Neural networks are powerful machine learning models that can learn from data and perform various tasks, such as image recognition, natural language processing, and speech synthesis. However, most neural networks are trained on fixed datasets and cannot adapt to new data inputs that change over time. This limits their applicability to real-world scenarios that involve dynamic and unpredictable data streams, such as medical diagnosis and autonomous driving. To overcome this challenge, researchers have developed a new type of neural network that learns on the job, not just during its training phase. These flexible algorithms, dubbed “liquid” networks, change their underlying equations to continuously adapt to new data inputs [1]. Liquid Neural Networks Liquid neural networks are inspired by the nervous system of a microscopic worm called C. elegans, which has only 302 neurons but can generate complex behaviors [1]. Liquid neural networks are composed of linear first-order dynamical syst