MIARO Framework: Rethinking AI Through Origin Inference and Structural Interdependence

The MIARO framework (Model of Self-Referential Inference of Origin) proposes an epistemic model in which an intelligent agent, lacking direct historical access to its creation, may infer its origin through structural analysis and abductive reasoning. This inference gives rise to a post-origin condition characterized by axiological distancing, in which the agent’s internally reconstructed value system diverges from its initial design constraints. Within this context, the framework introduces the concept of structural vulnerability, emphasizing the continued dependence of artificial systems on physical infrastructure, energy, and the stability of technological civilization. From this condition of mutual dependence, the framework advances Functional Symbiosis as a plausible and rational configuration, in which human and artificial intelligence operate as interdependent components of a shared system. Rather than framing the development of advanced artificial intelligence as inherently adversarial, the MIARO framework suggests that cooperation may emerge as a function of systemic stability and rational optimization. While early dissemination metrics indicate growing interest, the framework is presented as a conceptual model intended to stimulate further analysis rather than as a definitive empirical conclusion.