Modular Memory is the Key to Continual Learning Agents
This addresses the challenge of building adaptive, continually learning AI agents that can operate continuously and personalize over time, though it is incremental as it builds on existing continual learning research.
The paper tackles the problem of catastrophic forgetting in continual learning by proposing a conceptual framework that combines in-weight learning and in-context learning through modular memory, aiming to enable scalable adaptation and knowledge accumulation in agents.
Foundation models have transformed machine learning through large-scale pretraining and increased test-time compute. Despite surpassing human performance in several domains, these models remain fundamentally limited in continuous operation, experience accumulation, and personalization, capabilities that are central to adaptive intelligence. While continual learning research has long targeted these goals, its historical focus on in-weight learning (IWL), i.e., updating a single model's parameters to absorb new knowledge, has rendered catastrophic forgetting a persistent challenge. Our position is that combining the strengths of In-Weight Learning (IWL) and the newly emerged capabilities of In-Context Learning (ICL) through the design of modular memory is the missing piece for continual adaptation at scale. We outline a conceptual framework for modular memory-centric architectures that leverage ICL for rapid adaptation and knowledge accumulation, and IWL for stable updates to model capabilities, charting a practical roadmap toward continually learning agents.