Three-Layer Interpretive Control Architecture for Generative Language Models

This work presents a three-layer structured interpretive control architecture, designed to improve robustness, precision and semantic alignment in generative language models. Facing current challenges of instruction misinterpretation in critical environments, we propose a modular system composed of: (1) a context layer that defines the purpose and role of the model, (2) a shielded core with formal and unequivocal rules, and (3) an expected result that functions as a self-correction mechanism. Through more than 20 adversarial simulations, a 93% reduction in interpretive errors versus traditional prompts was demonstrated. This architecture does not require altering the base model nor employing external infrastructure, which makes it an applicable solution to assisted programming, education, legal writing, and assistants with high logical fidelity requirements.