The idea in plain language.
Use a structured dynamical system to carry information across many sequence steps.
How it works
A state-space model updates an internal state as inputs arrive and reads an output from that state. S4 introduces a structured parameterization that makes the resulting computations more efficient while preserving the ability to model long dependencies. The paper evaluates this approach across sequence benchmarks, exploring an alternative to relying entirely on attention or conventional recurrent units.
What to keep in mind
Performance depends on the parameterization and task. Long-range benchmark success is not the same as unlimited memory or perfect retrieval.
Source: Efficiently Modeling Long Sequences with Structured State Spaces. The original manuscript contains the methods, experiments, figures, and references. An arXiv posting date may follow an earlier conference publication. Read the linked record for version history.