Beyond Sequential Scanning: Rethinking Visual State Propagation with Neighborhood State Interaction

Visual state space models (VSSMs) provide an efficient mechanism for modeling long-range dependencies in vision tasks. However, many existing visual state space models mainly rely on sequential scanning to organize state propagation, which introduces indirect interactions between spatially adjacent locations and makes local information exchange dependent on intermediate states. In this work, we rethink visual state propagation beyond sequential scanning and introduce Neighborhood State Interaction (NSI), a novel state interaction mechanism that enables direct interactions among neighboring states during each update. Instead of propagating states along predefined scan paths, NSI performs synchronous neighborhood interactions on two-dimensional feature maps and progressively expands contextual information through multiple propagation steps. To make multi-step state interaction efficient and stable, we generate input-conditioned interaction kernels and share them across propagation steps, while using state-dependent retention to adaptively control accumulated information and incremental readout to combine representations from different propagation stages. Extensive experiments on image restoration tasks demonstrate that our approach consistently improves over SS2D, achieving PSNR gains of 0.50 dB on the GoPro dataset for deblurring, 1.15 dB on the O-HAZE dataset for dehazing, and 1.37 dB on the Haze4K dataset for dehazing, while reducing MACs by 25.1% and 30.2% for deblurring and dehazing models, respectively.