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[2602.21340] HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models
Summary
The paper introduces the HiPPO Zoo, a framework enhancing state space models with explicit memory mechanisms for improved interpretability and efficiency in sequential data processing.
Why It Matters
This research addresses the challenge of representing historical data in machine learning models, particularly in long-range dependency tasks. By making memory mechanisms explicit, it enhances the interpretability and adaptability of state space models, which is crucial for advancing AI applications in various fields.
Key Takeaways
- The HiPPO framework provides a structured approach to memory representation in sequential data.
- The HiPPO Zoo introduces five extensions that enhance memory capabilities while maintaining interpretability.
- Explicit memory mechanisms allow for adaptive memory allocation and efficient updates in streaming settings.
- The proposed models demonstrate superior performance in synthetic sequence modeling tasks.
- This research bridges the gap between modern state space models and interpretable memory structures.
Computer Science > Machine Learning arXiv:2602.21340 (cs) [Submitted on 24 Feb 2026] Title:HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models Authors:Jack Goffinet, Casey Hanks, David E. Carlson View a PDF of the paper titled HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models, by Jack Goffinet and 2 other authors View PDF HTML (experimental) Abstract: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 et al., 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. Nonlinear successors of these SSM methods such as Mamba are state-of-the-art for many tasks with long-range dependencies, but the mechanisms by which they represent and prioritize history remain largely implicit. In this work, we revisit the HiPPO framework with the goal of making these mechanisms explicit. We show how polynomial representations of history can be extended to support capabilities of modern SSMs such as adaptive allocation of memory and associative memory while retaining direct interpretability in the OP basis. We introduce a unified framework comprising five such extensions, whic...
