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Our first contribution is to maximize in a local search fashion the assignment trail in CDCL, by sticking to and extending promising assignments via a technique called target phases. 我们的第一个贡献是通过一种称为目标阶段的技术来坚持和扩展有希望的分配，以局部搜索的方式最大化CDCL中的分配轨迹。 Second, we relax the CDCL framework by again extending promising branches to complete assignments while ignoring conflflicts. These assignments are then used as starting point of local search which tries to fifind improved assignments with fewer unsatisfified clauses. 其次，我们通过再次扩展有希望的分支来完成任务，同时忽略冲突，从而放松CDCL框架。然后，这些赋值被用作局部搜索的起点，试图找到不满意子句较少的改进赋值。 Third, these improved assignments are imported back to the CDCL loop where they are used to determine the value assigned to decision variables. 第三，这些改进的赋值被导入到CDCL循环中，用于确定分配给决策变量的值。 Finally, the conflflict frequency of variables in local search can be exploited during variable selection in branching heuristics of CDCL. 最后，在分支启发式算法的变量选择中，可以利用变量在局部搜索中的冲突频率。
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