Advancing Abstract Reasoning in Artificial General Intelligence with a Hybrid Multi-Component Architecture

Artificial General Intelligence (AGI) models face significant challenges in abstract reasoning tasks, which require deep understanding and generalization across a wide range of domains. This study introduces a novel hybrid multicomponent architecture designed to enhance AGI performance on abstract reasoning tasks. The proposed model integrates advanced techniques including Transformer-based self-attention mechanisms, Graph Neural Networks (GNNs), Generative Adversarial Networks (GANs), and Reinforcement Learning (RL). Each component addresses distinct aspects of the task: Transformers capture sequence dependencies, GNNs model complex relationships between entities, GANs generate realistic data for training, and RL optimizes reasoning strategies. The synergy of these components allows the model to achieve strong generalization and abstract learning capabilities, significantly improving performance compared to single-component models. The experimental results on the ARC-AGI benchmark demonstrate the superiority of this hybrid approach, with marked improvements in key performance metrics such as accuracy, F1 score, mean squared error (MSE), and AUC-ROC. These results confirm that the proposed model is a promising solution to overcoming the limitations of current AGI systems in tackling novel and complex reasoning tasks. Future work will focus on further optimization of the model components, expanding its scope to more complex tasks, and evaluating its performance in practical applications such as natural language understanding and robotics.