Diffusion kernels capture global dependencies. We present Linear Diffusion Networks (LDNs), a novel architecture that reinterprets sequential data processing as a unified diffusion process. Our model integrates adaptive diffusion modules with localized nonlinear updates and a diffusion-inspired attention mechanism. This design enables efficient global information propagation while preserving fine-grained temporal details. LDN overcomes the limitations of conventional recurrent and transformer models by allowing full parallelization across time steps and supporting robust multi-scale temporal representations. Experiments on benchmark sequence modeling tasks demonstrate that LDN delivers competitive performance across ImageNet and GLUE tasks.

翻译：扩散核能够捕获全局依赖关系。本文提出线性扩散网络（LDN），这是一种新颖的架构，它将序列数据处理重新解释为一个统一的扩散过程。我们的模型将自适应扩散模块与局部非线性更新以及一种受扩散启发的注意力机制相结合。这种设计能够在保持细粒度时间细节的同时，实现高效的全局信息传播。LDN克服了传统循环模型和Transformer模型的局限性，允许跨时间步的完全并行化，并支持鲁棒的多尺度时间表征。在基准序列建模任务上的实验表明，LDN在ImageNet和GLUE任务上均展现出具有竞争力的性能。