A test set missing night images picks the wrong model: rewriting day photos into night restores the selection a fully covered set would make
When a test set underrepresents a semantic condition such as night, model selection can be misled; a generative pipeline uses vision-language models to find the missing semantics and diffusion models to turn day images into night images, so the augmented test set selects the same model as a fully covered one.