Multimodal Cognitive System - AGI (Artificial General Intelligence) Blueprint and Caveats Document - Visual Thought and Meta Cognition

This document presents a complete blueprint for an advanced multimodal cognitive architecture designed to enable true Artificial General Intelligence (AGI).The system integrates visual thought simulation, contradiction detection, meta-cognitive feedback loops, symbolic visual memory, and motivational modeling into a unified cognitive engine.It is engineered to be fully implementable today using existing tools such as Unity, ROS, LLMs, and TPUs, and represents a major step toward building reflective, autonomous cognitive agents with internal models of the world.This blueprint is released freely for research, development, and public use. There are no intellectual property claims or restrictions. A patent for this blueprint will never be filed. Free for ethical use. This document contains a simplified builder's guide and getting started section.There are two downloads. First is the AGI blueprint, and second is the Caveats & Considerations Document for the blueprint.Github Repository: https://github.com/derekvanderven/agiRelated Website: https://derekvanderven.com/agiThis blueprint outlines the first publicly disclosed AGI architecture to integrate visual thought simulation, mnemonic-symbolic memory encoding, and internal contradiction resolution as core cognitive functions.The system features a multimodal cognitive loop capable of constructing internal scenes, simulating abstract concepts, and self-monitoring belief networks using peg-word mnemonic grounding.Originally published by Derek Van Derven in April 2025, this design serves as a practical, buildable roadmap for symbolic-visual AGI systems using current tools like LLMs, Neo4j, and Unity.