In-Parameter Learning: Why Lifelong AI Systems Need More Than Longer Context

AI agents are increasingly expected to interact with changing users, tools, and environments over extended periods of time. However, current systems remain largely static: their parameters are fixed during deployment, while new experiences, domain knowledge, corrections, and behavioral adaptations are handled primarily through external memory management and in-context learning. In this position paper, we focus on lifelong AI: AI systems that can continually learn from their long-term experience throughout an extended deployment lifetime. This position paper argues that existing in-context mechanisms are insufficient for lifelong AI, and advocate In-Parameter Learning (IPL) as a necessary complement to In-Context Learning (ICL), referring to mechanisms that allow a deployed model to continually and safely update its parameters during use. More broadly, we aim to renew the community’s attention to in-parameter learning and to recalibrate the overly optimistic expectations placed on in-context learning alone. Our central claim is that future lifelong AI systems should be built around a hybrid paradigm: in-context learning for immediate, provisional, and reversible information, and in-parameter learning for durable, cumulative, and generalizable growth.