Situation Perception: Road to AGI

Abstract

Current large language models are extraordinary statistical engines, but pattern mastery is not the same as general intelligence. This paper argues that AGI depends on situation perception: the ability to construct, revise, and act within internal simulations of possible worlds across latent time.

The argument centers on three components: abstract prediction, long-term compressed memory, and active learning guided by objectives. It also proposes evaluation tests and explores the economic, social, and philosophical consequences of machines that can simulate futures, pursue goals, and judge their creators.

Table of Contents

1. Introduction
2. A Working Definition
3. What AGI Needs
4. Related Work
5. Methodology
6. Analysis
7. The Creator Problem
8. Economic Implications
9. Results, Risks, and Open Questions
10. Conclusion

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