Although often conflated, Digital Twins and World Models represent distinct paradigms of system reasoning. A Digital Twin is defined by a persistent, bidirectional data coupling with a specific physical asset. Conversely, a World Model captures environmental dynamics to support prediction and planning, without necessitating a direct link to a singular physical counterpart. This paper establishes a rigorous disambiguation for researchers and practitioners in the Physical AI realm. We define minimal criteria for each paradigm, compare them via a multidimensional taxonomy, and validate the framework using concrete real–world systems. Our analysis demonstrates that neither paradigm subsumes the other. Rather, they intersect within a bounded convergence zone where specific architectures integrate both. While this taxonomy is descriptive and integrative, we note that formal ontological grounding remains an avenue for future work.
Suri et al. (Wed,) studied this question.