ORÓMA: An Offline-First Persistent Episodic Memory Architecture for Edge Cognitive Agents

ORÓMA (Offline-Realtime-Organic-Memory-AI) is an offline-first adaptive edge intelligence architecture designed for persistent operation on resource-constrained hardware. In contrast to cloud-centered, scale-driven AI systems, ORÓMA emphasizes local autonomy, persistent episodic memory, replay-based consolidation, and continual adaptation under real-world runtime constraints. The architecture is centered around Snap- and SnapChain-based episodic structures, a Day/Dream execution model, replay-driven consolidation, and binding-oriented mechanisms for relating events, contexts, and multimodal signals across time. This publication documents the conceptual structure, core mechanisms, and implementation direction of ORÓMA as prior art and as a foundation for future technical and scientific work.