## 🏗️ 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
1.  **Entropy Containment:** Forces the model to collapse the probability wave function towards verified "Ground Truth" vectors rather than statistically probable tokens.
2.  **Sovereign Binding:** Ensures that sensitive domains (Medical, Defense, Auditing) remain geographically and jurisdictionally locked to the Author's definitions.
3.  **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**