flowm123/mnistworld-models

MNIST World Models

This repository hosts a curated set of checkpoints used for experiments in the paper Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments.

Project Page | GitHub

Description

Flow Equivariant World Modeling (FloWM) is a framework that leverages time-parameterized symmetries within a latent memory for stable and accurate dynamics prediction over long horizons. The latent memory shifts and transforms equivariantly with self-motion and inferred external object motion, keeping information about out-of-view regions aligned as time progresses. These specific checkpoints correspond to the MNIST World (2D) environment benchmarks.

Contents

This repository contains the following checkpoints:

• mnistworld/dfot/dynamic_po.ckpt — MNIST World Diffusion Forcing Transformer (DFoT)
• mnistworld/dfot-ssm/dynamic_po.ckpt — MNIST World Diffusion State Space Model (DFoT-SSM)
• mnistworld/flowm-fernn/dynamic_po_flowm.pt — MNIST World FloWM (FERN-N)
• mnistworld/vae/mnist_world_vae.ckpt — MNIST World VAE

Usage

For detailed instructions on environment setup, training, and inference (including replicating the paper's results), please refer to the official GitHub repository and its wiki.

Citation

@misc{lillemark2026flowequivariantworldmodels,
    title={Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments}, 
    author={Hansen Jin Lillemark and Benhao Huang and Fangneng Zhan and Yilun Du and Thomas Anderson Keller},
    year={2026},
    eprint={2601.01075},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    url={https://arxiv.org/abs/2601.01075}, 
  }