An Evolved Universal Transformer Memory

This is the primary source behind the memory-optimization link in the saved list. Sakana's Neural Attention Memory Models are interesting because they try to learn what a transformer should remember or forget rather than keeping every token equally alive.

Worth keeping, with caution. Memory savings are exciting, but production systems still need to ask what was discarded, when that is safe, and how failures show up in evaluation.