On the Importance of Gating: Memorization vs. In-Context Learning in State Space Models

Researchers have identified that state space models often struggle to match the performance of Transformers because they lack the complex gating mechanisms required for effective in-context learning. While these models offer superior computational efficiency through linear dynamics, their simplified architecture frequently results in inferior data memorization. By analyzing these structural limitations, the study clarifies why current state space models fall behind on specific sequence modeling tasks. This insight provides a clearer path for developing more capable alternatives to traditional Transformer architectures.