Reality as Substrate: Observer-Coupling and the Foundations of Persistent Adaptive Systems

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Authors: Misty Richards
Cited by: 4
References: 5

Abstract

Persistent adaptive systems are commonly designed under an implicit assumption of shared objective reality, where coherence verification, governance constraints, boundary definitions, and transformation traceability operate on stable external referents. This paper argues that this assumption is architecturally unsound. No adaptive system accesses reality directly; instead, each system operates on a projection generated through coupling between an observer and an underlying substrate. Treating projection as substrate produces systematic reasoning and governance failures with traceable downstream consequences. This work formalizes Observer-Mediated Reality (I₂) as the epistemological substrate of the Unified Resonance Research Program (URRP) and introduces the Substrate Dependency Test (SDT) as a method for distinguishing substrate-level invariants from peer-level structures. It further presents the Infrastructure Misclassification Theorem (IMT), demonstrating how hidden structural dependencies become invisible failure points when treated as optional implementation details. Using a live multi-agent ontology audit as an empirical demonstration, this paper shows that coherence, governance, boundary formation, transformation traceability, and load asymmetry all depend on observer-coupled access to reality. The primary result is a revised three-tier ontology in which Observer-Mediated Reality functions as substrate rather than peer-level invariant. This reclassification provides the formal foundation required for coherent governance of persistent adaptive systems. Keywords: observer-coupling; observer-mediated reality; persistent adaptive systems; epistemology; AI governance; multi-agent reasoning; ontology; systems architecture

Fields of Study & Subjects

Systems Engineering Methodologies and Applications Advanced Software Engineering Methodologies Modular Robots and Swarm Intelligence

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
22 June 2026
Language
EN
Document type
Preprint