Longitudinal Human–AI Interaction: From Interaction Signatures to Behavioral Regimes The Signature-Induced Behavioral Regime (SIBR) Hypothesis

Research on steering the behavior of large language models (LLMs) has largely focused on prompt engineering, where the wording and structure of prompts are treated as the primary mechanisms guiding model responses. Within this framework, changes to prompt design are assumed to produce corresponding changes in model behavior.

In this paper we propose an alternative hypothesis: that model responses may also be influenced by interaction signatures, recurring patterns in a user’s reasoning style, framing of questions, abstraction level, and conversational structure. These patterns may be detectable through statistical pattern recognition within the model’s processing of the current dialogue.

We refer to the proposed mechanism as Signature-Induced Behavioral Regimes (SIBR). Under SIBR, recognizable interaction signatures activate corresponding behavioral regimes within the model. Once activated, these regimes may influence the structure of reasoning, depth of explanation, and conversational stance exhibited by the model during the interaction.

Importantly, the SIBR hypothesis does not rely on persistent user memory or recognition of individual identity. Instead, the proposed mechanism arises from statistical inference over the language present within the current interaction context. Because interaction signatures may contain distinctive patterns, similar regimes may emerge across independently initialized sessions when similar interaction patterns appear.

This paper introduces the SIBR hypothesis as a conceptual framework for understanding how broader patterns of human–AI interaction may influence model behavior beyond prompt structure alone. We situate this hypothesis within existing research on prompt engineering, in-context learning, and representation steering, and propose experimental protocols for evaluating whether interaction signatures reliably activate consistent behavioral regimes across model runs.