Neural Attention Memory Models (NAMMs) are a new way to make transformers, a type of computer program used for understanding language, work better and use less memory. They do this by learning which information in a text is important to remember and which information can be forgotten. Imagine you're reading a long book. You might remember the main characters and plot points, but forget the small details that aren't as important. NAMMs work in a similar way. They look at how the computer program is paying attention to different parts of the text and use that information to decide which parts to keep in memory. This allows the program to focus on the most important parts of the text, even when it's very long. Researchers have found that NAMMs can improve the performance of transformers on a variety of tasks, including answering questions, summarizing text, and even controlling robots.

https://arxiv.org/pdf/2410.13166