This preprint presents an exploratory analysis of temporal dynamics in long-form Human-LLM interaction. Rather than focusing on semantic content or task performance, the study examines how conversations unfold over time when represented as normalized response-magnitude trajectories. Analyzing 25 extended conversations involving a single human participant with a consistent interaction style, we observe the emergence of multiple repeatable interaction regimes characterized by distinct temporal envelopes. These regimes are robust to stochastic perturbation of response magnitude but degrade under temporal scrambling, indicating sensitivity to sequential order rather than static statistics. The work is decriptive and conditional in scope and does not claim generalization across users, models, or interaction contexts. It is intended to highlight temporal structure as an underexplored dimension of Human-LLM interaction and to provide a methodological starting point for further investigation of long-form conversational dynamics.