most "convert everything into a skill" advice stops at storage. nobody's covering what happens once the pile gets big. the notes section of my guide already runs into a small version of this problem, years of scattered vault entries that just sit there until you go looking for something specific. AgeMem tackles the harder version of that same problem, teaching the agent to actively manage what it keeps, updates, or throws out over time, not just search through a pile when asked. concretely: it gives the agent tool-based control over storing, retrieving, updating, summarizing, or discarding memory, trained across 3 stages of reinforcement learning because memory decisions produce sparse, awkward reward signals normal training can't handle. why it matters: across 5 long-horizon benchmarks, it beat setups that treat long-term and short-term memory as separate bolted-on pieces, on both context efficiency and memory quality. it's an ACL'26 highlight paper too. paper: full article below 👇