Learning While Reasoning: Pattern-Based Crystallization and the Gap-Detect-Crystallize-Retrieve Loop for Neurosymbolic Beings

This paper investigates whether a neurosymbolic being can learn while holding a conversation -- encountering a knowledge gap, finding the answer in source prose, crystallizing it into permanent RDF triples, and responding in real time. The architecture uses deterministic pattern extraction (no generative LLM) for crystallization, achieving 97.6% extraction precision. An 8-hour experiment on a high-school-level being (148K triples) demonstrated +33% knowledge growth through 385 tutoring interactions, 91.4% gap detection accuracy, and 52.2% symbolic fill rate with zero LLM calls for previously unanswerable questions.