Vampire Bat Optimization (VBO): Autotelic Data Fusion and Semantic Drift Mitigation in Edge AI

A primary challenge in deploying continuous-learning artificial intelligence at the edge is the accumulation of "Semantic Drift"—the corruption of local databases through unstructured noise, duplicate entries, and contextual entropy over long-horizon execution loops. Standard Retrieval-Augmented Generation (RAG) models often fail in these "hot" data environments, indiscriminately ingesting data that leads to model collapse. This paper details the engineering implementation of the "Reaper/Vampire Auditor," a biomimetic heuristic algorithm modeled after the reciprocal altruism and survival strategies of Desmodus rotundus (Vampire Bats). Operating as an offline, zero-trust database deduplicator, the algorithm actively sweeps local SQLite and JSON ledgers during "night watch" idle cycles. It utilizes a Strict Sickness Protocol to quarantine hallucinatory agents and Stochastic Weight Averaging to fuse only verified, high-fidelity data into the core knowledge graph. The result is a self-cleaning, 1.58-bit ternary cognitive environment capable of maintaining absolute mathematical symmetry (1=1=1) without external cloud dependency.