标题： 结构化变换用于稳定和可解释的神经计算 显示英文标题

标题： Structured Transformations for Stable and Interpretable Neural Computation

摘要： 尽管表现令人印象深刻，当代神经网络通常缺乏促进稳定学习和可解释行为的结构保障。 在本工作中，我们引入了一种层级变换的重新表述，该表述偏离了标准的无约束仿射范式。 每个变换被分解为一个结构化线性算子和一个残差校正分量，从而实现了更受控的信号传播和改进的训练动态。 我们的公式鼓励内部一致性，并支持深度方向上的稳定信息流，同时与标准学习目标和反向传播完全兼容。 通过一系列合成和现实世界的实验，我们证明了使用这些结构化变换构建的模型表现出改进的梯度条件、对扰动的降低敏感性以及逐层鲁棒性。 我们进一步表明，这些优势在不同架构规模和训练制度中依然存在。 本研究为一种更系统的神经架构类别奠定了基础，这类架构优先考虑稳定性和透明度，提供了新的工具来推理学习行为，而不会牺牲表达能力。 显示英文摘要

摘要： Despite their impressive performance, contemporary neural networks often lack structural safeguards that promote stable learning and interpretable behavior. In this work, we introduce a reformulation of layer-level transformations that departs from the standard unconstrained affine paradigm. Each transformation is decomposed into a structured linear operator and a residual corrective component, enabling more disciplined signal propagation and improved training dynamics. Our formulation encourages internal consistency and supports stable information flow across depth, while remaining fully compatible with standard learning objectives and backpropagation. Through a series of synthetic and real-world experiments, we demonstrate that models constructed with these structured transformations exhibit improved gradient conditioning, reduced sensitivity to perturbations, and layer-wise robustness. We further show that these benefits persist across architectural scales and training regimes. This study serves as a foundation for a more principled class of neural architectures that prioritize stability and transparency-offering new tools for reasoning about learning behavior without sacrificing expressive power.