LoopMamba: Rethinking State Space Models from an Energy Perspective

As a powerful alternative to Transformers, State Space Models (SSMs) have demonstrated remarkable effectiveness across a wide range of vision tasks. However, existing visual SSM architectures still rely substantially on empirically motivated design choices to adapt SSMs to visual tasks, with limited theoretical understanding of their underlying state transition mechanisms to guide architectural design. In this work, we rethink SSMs from an energy perspective, revealing that state transitions can be viewed as an iterative energy minimization process in which hidden states progressively move toward stable representations. Guided by this insight, we propose LoopMamba, a visual architecture with four Self-loop Layers that performs iterative hidden state refinement through Variable-Parameter State Transition. Extensive experiments and analyses on image classification, object detection, instance segmentation, and semantic segmentation demonstrate the effectiveness of LoopMamba. On ImageNet-1K, LoopMamba achieves a 1.8% improvement in Top-1 accuracy over Vim-S with 31.5% fewer parameters. Overall, LoopMamba offers an effective and interpretable paradigm for SSM-based visual representation learning.