Long-running language agents may work better if they periodically stop to consolidate memory.

The problem is that today's transformer agents get slower and more expensive as their context grows， because attention has to keep checking more past tokens.

The usual fix for long context is to keep more tokens nearby， but that turns every next-token prediction into a larger search through the past.

The sharper idea here is that memory is not only storage.

Sometimes the hard part is converting a messy stretch of experience into a state that can actually be used later.

So the paper's idea is to add a sleep phase， where the model pauses， rereads recent context several times， writes the useful information into fixed-size memory layers， and then clears the short-term attention cache.