Dimensional Relativity: Meaning Preservation Across Representational Dimensions

This preprint introduces Dimensional Relativity (DR), a theoretical framework for studying how meaning survives, distorts, or breaks when representations move across observer frames, domains, tasks, and levels of abstraction. The central claim is that meaning is not preserved by surface similarity alone, but by the recoverability of task-relevant relations across dimensional transformation. DR proposes that projection precedes observation: an observer must first inhabit or apply a dimensional relation before a phenomenon can appear as meaningful, measurable, actionable, or true within a given frame. The paper develops a provisional vocabulary for manifolds, dimensional relations, projection, observation, folded and unfolded representations, semantic curvature, stability, and break conditions. The paper frames semantic curvature as a family of distortion modes rather than a single universal scalar, and provides a worked example using subway maps to show how the same representation can remain stable in one target frame while becoming unstable or broken in another. It also distinguishes DR from linguistic relativity, Active Inference, Geometric Deep Learning, phenomenology, physical relativity, relational quantum mechanics, and social constructivism. The purpose of this preprint is not to claim that DR is already experimentally established. It defines a theoretical map, formal vocabulary, worked example, limitations, and falsifiable predictions for future research into meaning preservation across representational dimensions.