CellVELA: Contextualizing Cell Foundation Models and LLMs via Retrieved Experimental Evidence

Cell foundation models and large language models (LLMs) are increasingly adapted for functional genomics, where predictions often depend on cellular context rather than on genes alone. We study how such context should be transferred to unseen biological settings. Cancer vulnerability provides a stringent testbed: the same gene can be essential in one cancer and dispensable in another, yet standard target-ranking metrics strongly reward genes that are broadly essential. We separate this gene-level prior from context-specific discrimination and show that a fixed ranking can score well while carrying no contextual information. Parameter-efficient fine-tuning of the LLM, including a within-target contrast objective, fits the training contexts but remains near chance on held-out cancer lines across changes in model scale, interface, labels, and sampling. We therefore propose CellVELA, which retrieves matched CRISPR measurements as experimental evidence at inference. This restores held-out discrimination, improves with evidence similarity and quantity, vanishes under random retrieval, and recovers canonical lineage-specific dependencies. The same pattern extends to perturbation-response prediction, where retrieval quality matters more than reader complexity. These results suggest that context-specific functional prediction depends not only on model adaptation, but on preserving the right experimental evidence at inference.