Longitudinal Human–AI Interaction: From Interaction Signatures to Regime Dynamics Toward a Mechanistic Model of Formation, Stability, and Breakdown in Signature-Induced Behavioral Regimes

Recent work on Signature-Induced Behavioral Regimes (SIBR) demonstrates that recurring patterns in human–AI interaction, including reasoning structure, abstraction level, and linguistic style, can induce stable configurations of behavior within large language models (Hudson Hudson Hudson & Hudson, 2025c), this framework unifies previously observed phenomena, including continuity without memory, interaction signature detection, and stability under repeated engagement, into a single mechanistic account. The model generates testable predictions regarding regime activation speed, persistence under variation, and sensitivity to perturbation, and provides a foundation for externally applied control of model behavior without modification to underlying parameters. By shifting the focus from prompt-level effects to interaction-level dynamics, this work extends the SIBR framework from a descriptive account to a mechanistic theory of regime dynamics, offering a complementary pathway to stability and alignment grounded in structured, constraint-consistent engagement.