Graph-Native Episodic Memory for Neurosymbolic Beings: Semantic Retrieval for Memory-Grounded Reasoning

This paper presents a graph-native architecture for episodic memory that stores operational experiences directly as typed RDF nodes in the knowledge graph, enabling semantic retrieval for memory-grounded reasoning. Unlike approaches maintaining separate episodic and semantic memory stores, this architecture treats the knowledge graph itself as the memory substrate, with experiences stored as Mistake, Success, and Learning nodes linked via semantic edges. Validation through controlled experiments with three trained beings demonstrates 85.7% mistake prevention (vs. 50% target) and 33.3 percentage point improvement in decision quality.