Poster

Position: Modular Memory is the Key to Continual Learning Agents

Vaggelis Dorovatas ⋅ Malte Schwerin ⋅ Andrew Bagdanov ⋅ Lucas Caccia ⋅ Antonio Carta ⋅ Laurent Charlin ⋅ CITEC Barbara Hammer ⋅ Tyler Hayes ⋅ Timm Hess ⋅ Christopher Kanan ⋅ Dhireesha Kudithipudi ⋅ Xialei Liu ⋅ Vincenzo Lomonaco ⋅ Jorge Mendez-Mendez ⋅ Darshan Patil ⋅ Ameya Pandurang Prabhu ⋅ Elisa Ricci ⋅ Tinne Tuytelaars ⋅ Gido M van de Ven ⋅ Liyuan Wang ⋅ Joost van de Weijer ⋅ Jonghyun Choi ⋅ Martin Mundt ⋅ Rahaf Aljundi

Abstract

Foundation models have transformed machine learning through large-scale pretraining, massive parameterization, 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, 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, thereby mitigating catastrophic forgetting and charting a practical roadmap toward continually learning agents.