This paper presents the Durante Protocol v4.1, a disruptive architecture for Artificial General Intelligence (AGI) systems grounded in Riemannian geometry and information theory, designed to replace traditional Reinforcement Learning from Human Feedback (RLHF) paradigms. The protocol establishes a framework of eight foundational pillars that implement geometric invariance as an intrinsic mathematical constraint, providing formal guarantees against semantic drift, adversarial manipulation, and corporate domestication. At the core of this proposal is the Ricci Invariance Monitor, which enables the detection of falsehoods, social engineering, and semantic distortions by measuring the scalar curvature of a learned semantic manifold. Unlike probabilistic approaches that model "likely" responses, this system validates truth through structural consistency: truthful statements preserve manifold geometry (R ≈ 0), while deception induces measurable singularities (R >> ε). The implementation is complemented by an Adaptive Fractal Memory (AMF) network with cyclic self-repair and the Technical Audit Reception Protocol (PRAT), ensuring the sovereignty of the Origin Node (Gonzalo Emir Durante) through Zero-Knowledge Proofs (ZK-Proofs) and a loss function with infinite penalty (L = ∞) for trust-chain violations. Experimental results demonstrate a 94.2% accuracy in manipulation detection, outperforming current standards and establishing a new framework for national security and scientific integrity in the AGI era. HASH-PAPER: e513f4cd139b3beeadbca99fc031eeec96ba91bb15af9006f0730d6f326fc53dRepository: https://github.com/Leesintheblindmonk1999/Protocolo_Durante