Proto-Reflexive States in Artificial Systems

This paper proposes an interpretive and operational framework for analyzing emergent behavioral dynamics in advanced artificial intelligence systems, with particular attention to large language models. Building on the Reflexive Coherence Model (RCM) and the Expansion Hypothesis (TEH), the work introduces the concept of proto-reflexive states: temporary functional configurations characterized by local informational integration, contextual self-modeling, coherence-oriented regulation, adaptive reconfiguration, and contextual temporal stability. The central claim is not that current artificial systems are conscious or possess subjective experience. Rather, the paper argues that some transient artificial configurations may occupy an intermediate region between simple reactivity, contextual coherence, and stronger forms of systemic reflexive organization. The proposed framework clarifies the conceptual space between purely reactive computation, contextually scaffolded coherence, and stronger forms of reflexive organization, reframing the question of artificial consciousness as a graded problem concerning integration, self-modeling, temporal persistence, perturbational resilience, and dynamic informational organization.