The search for long lived particles, a common extension to the Standard Model, requires a sophisticated neural network design, one that is able to accurately discriminate between signal and background. At the LHC's ALTAS experiment, beam induced background (BIB) and QCD jets are the two significant sources of background. For this purpose, a recurrent neural network (RNN) with an adversary was used to distinguish long lived particles from BIB and QCD as well as control systematic errors. We are presenting the modernization of this neural network, which both succeeds in improving the functionality of the network and improving the performance of the network which will be used in future analyses.