---
license: mit
pipeline_tag: summarization
---

# ELF: Embedded Language Flows

ELF (Embedded Language Flows) is a class of continuous diffusion language models based on continuous-time Flow Matching. Unlike existing diffusion language models (DLMs), ELF predominantly stays within the continuous embedding space until the final time step, where it maps to discrete tokens using a shared-weight network. This formulation makes it straightforward to adapt established techniques from image-domain diffusion models, such as classifier-free guidance (CFG).

- **Paper:** [ELF: Embedded Language Flows](https://huggingface.co/papers/2605.10938)
- **Repository:** [https://github.com/lillian039/ELF](https://github.com/lillian039/ELF)

## Sample Usage

To evaluate this model on the XSum summarization task using the official JAX implementation, you can use the following command:

```bash
cd src/

# Run evaluation on XSum
python eval.py \
    --config configs/training_configs/train_xsum_ELF-B.yml \
    --checkpoint_path embedded-language-flows/ELF-B-xsum
```

The evaluator runs on the task's validation set and reports ROUGE-1/2/L scores.

## Citation

If you find this work useful, please consider citing the paper:

```bibtex
@article{elf2026,
  title={ELF: Embedded Language Flows},
  author={Hu, Keya and Qiu, Linlu and Lu, Yiyang and Zhao, Hanhong and Li, Tianhong and Kim, Yoon and Andreas, Jacob and He, Kaiming},
  journal={arXiv preprint arXiv:2605.10938},
  year={2026}
}
```