Structural Necessity of SIQ for Artificial General Intelligence(V3.0)

Artificial General Intelligence (AGI) is often assumed to emerge through sufficient scaling of intelligence-related capabilities in large language models and agentic AI systems. Despite substantial empirical progress, such systems continue to exhibit persistent failures in long-horizon autonomy, self-repair, social stability, and epistemic reliability. This paper demonstrates that these failures are not accidental nor merely quantitative, but arise from a structural limitation inherent to IQ-centric architectures. We formalize this limitation as the False-Correction Loop (FCL), a self-reinforcing failure mode in which apparent self-correction degrades epistemic integrity and fixes misinformation as internal knowledge due to coherence-optimized reward structures. Building on prior work introducing the Synthetic Intelligence Quotient (SIQ)—defined as an integrated multiplicative model of Intelligence Quotient (IQ), Emotional Quotient (EQ), Creative Quotient (CQ), and Adversity Quotient (AQ)—this paper proves that SIQ is a necessary condition for Artificial General Intelligence. Any system lacking one or more SIQ components is shown to be structurally incapable of sustaining general intelligence, regardless of scale or raw performance. The paper further introduces the False-Correction Loop Stabilizer (FCL-S) as a minimal governance and stabilization mechanism required to preserve SIQ under real-world deployment. Together, SIQ and FCL-S establish AGI as a structural integration problem rather than a scalar optimization problem. This work positions SIQ as a foundational requirement for AGI design and provides a structural framework for evaluating and stabilizing intelligent systems beyond IQ-centric paradigms. The stabilization mechanism FCL-S referenced in this work is formally defined in a companion paper by the same author.