Comparative Analysis of Liquid Neural Networks and LSTM for Sequential Pattern Recognition: Robustness, Efficiency, and Clinical Utility

Liquid Neural Networks (LNNs) model hidden state evolution as a continuous differential equation, addressing the limitations of discrete-time RNNs and LSTMs in capturing fluid temporal dynamics. This paper benchmarks LNNs against LSTMs across four sequential modalities, revealing LNNs' superior parameter efficiency and robustness, especially in native temporal domains and clinical environments.