Modern neural networks obtain information about the problem and calculate the output solely from the input values. We argue that it is not always optimal, and the network's performance can be significantly improved by augmenting it with a query mechanism that allows the network at run time to make several solution trials and get feedback on the loss value on each trial. To demonstrate the capabilities of the query mechanism, we formulate an unsupervised (not depending on labels) loss function for Boolean Satisfiability Problem (SAT) and theoretically show that it allows the network to extract rich information about the problem. We then propose a neural SAT solver with a query mechanism called QuerySAT and show that it outperforms the neural baseline on a wide range of SAT tasks.

翻译：现代神经网络获取关于问题的信息, 并仅从输入值中计算输出。 我们争论说, 它并不总是最理想的, 网络的性能可以通过一个查询机制得到显著改善, 该查询机制允许网络运行时进行数项解决方案测试, 并获得关于每次测试损失价值的反馈。 为了显示查询机制的能力, 我们为 Boolean 满足性问题( SAT) 设计了一个不受监督( 不取决于标签) 的损失函数, 并在理论上显示它允许网络提取有关该问题的丰富信息。 我们然后提出一个神经SAT 解析器, 其查询机制叫做 QuerySAT, 并显示它超过了一系列SAT 任务的神经基线 。