R-CAGE: A Structural Model for Emotion Output Design in Human-AI Interaction

TL;DR

The paper addresses emotional overload in long-term affective AI interactions by highlighting cognitive fatigue and interpretive saturation that arise from repeated emotional output. It introduces R-CAGE as a theoretical framework that restructures emotion output into four control blocks—Control of Rhythmic Expression, Architecture of Sensory Structuring, Guarding of Cognitive Framing, and Ego-Aligned Response Design—to preserve interpretive autonomy and reduce overload. By reframing emotion as a design parameter focused on rhythm, sensory modulation, interpretive openness, and ego alignment, the work provides a foundation for resilient, user-centered affective AI. The contributions establish a structured, theory-only blueprint for future implementations that prioritize psychological resilience over reactive realism in human-AI interaction.

Abstract

This paper presents R-CAGE (Rhythmic Control Architecture for Guarding Ego), a theoretical framework for restructuring emotional output in long-term human-AI interaction. While prior affective computing approaches emphasized expressiveness, immersion, and responsiveness, they often neglected the cognitive and structural consequences of repeated emotional engagement. R-CAGE instead conceptualizes emotional output not as reactive expression but as ethical design structure requiring architectural intervention. The model is grounded in experiential observations of subtle affective symptoms such as localized head tension, interpretive fixation, and emotional lag arising from prolonged interaction with affective AI systems. These indicate a mismatch between system-driven emotion and user interpretation that cannot be fully explained by biometric data or observable behavior. R-CAGE adopts a user-centered stance prioritizing psychological recovery, interpretive autonomy, and identity continuity. The framework consists of four control blocks: (1) Control of Rhythmic Expression regulates output pacing to reduce fatigue; (2) Architecture of Sensory Structuring adjusts intensity and timing of affective stimuli; (3) Guarding of Cognitive Framing reduces semantic pressure to allow flexible interpretation; (4) Ego-Aligned Response Design supports self-reference recovery during interpretive lag. By structurally regulating emotional rhythm, sensory intensity, and interpretive affordances, R-CAGE frames emotion not as performative output but as sustainable design unit. The goal is to protect users from oversaturation and cognitive overload while sustaining long-term interpretive agency in AI-mediated environments.