ARCHITECTING SUPERINTELLIGENT AGENTS: A HIERARCHICAL META-LEARNING FRAMEWORK FOR CONTINUAL REASONING, MEMORY EVOLUTION, AND GOAL SELF-GENERATION FOR EMERGENT GENERAL INTELLIGENCE

In the pursuit of Artificial General Intelligence (AGI), architecting superintelligent agents remains an enduring challenge. This paper introduces a hierarchical meta-learning framework designed for continual reasoning, dynamic memory evolution, and autonomous goal generation. We present a synthesis of world modeling, introspective planning, and real-time value alignment as a foundation for emergent intelligence. Drawing from biologically inspired cognition, this architecture aims to replicate core features of flexible learning and self-improving inference. Our proposal integrates a scalable memory structure, adaptive reward modeling, and modular self-reflection, addressing key constraints in long-term agent autonomy and open-ended learning.