Position: Modular Memory is the Key to Continual Learning Agents

The article proposes a conceptual framework for modular memory-centric architectures that integrate In-Weight Learning (IWL) and In-Context Learning (ICL) to enhance continual learning in AI agents. It addresses the limitations of traditional IWL, particularly catastrophic forgetting, by suggesting that modular memory can facilitate rapid adaptation and knowledge accumulation while ensuring stable updates to model capabilities. This approach is significant for practitioners as it offers a roadmap for developing AI systems that can continuously learn and adapt without losing previously acquired knowledge.