Redefining AGI: The First Practical Framework and Working Demo of General Intelligence

Artificial General Intelligence (AGI) has remained largely theoretical due to vague definitions, non-measurable criteria, and architectures that cannot be implemented in practice. Existing interpretations of AGI, from cognitive theories to universal intelligence models, provide valuable insights but do not offer a concrete pathway for building or evaluating an actual general intelligence system. This paper introduces a new, measurable, and operational definition of AGI that emphasizes autonomous knowledge acquisition, reasoning across diverse and clearly defined domains, cross-domain transfer, adaptive self-improvement, and alignment with human goals. To support this definition, we propose a modular cognitive framework designed specifically for practical implementation. A working prototype is developed to demonstrate the feasibility of this approach. The system is capable of learning new knowledge, storing it in an adaptive memory, applying multi-step reasoning, transferring understanding across unrelated domains, and improving its performance through user feedback. Built using currently available technologies such as the Gemini API and structured memory mechanisms, the prototype shows that AGI can be demonstrated meaningfully even with today's tools. The paper also presents a standardized evaluation suite that measures generalization, transfer, reasoning accuracy, learning efficiency, memory retention, adaptability, and alignment stability. Together, the definition, architecture, and prototype form a complete foundation for practical AGI research and represent a significant step toward realizing general-purpose intelligence.

• AGI
• Cognitive architecture
• Evaluation
• Adaptive memory
• Alignment