blazerye/AROMA

🧬 AROMA: Augmented Reasoning Over a Multimodal Architecture for Virtual Cell Genetic Perturbation Modeling
(ACL 2026 Findings)

📃 Paper • 🐙 Code • 🗂️ Datasets

Please refer to our repository and paper 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.

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.