AI RESEARCH
HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models
arXiv CS.LG
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ArXi:2602.21340v2 Announce Type: replace Representing the past in a compressed, efficient, and informative manner is a central problem for systems trained on sequential data. The HiPPO framework, originally proposed by Gu & Dao, provides a principled approach to sequential compression by projecting signals onto orthogonal polynomial (OP) bases via structured linear ordinary differential equations. Subsequent works have embedded these dynamics in state space models (SSMs), where HiPPO structure serves as an initialization.