Code for "HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models" [ICML 2026]
- Authors: Jack Goffinet, Casey Hanks, David Carlson
Installation
Use a conda enviornment with JAX installed to run most of the code. The exception is the LSTM, Transformer, and S4D model runs, which we recommend running from a separate enviornment with PyTorch installed. Install the package in editable mode together with the optional development requirements for testing:
pip install -e .[dev]
Repository layout
src/mpm/
__init__.py # Re-exports the public API
integration.py # NumPy/SciPy integrator with an optional trajectory
polynomials.py # Legendre and Fourier polynomial helpers
synthetic.py # Synthetic datasets
utils.py # Shared helper functions
models.py # HiPPO Zoo models (JAX)
lstm.py # LSTM model (PyTorch)
transformer.py # Transformer model (PyTorch)
s4d.py # S4D model (PyTorch)
tests/ # Small tests
scripts/ # Experiment and figure scripts used in the paper
multiscale_hippo.py # Multiscale HiPPO figures
forecasting_hippo.py # Forecasting HiPPO figures
associative_memory_hippo.py # Associative Memory HiPPO figures
salience_hippo.py # Salience HiPPO figures
salience_hippo_schematic.py # Salience HiPPO schematic figure
volterra_hippo.py # Volterra HiPPO figure
run_arsc_experiment.py # JAX benchmark of HiPPOs on AR and SC tasks
run_arsc_experiment_torch.py # PyTorch benchmark of baselines on AR and SC tasks
Paper BibTex
@inproceedings{Goffinet2026HiPPOZoo,
author = {Goffinet, Jack and Hanks, Casey and Carlson, David E.},
title = {HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models},
booktitle = {International Conference on Machine Learning},
year = {2026},
organization = {PMLR}
}