VCWorld: A Biological World Model for Virtual Cell Simulation 1. VCWorld introduces a novel cell-level white-box simulator that integrates structured biological knowledge with large language models (LLMs) to predict cellular responses to perturbations, offering a transparent and interpretable alternative to traditional black-box models. 2. The core innovation lies in its biological world model, which leverages signaling pathways, protein-protein interactions, and gene regulatory networks to enhance data efficiency and generalization, even with limited training data. 3. VCWorld achieves state-of-the-art performance in predicting differential expression and directional changes in gene expression, outperforming existing models while providing mechanistic explanations aligned with biological principles. 4. The study introduces GeneTAK, a new benchmark derived from the Tahoe-100M dataset, reframing cell-drug observations into gene-centric perturbation responses to enable more granular modeling of drug effects. 5. VCWorld's reasoning process is grounded in a comprehensive biological knowledge graph, ensuring that predictions are not only accurate but also verifiable through step-by-step mechanistic hypotheses. 6. The model demonstrates robustness in few-shot learning scenarios, making it particularly valuable for predicting responses to novel perturbations not present in the training data. 7. VCWorld's design emphasizes interpretability, allowing biologists to trace the reasoning behind each prediction, which is crucial for scientific discovery and experimental design. 📜Paper: arxiv.org/abs/2512.00306v1 #VirtualCellModeling #BiologicalWorldModel #LLMs #PerturbationPrediction #InterpretableAI #ComputationalBiology