<ul><li>Researchers introduce a theoretical framework called Parallel Flows for analyzing linear attention models using matrix-valued state space models (SSMs).</li><li>The approach of Parallel Flows decouples temporal dynamics from implementation constraints, allowing independent analysis of chunking, parallelization, and information aggregation.</li><li>The framework reinterprets chunking procedures as computations of the flows governing system dynamics, connecting it to mathematical tools from rough path theory.</li><li>The application of Parallel Flows to DeltaNet in a low-rank setting allows for the design of simple, streamlined generalizations with lower complexity, demonstrating the power of theoretical analysis in inspiring new computational approaches.</li></ul>

ParallelFlow: Parallelizing Linear Transformers via Flow Discretization

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