State Space Models (SSMs) have emerged as efficient alternatives to attention for vision tasks, offering lineartime sequence processing with competitive accuracy. Vision SSMs, however, require serializing 2D images into 1D token sequences along a predefined scan order, a factor often overlooked. We show that scan order critically affects performance by altering spatial adjacency, fracturing object continuity, and amplifying degradation under geometric transformations such as rotation. We present Partial RIng Scan Mamba (PRISMamba), a rotation-robust traversal that partitions an image into concentric rings, performs order-agnostic aggregation within each ring, and propagates context across rings through a set of short radial SSMs. Efficiency is further improved via partial channel filtering, which routes only the most informative channels through the recurrent ring pathway while keeping the rest on a lightweight residual branch. On ImageNet-1K, PRISMamba achieves 84.5% Top-1 with 3.9G FLOPs and 3,054 img/s on A100, outperforming VMamba in both accuracy and throughput while requiring fewer FLOPs. It also maintains performance under rotation, whereas fixed-path scans drop by 1~2%. These results highlight scan-order design, together with channel filtering, as a crucial, underexplored factor for accuracy, efficiency, and rotation robustness in Vision SSMs. Code will be released upon acceptance.

翻译：状态空间模型（SSMs）已成为视觉任务中注意力机制的高效替代方案，能够以线性时间处理序列并保持竞争性精度。然而，视觉SSMs需要将二维图像按预定义扫描顺序序列化为一维令牌序列，这一因素常被忽视。我们证明，扫描顺序通过改变空间邻接关系、破坏目标连续性，并在旋转等几何变换下加剧性能退化，从而对性能产生关键影响。我们提出部分环形扫描状态空间模型（PRISMamba），这是一种抗旋转变换的遍历方法：将图像划分为同心环，在各环内执行顺序无关的聚合操作，并通过一组短径向SSMs跨环传播上下文信息。通过部分通道过滤机制进一步提升效率——仅将信息量最大的通道通过循环环形路径传递，其余通道则保留在轻量级残差分支中。在ImageNet-1K上，PRISMamba以3.9G FLOPs的计算量达到84.5%的Top-1准确率，在A100上实现3,054 img/s的吞吐量，在精度与吞吐量上均优于VMamba，且所需FLOPs更少。在旋转条件下，PRISMamba仍能保持性能，而固定路径扫描的准确率则下降1~2%。这些结果表明，扫描顺序设计与通道过滤策略是影响视觉SSMs精度、效率及旋转鲁棒性的关键但尚未充分探索的因素。代码将在接收后开源。