Bridging Transformers and State-Space Models for Enhanced AI Generalization

Recent advancements in artificial intelligence have led to the development of two powerful paradigms: Transformers and State-Space Models (SSMs). While Transformers have dominated natural language processing due to their self-attention mechanisms and parallel processing capabilities, SSMs offer a more interpretable framework for modeling sequential data through linear dynamical systems. This exploration investigates the intersection of these two approaches, focusing on how hybrid models can leverage the strengths of both to achieve improved performance and generalization in complex AI tasks. By examining recent research and experiments that merge these paradigms, this exploration aims to uncover new pathways for building more robust and efficient AI systems.