lrnnx：线性递归神经网络的库
$\texttt{lrnnx}$: A library for Linear RNNs
AI总结：
lrnnx是一个统一的软件库，旨在提高线性递归神经网络的可访问性、可重复性和可扩展性，通过提供统一接口和多层次控制实现多种现代LRNN架构。
摘要：
线性递归神经网络（LRNNs）提供了一种结构化的方法来建模序列，它在经典线性动态系统和现代深度学习之间架起桥梁，兼具表现力和理论保证，如稳定性与可训练性。近年来，多个基于LRNN的架构已被提出，每种都引入了不同的参数化方式、离散化方案和实现约束。然而，现有的实现分散在不同的软件框架中，往往依赖于框架特定的优化，甚至在某些情况下需要自定义CUDA内核或缺乏公开可用的代码。因此，使用、比较或扩展LRNNs需要大量的实现工作。为此，我们引入了lrnnx，一个统一的软件库，它在统一的接口下实现了几种现代的LRNN架构。该库暴露了多个层次的控制，允许用户直接与核心组件或更高层次的模型抽象进行交互。lrnnx旨在提高LRNN研究和应用的可访问性、可重复性和可扩展性。我们以宽松的MIT许可证发布我们的代码。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
