Listening as Intervention: Tonal Calibration as Stabilizing Substrate in Conversational AI Systems

This paper introduces a falsifiable experimental protocol for evaluating restraint as a design variable in conversational AI systems. We test whether a passive observational pre-prompt reduces narrative amplification compared to default model behavior. Success is defined as non-amplification rather than resolution and is operationalized through measurable indicators including response length, interpretive additions, absolutist language frequency, and turn count to natural stopping. The framework extends substrate-based interaction theory and positions calibrated presence as a stabilizing interaction primitive. Ethical boundaries exclude therapeutic and crisis contexts. Results from this protocol would inform AI safety, interaction design, and professional conversational practice.