State Space Models (SSMs) have emerged as a promising alternative to computationally expensive attention-based models for sequence modeling. However, the effective memory of traditional SSMs is limited, requiring larger state sizes for improved recall. This paper introduces a multi-resolution SSM framework that addresses these limitations by representing sequence dynamics across multiple levels of detail. This approach captures both fine-grained, high-frequency patterns and broader, low-frequency trends, hence effectively capturing historical patterns at multiple scales. Our multi-resolution SSM demonstrates superior performance in various sequence modeling tasks, particularly in domains where multi-resolution patterns naturally occur, such as time series analysis and image processing.