chreode-pretrained

Chreode — pretrained backbone

Pretrained weights for Chreode, a one-step cell world model.

Paper: arXiv:2605.28111 · Code: github.com/mufanq/Chreode

Files

How to use

from huggingface_hub import snapshot_download
import torch

ckpt_dir = snapshot_download(repo_id="WhenceFade/chreode-pretrained")

vae          = torch.load(f"{ckpt_dir}/vae.pt",          map_location="cpu", weights_only=False)
dynamics_dit = torch.load(f"{ckpt_dir}/dynamics_dit.pt", map_location="cpu", weights_only=False)
static_dit   = torch.load(f"{ckpt_dir}/static_dit.pt",   map_location="cpu", weights_only=False)

End-to-end loader and the full latent → prediction example are in the companion GitHub repo; see reproduce/01_pretrain.md for the exact config, and reproduce/00_setup.md for environment setup.

Pretraining data

• 2,477,217 mouse embryonic cells from 7 public datasets, 10 leaf trajectories, 88 sampled timepoints (0 → 19 dpf).
• Gene vocabulary: 16,520 mouse–human 1:1 orthologs (Ensembl BioMart, confidence=1).
• Preprocessing: normalize_total(1e4) + log1p. Cached preprocessing artifacts: WhenceFade/chreode-phase0.

Training recipe

Reported metrics (paper Tables 1–7)

When this backbone is plugged into the downstream evaluation in mufanq/Chreode:

Three downstream tasks include fine-tuning; the fate task is zero-shot.

Intended use

• Predict population-level transitions $p(z_{t+\Delta} \mid z_t, \mathrm{do}(a))$ on single-cell transcriptomics, with a one-pass residual generator.
• Use as a starting point for fine-tuning on new developmental or perturbation atlases that share the mouse–human 1:1 ortholog vocabulary.
• Use as a gene-state embedding inside other perturbation predictors (e.g. GEARS).

Out-of-scope use

• Not a general-purpose representation learner — for cell-type annotation, integration, or gene-network reasoning, prefer Geneformer / scGPT.
• Trained only on mouse embryonic data. Cross-species transfer is mediated by 1:1 orthologs; adult-human tissues are out of distribution.
• The fine-tuned Norman headline (DE20 MSE 0.18580) is a single-seed number; see reproduce/known_issues.md.

Bias, risks, and limitations

• Training data is heavily biased toward early embryonic development; cell-state coverage in adult tissues is poor.
• The model is a predictive generator, not a causal one, even though we condition on do(a) notationally. For mechanistic claims, treat predictions as hypotheses, not endpoints.
• Same atlas-level confounders (batch / lab / donor heterogeneity) carry into latent space.

License

MIT — see the GitHub repository.

Citation

@article{qiu2026chreode,
  title         = {Chreode: A Cell World Model for One-Step Temporal Dynamics and Perturbation Prediction},
  author        = {Qiu, Mufan and Zheng, Genhui and Xu, Yinuo and Zhang, Ruichen and Ding, Ying and Long, Qi and Chen, Tianlong},
  year          = {2026},
  eprint        = {2605.28111},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2605.28111}
}