Researchers have released a software toolbox that uses neural networks to test whether observed correlations in quantum networks are Bell local (explainable by independent local variables) or truly nonlocal. The preprint (https://arxiv.org/abs/2603.24665), which has not been peer-reviewed, builds on earlier work from arXiv:1907.10552 by parameterizing local models with neural networks so that machine-learning optimization tools can efficiently search for nonlocality. The method works with arbitrary network structures, is designed to be easy to use, and includes technical upgrades that deliver significantly better performance than previous approaches. The team applied the toolbox to several previously unexplored networks, gaining fresh insights into quantum nonlocal sets and suggesting promising experimental realizations. As a purely computational study there is no participant sample size; limitations include the inherent difficulty of the problem for very large or complex networks, though specific scalability bounds are not detailed in the abstract.