Researchers have developed EchoLSTM, a novel recurrent neural network model aimed at enhancing long-range memory retention. This model employs a technique known as Output-Conditioned Gating, which enables it to self-reflect by adjusting its memory based on previous inferences. Through this self-reflective mechanism, EchoLSTM creates a stabilizing feedback loop that improves its ability to maintain information over extended sequences. The primary function of the model is to stabilize long-range memory, addressing challenges commonly faced by traditional recurrent networks. By integrating this feedback process, EchoLSTM demonstrates improved performance in tasks requiring sustained memory. This advancement aligns with ongoing efforts in artificial intelligence to develop models capable of more reliable and stable long-term information processing. The introduction of EchoLSTM represents a significant step toward more effective recurrent architectures for sequential data analysis.