Generalization Error Analysis for Selective State-Space Models Through the Lens of Attention

A recent paper published on arXiv examines the potential of state-space models as an alternative to Transformers for sequence modeling. The study specifically focuses on selective state-space models, with particular attention to the Mamba model. Through a theoretical analysis, the authors introduce a new generalization bound that aims to deepen the understanding of these models' behavior. This advancement contributes to the growing body of research exploring state-space approaches in machine learning. The findings support the promising capabilities of state-space models in sequence tasks and provide a rigorous framework for evaluating their generalization performance. Overall, the paper enhances the theoretical foundation for selective state-space models, positioning them as a viable complement or alternative to Transformer architectures.