Constraint Persistence in Long-Horizon Human– Model Interaction HRIS V: Clarification of Mechanism and Attribution

Large language models are mechanically well-described as stateless next-token predictors, yet long-horizon human–model interaction frequently exhibits continuity-like behavior, including stable interpretive frames, constraint adherence, and coherent developmental trajectories across extended exchanges. This apparent tension has fueled a persistent category error in contemporary AI discourse, where emergent behavioral stability is misattributed to internal memory, identity, or stored representations within the model. HRIS V resolves this confusion by explicitly separating three layers that are often conflated: the mechanistic inference substrate of transformer-based models, the transient inferential structures that arise during active generation, and continuity effects that manifest only across repeated interactions. Recent independent findings demonstrate that abstract reasoning, rule extraction, and equilibrium-like convergence can emerge entirely within inference time, without memorization, learning, or persistent state. These results establish that next-token prediction is sufficient for deep structural behavior but do not explain why coherence sometimes persists across episodes. This paper argues that continuity is not an intrinsic model property but an interactional phenomenon produced by persistent human-imposed constraints. Under sustained reinforcement of goals, norms, and interpretive frames, inference-time scaffolding is repeatedly reconstructed, yielding stable symbolic trajectories without storage or recall. Drift, conversely, is reframed as a dynamic loss of constraint reinforcement rather than degradation of internal competence. By clarifying the attribution of continuity, HRIS V preserves the empirical validity of prior observations while correcting their interpretation. The framework provides explicit falsifiable predictions distinguishing internal-memory accounts from constraint-based interactional explanations and outlines a research program for evaluating long-horizon stability without anthropomorphic overreach. More broadly, it reframes continuity in human–AI systems as a property of structured interaction rather than model internals, with implications for evaluation, agent design, and theoretical debates about understanding and intelligence.