A Survey on State Space Models
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A comprehensive survey of modern SSMs with a principles-first walkthrough, taxonomy of existing variants, and experimental comparison across NLP, vision, graph, multimodal, point-cloud, event-stream, and time-series tasks.
Principles first: The survey introduces the core SSM recurrence, discretization, and parallel-scan tricks up front so readers can reason about why variants like S4, Mamba, and H3 differ.
Broad variant coverage: Catalogs architectural and parameterization choices across major SSM families, highlighting which choices matter for which modalities.
Cross-domain applications: Demonstrates how SSMs have been applied beyond language - vision backbones, graph learning, multimodal fusion, and long time-series modeling - with comparative results.
Open challenges: Identifies theoretical limits (e.g. state capacity), scaling behavior, hybridization with attention, and hardware-efficient training as the main frontiers, alongside a live GitHub tracker.
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