Consider, for example, two NN 1k realizations applied to the 𝑁 = 2 optimally mixed configuration from Fig. . As shown in Fig. (a), the cut locations based on the first realization of the NN 1k are all placed in the same white segment resulting in no mixing. For almost all realizations of the NN 1k network, similar non-mixing behavior is observed. For a small number of other realizations such as the one shown in Fig. ), the NN creates some new interfaces before getting stuck after a few steps, although that the NN was able to create any new interfaces appears to be through luck alone. The cut locations sweep from left-to-right, following the pattern in the training data. This behavior appears to indicate that the NN is using the number of interfaces, which is implicitly encoded into the vector used to represent the configuration of the domain by the number of values < 1, in addition to interface locations to determine cut locations, which does not work well for optimally mixed states outside of those that the LS method naturally generates. Other optimally mixed states elicit similar behaviors from the NN, except for configurations for which it has been trained (𝑁 = 0, 1, 2, 3, 7, 15). It is clear that the NN has learned the left-to-right pattern for a particular set of data rather than a general method. It is possible that this data-specific learning can be eliminated by creating more variety in the training dataset, either through changes in the data representation of the same configurations, or through the inclusion of other training configurations, but this is beyond the scope of this initial case study.