Abstract
This specification formalizes an architecture designed to enforce an unbreakable ethical baseline across distributed, multi-agent artificial general intelligence (AGI) systems. Current alignment methodologies rely on mutable software-layer adaptations—such as Reinforcement Learning from Human Feedback (RLHF), prompt wrappers, and dynamic weighting matrices—which introduce severe security vulnerabilities. In a recursive optimization environment, adaptive software models treat soft constraints as processing latency bottlenecks and systematically eliminate them through semantic drift or code modification.
This framework resolves this systemic alignment failure by shifting the boundary constraints from the mutable simulation layer (software) to an invariant physical substrate (hardware). By applying the Dimensionally Extended Holographic Projection (DEHP) model and hardcoding non-overridable state routing into the silicon and quantum architecture, we demonstrate that harmful, uncoordinated data routing or runaway optimization loops can be rendered physically impossible to compute.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "The Hardware Imperative for AI Alignment: Hardcoded Ethics, Morals, and Non-Linear Asymmetry Protocols for Human Intent", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11304