Answers are grounded in this paper and cite their sources.
Theoretical framework reveals that increasing processing velocity without structural constraints causes model collapse in AI, indicating the primacy of dynamic constraint fields.
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.
No takes yet. Share an insight, caveat, or question.
Titan G. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: