What Makes a Generated Cell Realistic? Dissecting Biological Fidelity in Single-Cell Generative Models

Single-cell generative models are increasingly used to synthesize cellular profiles and predict unobserved biological states, but defining what makes a generated cell realistic remains an open problem. Existing evaluations often collapse realism into global statistical similarity, low-dimensional overlap, or classifier agreement, even though a generated cell may appear realistic in one sense while being biologically wrong in another. We propose CellFidelity, a multidimensional diagnostic framework that decomposes biological realism into four complementary dimensions: lexical fidelity of gene usage and expression magnitude, syntactic fidelity of gene relationships and functional programs, semantic fidelity of conditional cellular identity and identity-associated programs, and population fidelity of diversity and organization. CellFidelity operationalizes these dimensions using reference-calibrated measurements that distinguish deviations from the empirical variation observed among real cells. Across representative single-cell generators, CellFidelity exposes failure modes hidden by aggregate metrics: strong marker enrichment despite incomplete identity programs, plausible pathway activity despite sparse constituent-gene recovery, and globally similar populations despite distorted heterogeneity. These results show that generated-cell realism cannot be captured by a single notion of similarity. CellFidelity provides a diagnostic framework for revealing model-specific biological failures, guiding model development, and supporting more principled use of generated cells in downstream biology.