The Affective Regulation Layer: A Biologically Plausible Architecture for Real-Time Emotional Dynamics in Artificial Intelligence

We introduce the Affective Regulation Layer (ARL), an open-source, biologically plausibleaffective architecture designed to implement real-time emotional dynamics with neuroscientificfidelity. Traditional sentiment analysis in Large Language Models (LLMs) is limited to discrete,stateless classification, failing to capture the nuanced and persistent nature of human emotion.To address this, the ARL features a dual-process scoring engine that integrates symbolic keyworddetection with sub-symbolic semantic centroids derived from transformer-based embeddings.Central to the system is a homeostatic decay mechanism modeled after neurochemical halflives, ensuring that emotional states persist and dissipate realistically over time rather thanresetting between inputs. Furthermore, we implement biological cross-modulation logic basedon Panksepp’s affective neuroscience, allowing primary states like Joy to prime secondary drivessuch as Curiosity. This architecture provides a theoretical foundation for human-like emotionalmemory in AI systems, enabling more empathetic and context-aware machine intelligence. Wepresent the complete architectural design, mathematical formalization, and open-source implementation.