Enhancing Large Language Models with Top Layers for Artificial General Intelligence: Architectural Integration and Software Implementation
Year: 2025
Type: article
Abstract: Author: J. Philippe Blankert Description:This article outlines a software-level methodology for transforming Large Language Models (LLMs) into Artificial General Intelligence (AGI) systems by adding symbolic and reflexive "top layers." It introduces conceptual frameworks including S.A.G.E.-AI, META-AI, and reflexive operators, and explains how to integrate them with models such as GPT-2 or LLaMA. The paper emphasizes interpretability, ethical alignment, and modularity, and provides example pseudocode for self-correcting belief systems and modular attention-enhanced reasoning grids. While this release outlines core concepts and working code, deeper technical designs and optimized architectures are available only under NDA or consultancy agreements for commercial or safety reasons. DOI: 10.5281/zenodo.16421176Keywords: Artificial General Intelligence, AGI, Reflexive Architecture, LLMs, Cognitive Geometry, Ethics in AI, Pseudocode, Scalable AI
Author: Blankert, Jean Philippe
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949362765
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