English(EN) SAT-ACT: Learning CDCL Branching from Outcome-Derived Action Preferences

新的SAT-ACT框架改进了CDCL求解器的分支决策

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了SAT-ACT，一个旨在增强SAT求解器决策能力的新框架。该方法通过比较同一求解器状态下不同分支动作产生的后果来学习分支偏好，而不是依赖单一标量指标。该系统使用条件化网络来指导搜索过程，实验表明它降低了搜索成本并提高了在SAT竞赛基准测试上的性能。 AI

影响这项研究可能带来更高效的SAT求解器，从而影响依赖于自动推理的领域。

排序理由关于SAT求解器新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Zhenchao Sun, Chongyang Tao, Ping Lu, Tongkai Lu, Jianling Gao, Shuai Ma · 2026-11-02 05:00

SAT-ACT: Learning CDCL Branching from Outcome-Derived Action Preferences

arXiv:2610.41290v1 Announce Type: cross Abstract: Learning-based approaches have increasingly been explored for SAT solving, with particular attention to improving branching decisions in conflict-driven clause learning (CDCL) solvers. These approaches derive branching guidance ei…