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senngadaisuki/
hpc-mec-worldmodel

Name: hpc-mec-worldmodel
Availability: InStock

representation-learning

2605.15733

mit

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HPC-MEC World Model

This repository hosts pretrained checkpoints for Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model.

The model is a hippocampal-entorhinal inspired world model that learns reusable transition structures from observation-only videos. It separates content-rich episodic representations from compact abstract dynamics, and uses velocity-like latent transitions for prediction and structural generalization across objects and scenes.

Paper

Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model
Tianqiu Zhang*, Muyang Lyu*, Xiao Liu, Si Wu
ICML 2026

Paper: https://arxiv.org/abs/2605.15733
Project page: https://hpc-mec-worldmodel.github.io/

Code

The training and evaluation code is available at:

https://github.com/senngadaisuki/hpc-mec-worldmodel

Usage

Please see the GitHub repository for installation, checkpoint loading, training, and evaluation instructions.

Citation

bibtex

@inproceedings{zhang2026structure,
  title = {Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model},
  author = {Zhang, Tianqiu and Lyu, Muyang and Liu, Xiao and Wu, Si},
  booktitle = {Forty-third International Conference on Machine Learning},
  year = {2026},
  url = {https://openreview.net/forum?id=AYXgo5FjYz}
}