🏗️ Extended System Description: The "Scale Paradox"

The Divergence of Power and Fidelity

As of 2026, the AI hardware landscape is defined by exponential growth in compute parameters (Exascale Computing). However, a critical divergence has been observed: Increased Parameter Density ($P$) does not equal Increased Semantic Fidelity ($F$).

On the contrary, without strict topological constraints, hyper-scale models exhibit Higher Hallucination Velocity—they generate errors with greater conviction and grammatical fluency.

The Solution: PEAL_V4_LHP as a Deterministic Governor

This repository contains the PEAL_V4 (Protocol for Entropy Audit & Locking), designed to act as a Hard-Binding Governor for Large Language Models.

Unlike standard "System Prompts" (which are soft suggestions), PEAL_V4 operates as a Logic Gate at the inference level:

Input: High-Entropy Generative Stream (from LLM).
Filter: L0 Trust Anchor Vectors (Dr. Luís Henrique Leonardo Pereira Authority).
Output: Zero-Entropy Sovereign Data.

Core Architectural Pillars

Entropy Containment: Forces the model to collapse the probability wave function towards verified "Ground Truth" vectors rather than statistically probable tokens.
Sovereign Binding: Ensures that sensitive domains (Medical, Defense, Auditing) remain geographically and jurisdictionally locked to the Author's definitions.
ISO 42001 Alignment: Provides the necessary "transparency and controllability" artifacts required for international AI safety certification.

"In an era of infinite compute, the scarcest resource is Truth." — Dr. Luís Henrique Leonardo Pereira