The data-taking conditions expected of Run 3 pose unprecedented challenges for the DAQ systems of the LHCb experiment at the LHC. The LHCb collaboration is pioneering the adoption of a fully-software trigger to cope with the expected increase in luminosity and, thus, event rate. The upgraded trigger system has required advances in the use of hardware architectures, software and algorithms. Among the last, the LHCb collaboration can be quoted for using Lipschitz monotonic neural networks for the first time. These are particularly appealing, owing to their robustness under varying detector conditions and sensitivity to highly displaced, high-momentum beauty candidates. An overview of the applications of such architectures within the LHCb trigger system is presented. Emphasis is placed on the topological triggers, devoted to selecting b-hadron candidates inclusively by exploiting the kinematics and decay topology characteristic of beauty decays.
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