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README.md

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-license: apache-2.0
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+<p align="center">
+  <img src="figures/logo.jpg" alt="AROMA Logo" width="120">
+</p>
+
+<h2 align="center"> 🧬 AROMA: Augmented Reasoning Over a Multimodal Architecture for Virtual Cell Genetic Perturbation Modeling </h2>
+
+<p align="center">
+  📃 <a href="https://openreview.net/pdf?id=gRbreJtjST" target="_blank">Paper</a> • 🐙 <a href="https://github.com/blazerye/AROMA" target="_blank">Code</a> • 🗂️ <a href="https://huggingface.co/datasets/blazerye/PerturbReason" target="_blank">Datasets</a><br>
+</p>
+</p>
+
+> Please refer to our [repository](https://github.com/blazerye/AROMA) and [paper](https://openreview.net/pdf?id=gRbreJtjST) for more details.
+
+## 🌐 Overview
+
+AROMA is a novel multimodal architecture for virtual cell modeling that integrates textual evidence, graph topology, and protein sequences to predict the effects of genetic perturbations.
+
+<p align="center">
+  <img src="figures/overview.jpg" alt="Overview">
+</p>
+
+The overall AROMA pipeline is illustrated in the figure above and is divided into three stages:
+
+- **Data stage.** AROMA constructs two complementary knowledge graphs and a large-scale virtual cell reasoning dataset for evidence grounding.  
+
+- **Modeling stage.** AROMA adopts a retrieval-augmented strategy to incorporate query-relevant information, thereby providing explicit evidence cues for prediction. In addition, it jointly leverages topological representations learned from graph neural networks (GNN) and protein sequence representations encoded by ESM-2, and applies a cross-attention module to explicitly model perturbation-target gene dependencies across modalities.  
+
+- **Training stage.** AROMA first performs multimodal supervised fine-tuning (SFT), and is then further optimized with Group Relative Policy Optimization (GRPO) reinforcement learning to enhance predictive performance while generating biologically meaningful explanations.