Toward a Coherent Virtual Cell Model: Probing Biological World-Model Coherence in Transcriptomic Foundation Models

Transcriptomic foundation models (TFMs) promise to act as virtual cell models, but it remains unclear whether they have internalized the biological rules of transcriptomic space. To address this question, we propose assessing the quality of pretrained TFMs by probing the coherence of their internal world model using the pretraining loss on synthetic samples. Our approach combines two complementary tests. First, as a stress test of plausibility, we compare pretraining loss on shuffled cells compared to real samples. Second, to probe the coherence of the internal world model, we evaluate interpolated samples both within and between cell types, quantifying whether the model identifies coherent clusters. Across multiple datasets, TFMs tend to distinguish real and shuffled cells, with entropy of expression value strongly predicting the loss gap. Interpolations reveal "loss barriers" between distant cell types while similar cell types tend not to have barriers. Interestingly, much of the structure of cell embeddings persists despite the shuffling of the values of expressed genes. This approach demonstrates that quantification of an internal world model is possible, even in a "zero resource" setting, without labeled data. We argue that this is a critical step toward identifying whether TFMs can truly function as virtual cell models, rather than stochastic parrots.