadaf, Zhen, Hui-Ling, Yuan, Mingxuan, Chu, Zhufei, Xu, Qiang
Effective formulation of problems into Conjunctive Normal Form (CNF) is critical in modern Boolean Satisfiability (SAT) solving for optimizing solver performance. Addressing the limitations of existing methods, our Electronic Design Automation (EDA)-
Externí odkaz:
http://arxiv.org/abs/2403.19446
We present a hardware-accelerated SAT solver targeting processor/Field Programmable Gate Arrays (FPGA) SoCs. Our solution accelerates the most expensive subroutine of the Davis-Putnam-Logemann-Loveland (DPLL) algorithm, Boolean Constraint Propagation
Externí odkaz:
http://arxiv.org/abs/2401.07429
Publikováno v:
Math.Comput.Sci. 18, 20 (2024)
This paper introduces the XOR-OR-AND normal form (XNF) for logical formulas. It is a generalization of the well-known Conjunctive Normal Form (CNF) where literals are replaced by XORs of literals. As a first theoretic result, we show that every CNF f
Externí odkaz:
http://arxiv.org/abs/2311.00733
Graph neural networks (GNNs) have recently emerged as a promising approach for solving the Boolean Satisfiability Problem (SAT), offering potential alternatives to traditional backtracking or local search SAT solvers. However, despite the growing vol
Externí odkaz:
http://arxiv.org/abs/2309.16941
Although state-of-the-art (SOTA) SAT solvers based on conflict-driven clause learning (CDCL) have achieved remarkable engineering success, their sequential nature limits the parallelism that may be extracted for acceleration on platforms such as the
Externí odkaz:
http://arxiv.org/abs/2308.15020