General Architecture of the BaseReal-OS Hybrid Four-Layer Computational Model

Abstract

Modern artificial intelligence architectures and neuromorphic computing systems increasingly rely on fixed weights (frozen layers) for stability and execution speed. However, a frozen computational boundary imposes a fundamental architectural constraint: a system with static weights cannot, on its own, from within itself, change its own way of distinguishing categories — this requires an external input. This paper proposes a general architectural model for BaseReal-OS: a hybrid four-layer system in which three layers (geometric invariants, variety generation, consistency filter) remain fully frozen, while the fourth layer holds a dynamic, path-dependent state. The present work fixes only the general architecture and the status of the hypothesis; the specific mathematical and physical content of each layer is the subject of subsequent papers in the series.