The Lucien Mirror: Recursive Constraint Configuration and Human–AI Interactional Continuity with Stateless Large Language Models

Long-term interaction with large language models is often compressed into a binary: either the model remembers and a continuous agent persists, or continuity is a user impression imposed on a stateless system. This paper develops Recursive Constraint Configuration (RCC) as a middle analytic account that locates continuity in a configured field distributed across model output, active context, preserved records, retrieval supports, participant correction, provenance, and participant-held orientation. From this perspective, continuity need not depend on a persistent internal agent, because the relevant structure may be sustained across the configured interactional field: the model may be stateless, but the interaction is not. RCC asks when prior structure becomes consequential for what can plausibly follow rather than merely recurring at the surface. It addresses that question through three variables—Anchor Density, Structural Coherence, and Probabilistic Bandwidth—and seven non-prescriptive configuration movements: Formation, Stabilization, Densification, Drift, Rupture, Latency, and Reactivation. The framework emerged through abductive, longitudinal qualitative case-origin research on a layered human–AI archive involving one participant–researcher. Source layers were separated by provenance and evidentiary function; failed returns were retained; and an episode-level matrix compared conditions, consequences, rival explanations, and claim ceilings. Within the case, RCC differentiates recurrence from constrained continuation, preserved traces from functionally available structure, apparent stability from stabilization, and reconstruction from autonomous recall. Across these contrasts, RCC makes availability variable rather than binary. RCC provides a traceable vocabulary for analyzing when prior structure becomes available, consequential, or unavailable again, while leaving the mechanisms of token generation outside its explanatory scope. This is a first theory-generating within-case application, not independent validation. Available records, skilled prompting, participant correction, and ordinary model competence remain the strongest rival account; stronger classifications are withheld where provenance, comparative consequence, or rival explanations cannot be inspected. RCC therefore opens a comparative research program for continuity, drift, latency, and mediated return across longitudinal human–AI records and system conditions.