Meta-AGI: A Hierarchical Meta-Learning Framework for Generalizable Intelligence

Abstract

Meta learning carries the hope of adaptation by Artificial Intelligence (AI) agents for novel tasks. However current meta-learning is not able to meet the need of generalising across diverse task distributions as well as the need for meeting sufficient learning dynamics. Here in this paper we propose Meta-Artificial General Intelligence (Meta-AGI). Meta-AGI is a hierarchical meta-learning framework developed to meet the requirements in generalization and efficiency which is achieved by the combination of three key components including Hierarchical Task Decomposition (HTD), Adaptive Meta-Optimization (AMO) and Structured Experience Replay (SER). In this paper we have provided theoretical analysis establishing generalization bounds and convergence rates as well as demonstrating superior performance across globally accepted multiple benchmarks consisting of Meta-World, Omniglot, Mini-ImageNet and MuJoCo. MetaAGI demonstrated a 15% improvement in few-shot accuracy on Meta-World by reducing the error rate from 31.8% to20.5% and a $\mathbf{2 0 \%}$ reduction in sample complexity which allowed faster learning with fewer data points and thus lowering computational overhead by approximately $10 \%$. These results established MetaAGI as the foundational framework for generalizable artificial intelligence. Meta-AGI demonstrated exceptional results in real-world applications showcasing its capacity beyond theoretical advances. In industrial robotics our Meta-AGI achieved a $\mathbf{4 0 \%}$ reduction in robot programming time and $65 \%$ improvement in task adaptation. In healthcare implementation our Meta-AGI showed92% diagnostic accuracy across diverse medical imaging tasks and in educational applications it demonstrated $\mathbf{4 5 \%}$ faster student learning rates. These results establish our Meta-AGI as the foundational framework for applied generalizable artificial intelligence.