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, 2026b). These regimes influence response characteristics such as coherence, reasoning depth, and conversational posture, and can re-emerge across independent sessions without reliance on persistent memory. While prior work establishes the existence of such regimes and their activation through interaction signatures, the mechanisms governing their formation, stability, and breakdown remain under-specified. This paper introduces a dynamical model of interaction regimes, conceptualizing them as attractor-like regions within the model’s conditional output space. We propose that regime formation occurs when early interaction satisfies boundary conditions that place the model’s trajectory within a stable region, consistent with prior findings on early-trajectory anchoring and human-carried continuity in stateless systems (Hudson & Hudson, 2026a). Regime stability is maintained through constraint-consistent interaction across turns, wherein aligned constraints progressively reduce output variance and reinforce coherence. Conversely, regime breakdown occurs when constraint inconsistency, ambiguity, or competing priors induce trajectory divergence, resulting in drift or transition into alternative behavioral regions. Drawing on prior work in longitudinal human–AI interaction (Hudson & Hudson, 2025a; Hudson & Hudson, 2025b; 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.