Gold-Standard AGI: 2: Inner ASI Superalignment
Aaron Turner · Zenodo (CERN European Organization for Nuclear Research) · 2026
An earlier paper, \emph{Gold-Standard AGI: Outer ASI Superalignment}, introduced the concept of Gold-Standard AGI; that is, AGI (Artificial General Intelligence) that is both maximally-aligned and maximally-validated. The first of these properties --- alignment --- is traditionally decomposed into outer alignment (how do we define a final goal $\mathbf{FG}_G$ that correctly states what we want?), and inner alignment (how do we build an agent $G$ that forever pursues $\mathbf{FG}_G$ as intended?) The earlier paper presented a complete, foundational, and self-contained theory of AGI, culminating in an implementation-neutral solution to the outer AGI alignment problem in the case that $G$ is superintelligent (hence "superalignment"). Following on from the earlier paper, the present paper presents a solution to the inner AGI alignment problem in the case that $G$ is superintelligent, including a novel cognitive architecture, and corresponding construction sequence, for superintelligent AGI. Following the example of the earlier paper, we adopt a pedagogic style, in order that the paper might be accessible to less technical readers such as AGI policymakers.