  The data-taking conditions expected in Run 3 of the LHCb experiment at CERN
are unprecedented and challenging for the software and computing systems.
Despite that, the LHCb collaboration pioneers the use of a software-only
trigger system to cope with the increased event rate efficiently. The beauty
physics programme of LHCb is heavily reliant on topological triggers. These are
devoted to selecting beauty-hadron candidates inclusively, based on the
characteristic decay topology and kinematic properties expected from beauty
decays. The following proceeding describes the current progress of the Run 3
implementation of the topological triggers using Lipschitz monotonic neural
networks. This architecture offers robustness under varying detector conditions
and sensitivity to long-lived candidates, improving the possibility of
discovering New Physics at LHCb.
