| --- |
| license: mit |
| tags: |
| - world-model |
| - video-prediction |
| - hippocampus |
| - entorhinal-cortex |
| - representation-learning |
| --- |
| |
| # 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} |
| } |
