提出可解释性框架，让视觉状态空间模型的决策过程变透明。
X-VMamba: Explainable Vision Mamba
- 基于控制性构建分析框架，通过前向传播量化输入对状态的影响。
- 在三种医学影像上验证，发现模型逐层从纹理到关键病灶的特征提炼。
- 无需修改模型，单次前向计算，适用于各类视觉状态空间模型。
状态空间模型（SSM）特别是Mamba架构，近年来作为序列建模的有力替代方案出现，具备线性计算复杂度并达到与Transformer相当的性能。然而，由于缺乏类似注意力的透明机制，理解这些视觉SSM如何处理空间信息仍具挑战。为此，我们提出一种基于可控性的可解释性框架，量化输入序列（标记或图像块）对SSM内部状态动态的影响。我们提出两种互补方法：一种适用于任意SSM的雅可比方法，通过完整状态传播链测量影响；另一种针对对角线SSM的格拉米安方法，利用闭式解析解实现更高速度。两者均在单次前向传播中完成，保持线性复杂度，无需结构修改或超参数调优。我们在三种不同医学影像模态上验证该框架，结果表明SSM自然实现了从早期层的粗略低级纹理到深层聚焦临床意义模式的层级特征优化。分析揭示了与诊断标准一致的领域特定可控性特征、网络层次中的渐进空间选择性，以及扫描策略对注意力模式的显著影响。该框架不仅适用于医学影像，还可拓展至计算机视觉、自然语言处理及跨域任务。我们确立了可控性分析作为所有领域SSM的统一基础可解释性范式。代码与分析工具将在发表后公开。
原文摘要 · Abstract (English)
State Space Models (SSMs), particularly the Mamba architecture, have recently emerged as powerful alternatives to Transformers for sequence modeling, offering linear computational complexity while achieving competitive performance. Yet, despite their effectiveness, understanding how these Vision SSMs process spatial information remains challenging due to the lack of transparent, attention-like mechanisms. To address this gap, we introduce a controllability-based interpretability framework that quantifies how different parts of the input sequence (tokens or patches) influence the internal state dynamics of SSMs. We propose two complementary formulations: a Jacobian-based method applicable to any SSM architecture that measures influence through the full chain of state propagation, and a Gramian-based approach for diagonal SSMs that achieves superior speed through closed-form analytical solutions. Both methods operate in a single forward pass with linear complexity, requiring no architectural modifications or hyperparameter tuning. We validate our framework through experiments on three diverse medical imaging modalities, demonstrating that SSMs naturally implement hierarchical feature refinement from diffuse low-level textures in early layers to focused, clinically meaningful patterns in deeper layers. Our analysis reveals domain-specific controllability signatures aligned with diagnostic criteria, progressive spatial selectivity across the network hierarchy, and the substantial influence of scanning strategies on attention patterns. Beyond medical imaging, we articulate applications spanning computer vision, natural language processing, and cross-domain tasks. Our framework establishes controllability analysis as a unified, foundational interpretability paradigm for SSMs across all domains. Code and analysis tools will be made available upon publication
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