The article compares LLMs’ frozen knowledge to the amnesiac in Memento, showing how they rely on context prompts, retrieval systems, and external memory instead of updating their own weights. It reviews in-context learning and state-space memory layers, then argues that only continual learning—letting models compress new information into their parameters after deployment—can bridge the gap to genuine, scalable understanding.