Beyond Static Locality: Adaptive State Space Modeling for 3D Point Clouds

State space models (SSMs) have recently been explored as an efficient framework for point cloud representation learning. However, existing point-cloud SSMs typically treat locality as a fixed architectural assumption, limiting their ability to adapt local modeling to diverse geometric structures and hierarchical feature representations. We propose AdaLoMamba3D, a locality-adaptive state-space framework that models locality as an adaptive property determined by learned representations. First, local interaction ranges are adapted according to representation hierarchy, enabling different representation levels to employ distinct neighborhood ranges. Center-neighbor relations are further normalized based on local feature statistics in a channel-wise manner, enabling different regions to obtain adaptive normalization references and reducing the bias introduced by uniform normalization. The resulting representations are propagated through bidirectional state-space modeling, where information exchange between complementary branches is conditioned on current token states. Together, these designs form a unified framework for adaptive locality. Experimental results show competitive and consistent performance across classification, few-shot recognition, and part segmentation.