Abstract
Linear recurrent neural networks (LRNNs) provide a structured approach to sequence modeling that bridges classical linear dynamical systems and modern deep learning, offering both expressive power and theoretical guarantees on stability and trainability. In recent years, multiple LRNN-based architectures have been proposed, each introducing distinct parameterizations, discretization schemes, and implementation constraints. However, existing implementations are fragmented across different software frameworks, often rely on framework-specific optimizations, and in some cases require custom CUDA kernels or lack publicly available code altogether. As a result, using, comparing, or extending LRNNs requires substantial implementation effort. To address this, we introduce $\texttt{lrnnx}$, a unified software library that implements several modern LRNN architectures under a common interface. The library exposes multiple levels of control, allowing users to work directly with core components or higher-level model abstractions. $\texttt{lrnnx}$ aims to improve accessibility, reproducibility, and extensibility of LRNN research and applications. We make our code available under a permissive MIT license.
Abstract (translated)
线性递归神经网络（LRNN）提供了一种结构化的方法来建模序列，它连接了经典的线性动态系统和现代深度学习技术，既具有表达能力又能保证稳定性和可训练性的理论保障。近年来，提出了多种基于LRNN的架构，每个架构都引入了不同的参数设置、离散化方案以及实现约束条件。然而，现有的实现方式分散在不同的软件框架中，常常依赖于特定框架的优化，并且有些需要定制CUDA内核或根本没有公开可用的代码。因此，使用、比较或扩展LRNN都需要大量的实现工作。 为解决这一问题，我们引入了$\texttt{lrnnx}$，这是一个统一的软件库，实现了几种现代的LRNN架构，在一个通用接口下进行操作。该库暴露了多个级别的控制，使用户可以直接与核心组件或更高层次的模型抽象进行交互。$\texttt{lrnnx}$旨在提高LRNN研究和应用的可访问性、重现性和扩展性。我们以宽松的MIT许可证提供代码。 --- 这段文本主要介绍了线性递归神经网络（LRNN）及其在现代深度学习中的重要地位，同时指出了当前实现这些模型时面临的一些挑战，并提出了一个名为$\texttt{lrnnx}$的新软件库来解决这些问题，通过统一接口和多层级控制提高了研究的可访问性和重现性。
URL
https://arxiv.org/abs/2602.08810
