Transformer-based large language models struggle with long-horizon tasks due to their attention mechanism's poor scaling with context length. Researchers propose a sleep-like consolidation mechanism to address this issue, where the model periodically converts recent context into persistent fast weights before clearing its key-value cache. This process involves the model performing offline recurrent passes during sleep, allowing it to better retain information. The study explores the potential benefits of this mechanism in improving the performance of large language models1. By introducing a sleep-like mechanism, the models can mitigate the limitations of their attention mechanism and enhance their overall capability. This development has significant implications for the security landscape, as large language models are increasingly used in various applications. The security risks associated with these models must be carefully considered, particularly as their capabilities continue to evolve. The introduction of a sleep-like mechanism may have unintended consequences that practitioners must be aware of.