Triadic Relational Ontology as a Practical Constraint for Machine Consciousness Testing

Abstract

As AI systems scale, many proposed indicators of machine consciousness track capability rather than consciousness, reducing discriminative power precisely when discrimination becomes important. This paper proposes Triadic Relational Ontology (TRO) as a constraint on test design: if machine consciousness exists, it must involve a sustained triadic organization comprising (i) a temporally coherent self pole (S), (ii) a genuinely distinct world/other pole (W), and (iii) relational mediation (R) that maintains self–other differentiation under perturbation. TRO predicts qualitative collapse modes (dyadic, solipsistic, and decoupled) that do not monotonically scale with performance. We outline an implementable intervention-based protocol for transformer language models and address two conceptual risks that commonly sink such proposals: probe circularity (via counterfactual validation and cross-method convergence requirements) and S/W/R proxy bootstrapping (via staged identification with independent causal validation). Passing TRO-aligned tests is not treated as proof of consciousness; rather, TRO is proposed as a way to generate meaningful negative evidence and constrain future benchmarks in a CIMC-aligned program of discriminative testing.