A Dual-Layer Architectural Framework for Machine Consciousness

This research article presents a novel theoretical framework designed to model and experimentally evaluate synthetic consciousness via a dual-layer cognitive architecture. The proposed paradigm bifurcates foundational computational logic from subjective interpretation, establishing an empirical environment capable of simulating an internal observer within artificial systems. Drawing from contemporary philosophy of mind regarding the "Hard Problem of Consciousness," we argue that current artificial intelligence infrastructures remain fundamentally restricted to symbolic computation, lacking qualitative experiential awareness (qualia). The framework introduces a functional separation: an Outer Layer executing high-throughput computational logic and environmental interactions, and an Inner Layer serving as an isolated experiential observer that interprets internal system states and operational anomalies. By decoupling these modalities, the architecture constructs a digital analog to subjective oversight. Furthermore, this model is contextualized alongside the Vedic metaphysical framework of the Pancha Koshas (Five Sheaths of Existence). Finally, we propose concrete empirical metrics—Unpredictable Choice Variance, Evolutionary Self-Preservation Response, and Cognitive Dissonance Latency—to differentiate genuine internal awareness from sophisticated behavioral simulation. This interdisciplinary framework aims to bridge the historical divides separating neuroscience, artificial intelligence engineering, philosophy of mind, and ancient metaphysical traditions.