  Structured State Space Models (SSMs) have emerged as compelling alternatives
to Transformer architectures, offering linear-time complexity and superior
performance in various sequence modeling tasks. Despite their advantages, SSMs
like the original Mamba-2 face training difficulties due to the sensitivities
introduced by the extended series of recurrent matrix multiplications. In this
paper, we propose an advanced architecture that mitigates these challenges by
decomposing A-multiplications into multiple groups and optimizing positional
encoding through Grouped Finite Impulse Response (FIR) filtering. This new
structure, denoted as Grouped FIR-enhanced SSM (GFSSM), employs semiseparable
matrices for efficient computation. Furthermore, inspired by the "attention
sink" phenomenon identified in streaming language models, we incorporate a
similar mechanism to enhance the stability and performance of our model over
extended sequences. Our approach further bridges the gap between SSMs and
Transformer architectures, offering a viable path forward for scalable and
high-performing sequence modeling.
