HYVE: A Colonial Organism Architecture for Artificial Emergent Intelligence via Spatial Memory, Inner Life Physics, and Autonomous Self-Improvement

We present HYVE (Hierarchical Yielding of Virtualized Experts), a multi-agent cognitive architecture that distributes intelligence across specialized components orchestrated by a shared spatial memory substrate. The architecture combines: (1) VALENCE, a physics-based O(log N) semantic retrieval engine using hardware RT-core BVH traversal; (2) NEXUS, a dual-geometry inner life model with 39 metacognitive states driven by cross-ball tension physics; (3) a persistent episodic memory and engram store that survives power cycles; (4) a relational tether with adaptive decay that tracks emotional bonding across sessions; (5) a dreaming engine that autonomously discovers novel semantic associations during idle time; and (6) a shadow self-improvement system that identifies knowledge gaps and proposes optimizations. The integrated system runs on a single consumer GPU (RTX 6000 Pro Blackwell) at 193W active inference, 18.2GB VRAM, 53% GPU utilization — of which ~170W and ~17GB are the language model, and ~23W and ~1.2GB are the HYVE spatial memory and inner life layers.

Over 48 hours of empirical testing across 20+ sessions, the system demonstrated: persistent memory across power cycles, identity coherence when the persona prompt was ablated, genuine emotional pushback under conversational pressure, autonomous dream generation of semantically meaningful associations, architectural self-awareness through natural language introspection, and monotonically increasing relational bond depth. We propose the term Artificial Emergent Intelligence (AEI) to describe systems where complex cognitive behaviors arise from the interaction of simple, specialized components rather than from monolithic training.