1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LO2025
Breaking Symmetries in Quantified Graph Search: A Comparative Study
Mikoláš Janota, Markus Kirchweger, Tomáš Peitl +1
Graph generation and enumeration problems often require handling equivalent graphs -- those that differ only in vertex labeling. We study how to extend SAT Modulo Symmetries (SMS),…
cs.AI2025★ 1 cited
Extracting Problem Structure with LLMs for Optimized SAT Local Search
André Schidler, Stefan Szeider
Local search preprocessing makes Conflict-Driven Clause Learning (CDCL) solvers faster by providing high-quality starting points and modern SAT solvers have incorporated this techn…
cs.AI2025
Smart Cubing for Graph Search: A Comparative Study
Markus Kirchweger, Hai Xia, Tomáš Peitl +1
Parallel solving via cube-and-conquer is a key method for scaling SAT solvers to hard instances. While cube-and-conquer has proven successful for pure SAT problems, notably the Pyt…