In the late 1990s, German researchers Hochreiter and Schmidhuber proposed the concept of Long Short-Term Memory (LSTM), which helps Recurrent Neural Networks (RNNs) retain information over extended sequences, rather than just between consecutive time steps. For example, a character’s name introduced at the beginning of a paragraph may be necessary to understand the context at the end. An LSTM-based RNN shares the same high-level architecture as a basic RNN (whether simple, bidirectional, or deep), but replaces standard activation functions with specialized LSTM cells.