Event-Centric World Modeling with Memory-Augmented Retrieval for Embodied Decision-Making

ArXi:2604.07392v2 Announce Type: replace Autonomous agents operating in dynamic and safety-critical environments require decision-making frameworks that are both computationally efficient and physically grounded. However, many existing approaches rely on end-to-end learning, which often lacks interpretability and explicit mechanisms for ensuring consistency with physical constraints. In this work, we propose an event-centric world modeling framework with memory-augmented retrieval for embodied decision-making.