Update README.md

README.md

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----
-license: cc-by-4.0
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+---
+license: cc-by-nc-nd-4.0
+tags:
+- spatial-transcriptomics
+- pathology
+- histology
+- deep-learning
+- pytorch
+---
+
+# MoLF: Mixture-of-Latent-Flow for Pan-Cancer Spatial Gene Expression Prediction from Histology
+
+<div align="center">
+
+[![arXiv](https://img.shields.io/badge/arXiv-2601.21560-b31b1b.svg)](https://arxiv.org/abs/2602.02282)
+[![Model Architecture](https://img.shields.io/badge/Model-MoLF-blue)]()
+[![Dataset](https://img.shields.io/badge/Dataset-HEST-green)]()
+
+</div>
+
+**MoLF** is a deep learning model designed to bridge the gap between histology images (H&E) and spatial gene expression. 
+
+Check out the details in the [github repo](https://github.com/susuhu/MoLF).
+
+This repository contains the weights for the checkpoints in the paper trained on the HEST v1.1.0 dataset.
+
+## 📄 Paper
+**Title:** MoLF: Mixture-of-Latent-Flow for Pan-Cancer Spatial Gene Expression Prediction from Histology  
+**Authors:** Hu, Susu and Speidel, Stefanie  
+**Link:** [ICLR 2026 arXiv](https://arxiv.org/abs/2602.02282)
+
+
+## 💻 Usage
+To load this checkpoint, ensure you have the MoLF codebase or compatible model definition.
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+# Download the model checkpoint
+checkpoint_path = hf_hub_download(repo_id="HuSusu/MoLF", filename="MoLF_latent_vae.ckpt")
+
+# Load weights (Pseudo-code: replace with your actual model class)
+# model = HistoPrism(config=...)
+# checkpoint = torch.load(path, map_location=map_location)
+# model.load_state_dict(checkpoint["model_state"])
+