A Mechanistic Probe of Regulatory Structure in Single-Cell Foundation Models

Do single-cell foundation models (scFMs) preserve the regulatory information needed to explain cellular responses to perturbation? Existing evaluations measure predictive accuracy, which cannot establish which regulatory relationships their representations retain. We introduce CLAMP, a mechanistic probe that fits a gene regulatory network through a frozen encoder and evaluates the recovered network in gene space, where its interactions can be inspected and tested against independent biological references. We apply CLAMP to four scFMs (scGPT, scPRINT, Stack and Tahoe-x1) on four Perturb-seq datasets (Norman, Replogle K562, Replogle RPE1 and X-Atlas HCT116). Fitted directly in gene space, CLAMP recovers 70–89% of an empirical ceiling, predicts unseen two-gene interventions without combinatorial training, and recovers the known lineage programs of key regulators. Recovery through the scFMs generally falls below simple baselines such as PCA and random projections. The deficit concentrates in the part of each response that identifies its regulator: the recovered networks reproduce responses shared across interventions more readily than the differences between them. A layerwise analysis shows that this specificity is already reduced at the first transformer block and is not regained with depth, and that the encoders transmit lower-variance regulatory directions more weakly than leading ones. These results suggest pretraining objectives that reward distinguishing perturbations as a way to preserve regulatory specificity. Code will be released upon acceptance.