arXiv:2606. 06656v1 Announce Type: new Abstract: We study parallel Continuous Local Search (CLS) as a solution approach for Boolean satisfiability problems with symmetric pseudo-Boolean (PB) constraints.
By Cody J Christopher, Charles Gretton
arXiv:2606. 31878v1 Announce Type: cross Abstract: We investigate two approaches for extending CEGAR-tableaux with SAT-shortcuts using a previously known approach called RECAR but also a totally new approach using the modal resolution theorem prover KSP as an oracle.
By Rajeev Gor\'e (Faculty of Information Technology, Monash University, Australia), Cormac Kikkert (Cormac Kikkert Research)
arXiv:2608. 15143v1 Announce Type: new Abstract: Constraint solving is a declarative approach for solving combinatorial satisfaction and optimization problems.
By Tias Guns, Ignace Bleukx, Hendrik Bierlee, Jo Devriendt, Emilio Gamba, Orestis Lomis, Wout Piessens, Thomas Sergeys, Dimos Tsouros, Wout Vanroose, H\'el\`ene Verhaeghe
The paper presents an exact 93‑move solution to a nonlocal crystal‑exchange puzzle on an 11×11 board, where a command swaps the player with the nearest crystal in a chosen row or column. The authors prove that no shorter solution exists by performing a cost‑bounded breadth‑first search that explores over 1.8 billion states, using a compressed core abstraction and several pruning conditions. The study includes a detailed description of the reductions, pruning rules, storage scheme, and provides source code, execution logs, and a reproduction driver.
By Zilai Wang
GradSAT is a new framework that improves satisfiability solving for Quantifier-Free Floating-Point formulas by treating each SMT clause as an independent multi‑task learning problem. It uses dynamic gradient normalization (GradNorm) to balance gradient magnitudes across clauses, preventing a few difficult clauses from dominating the optimization trajectory. The approach combines a GPU‑accelerated PyTorch backend for continuous relaxation with a precise local search engine, yielding a robust, highly parallelizable solver.
By Yuanzhuo Zhang
The paper introduces the Minimum Span Antibandwidth and Cyclic Antibandwidth Labeling (MSABL/MSCABL) problems, which fix a minimum (cyclic) distance between labels of adjacent vertices and aim to minimize the overall label span. A unified Boolean Satisfiability (SAT) framework is developed, formulating the problems as a sequence of decision problems and exploiting monotonicity to accelerate search. Two SAT solving strategies—parallel and incremental—are evaluated on benchmark instances, showing that SAT-based approaches are highly competitive with commercial solvers, especially for MSCABL.