Digital twins have become a central paradigm for cultural heritage documentation, monitoring, and preventive preservation. Yet, when cultural heritage systems promise prediction, simulation, intervention planning, and decision support, a more explicit account is needed of the computational commitments behind such claims. This position paper proposes the notion of the heritage world model as a conceptual and architectural abstraction that uses the semantic digital twin as its representational layer and extends it toward prediction, memory, uncertainty-aware reasoning, and intervention evaluation. We define a heritage world model as a structured, temporally updated, semantically grounded, and action-aware model of a heritage asset and its preservation environment, capable of integrating observations, estimating latent risk states, predicting plausible future trajectories, and evaluating interventions under uncertainty. The paper does not present a validated deployed system. Rather, it clarifies the architectural conditions under which a decision-support digital twin infrastructure could support the kind of world-model-like preservation system proposed here. It further argues that such a model becomes operationally meaningful only when it includes a human-supervised controller layer that maps semantic state, predicted risk trajectories, uncertainty, memory, and institutional constraints into preservation-relevant actions, alerts, monitoring adaptations, or requests for expert review. Sensor data, remote sensing, computational models, risk assessments, policies, and conservation actions are interpreted as possible observational, dynamic, and intervention layers of a heritage world model. The paper reviews adjacent work in heritage digital twins, semantic and reactive ontologies, risk-aware preservation, agentic AI, and modern AI world models, and proposes a research agenda for moving toward predictive, memory-bearing, and intervention-aware preservation intelligence.
Pavlidis et al. (Sat,) studied this question.