xLSTM and Naju revive LSTM-style retention and writing gates

TheTuringPost · x · 2026-07-29

This post points to an explanation of xLSTM and a Korean model called Naju, which revives a classic LSTM idea: separating the control of retention and writing.

The key idea is to use two distinct gates inside a modern state-space model compatible with parallel scans:

one gate controls how much of the existing state survives
another controls how strongly new information is written

The linked article explains xLSTM as an extended LSTM with exponential gating and matrix memory, positioning it as a modern attempt to make recurrence competitive again against Transformers. It also claims xLSTM performs especially well on time-series prediction, citing Sepp Hochreiter’s assessment that xLSTMTime compares favorably with state-of-the-art transformer-based and other recent time-series models.

Original post →

xLSTM and Naju revive LSTM-style retention and writing gates

More from Research