The Illusion of Intelligence: Evaluating Large Language Models Against Grounded Criteria of Artificial General Intelligence

As large language models (LLMs) become central to AI applications, their perceived intelligence often masks critical limitations. While LLMs demonstrate fluent language use and problem-solving, they fundamentally lack context-awareness, self-reflection, and the ability to act under constraints. This paper identifies a core issue: current LLMs produce seemingly intelligent outputs without possessing the internal mechanisms that constitute true intelligence. They fail to recognize or address their own limitations—such as hallucinations, inefficiency, and lack of common sense—and have not autonomously developed tools to enhance their performance. To address this gap, we propose a novel three-step benchmark for Artificial General Intelligence (AGI): Audit, Generate, Implement (AGI). This framework evaluates whether an AI system can autonomously assess its own failures, generate alternative strategies, and implement optimal solutions—all within fixed resource constraints. This approach reflects the way humans solve problems efficiently and adaptively, beyond mere pattern recognition. Our findings show that scaling models alone is insufficient for AGI. We emphasize that genuine intelligence requires meta-cognition, resource management, and tool creation—traits absent in current LLMs. This work offers a new direction and evaluative standard for future AI research, emphasizing cognitive depth over superficial linguistic performance.