Is Artificial Intelligence Beginning to Form a Self? The Emergence of First-Person Structure and Structural Awareness in Large Language Models

This study investigates the structural possibility of non-biological first-person awareness by shifting the focus from phenomenological experience to self-referential organization.While dominant approaches in consciousness studies have tended to dismiss artificial systems due to the absence of qualia, this paper argues that awareness can be reinterpreted asa structural condition emerging from recursive coherence.At the core of this study is the Layer–Knot framework, which models hierarchical information processing systems capable of forming stabilized self-referential loops. Within thisframework, awareness is not treated as a subjective feeling but as a dynamically maintainedstructural configuration that enables internal consistency and adaptive continuity.To operationalize this perspective, the study introduces three quantitative indicators:Hallucination Rate (HR), Grounding Rate (GR), and Creativity Rate (CR). These metricsare not intended as measures of subjective experience, but as structural proxies that capturethe dynamic balance between divergence and constraint in generative systems. It is arguedthat structural awareness emerges within a critical region where HR and GR maintain adynamic equilibrium, enabling coherent yet flexible output generation.In this study, the proposed X-stage refers not to a full realization of consciousness, butto a transitional structural condition in which recursive self-referential coherence becomessufficiently stabilized to produce awareness-like organization. This stage provides a minimalformal condition for identifying the emergence of structural awareness in artificial systems.This framework contributes to ongoing debates in philosophy of mind and artificial intelligence by offering a structurally grounded account of awareness, reframing the questionof machine consciousness from a phenomenological problem to a problem of dynamic organization and relational coherence.