PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
June 2, 20260 citationsOpen Access

Gold-Standard AGI: Inner AGI Superalignment

View Full Paper
ATAaron Turner

Key Points

  • The aim is to address the inner alignment problem for superintelligent AGI entities.
  • Introduces a novel cognitive architecture for superintelligent AGI.
  • Develops a corresponding construction sequence for implementing the architecture.
  • Adopts a pedagogic style to enhance accessibility for policymakers.
  • Presents a foundational theory addressing inner AGI alignment issues.
  • Offers an implementation-neutral solution tailored for superintelligent AGI scenarios.

Abstract

An earlier paper, Gold-Standard AGI: Outer AGI 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 FGG that correctly states what we want? ), and inner alignment (how do we build an agent G that forever pursues FGG 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 for superintelligent G, including a novel cognitive architecture, and corresponding construction sequence, for superintelligent AGI. Following the example of the earlier paper, we adopt a pedagogic style throughout, in order that the paper might be accessible to less technical readers such as AGI policymakers.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Aaron Turner (2026) studied this question.

synapsesocial.com/papers/6a1e72cb30b38c64201b5fachttps://doi.org/10.5281/zenodo.20476572
Ask AI
Helpful
Bookmark
Share
View Full Paper