Research finds that AI models need sleep

Carnegie Mellon University researchers proposed a sleep-inspired method for large language models to retain information over long runs.

The system pauses external input, reprocesses stored context, and consolidates it into persistent model weights.

Experiments showed the approach helps especially on complex tasks, and repeated sleep cycles further improve results.

The work could reduce memory loss and performance decline in long-horizon AI tasks.