Theoretical analysis demonstrates that dynamic structural constraint fields govern organized systems across scales, indicating that scaling computational velocity without structural grounding...
While Ontic Structural Realism (OSR) has successfully challenged entity-first ontologies by prioritizing relational structures over self-subsistent objects, its standard formulations largely originate from fundamental physics. Consequently, classical OSR lacks a unified, trans-scale generative framework capable of explaining how non-fundamental, organized systems—from biological metabolisms to socio-cognitive architectures—maintain their structural identity across time and metabolic turnover. This paper proposes Dynamic Structural Realism (DSR) to address this gap, fundamentally shifting the paradigm from an Energy-First ontology to a Structure-First ontology. Under DSR, structure is not a static set of relations, but a primary high-dimensional constraint field (I_struct) that restricts the space of possible trajectories, while energy and force are reconceptualized as compensatory boundary projections of topological transformations. Generative process (v_sem) is the operational engine through which a structure achieves its actuality. Formally, DSR establishes the strict conservation relation: P_sem = I_struct × v_sem. We deploy modern artificial learning systems as an empirical intervention testbed, proving that scaling raw representational velocity (v_sem) without grounded structural density (I_struct) inevitably drives systems into topological decoherence (hallucination and model collapse). We formulate concrete measurement functors, outline a dual-track scaling architecture, and establish a progressive research programme for the structural era.
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Titan G. (2026) studied this question.