Memory Over Matter: How MemAgent Redefines Long-Context Reasoning with Reinforcement Learning
TL;DR for operators MemAgent is not another “look, we made the context window enormous” paper. Thank goodness; the context-window arms race was starting to look like cloud billing cosplay. The paper’s core move is simpler and more interesting: take a standard dense transformer, let it read a long document in chunks, and force it to maintain a fixed 1024-token working memory. After each chunk, the model overwrites that memory. At the end, it answers using the problem and the memory, not the whole document. The authors then train this behaviour with reinforcement learning, so the model learns what to retain, what to discard, and when a piece of information is merely shiny garbage. ...