Semantic Field of View: Predictive Memory Loading for Neurosymbolic Conversational AI

This paper presents Semantic Field of View (SFoV), a predictive memory loading mechanism for neurosymbolic AI systems inspired by level-of-detail rendering in 3D video games. Just as games pre-load terrain in the player's direction of travel, SFoV pre-loads knowledge along the conversation's semantic trajectory. The architecture implements topic temperature tracking, graph-based trajectory prediction, parallel background preparation, and WHY-list generation for curiosity-driven question tracking. Validation demonstrates 76.2% next-topic prediction accuracy and 42.8% automatic question answering rate from the knowledge graph.