Researchers invert multi-vector visual document indices with a conditional flow-matching inverter, recovering 47% of words and 45% of sensitive tokens and ranking the source page first 98.4% of the time
The work frames inversion of a multi-vector visual document index as conditional document image generation, identifying the encoder, inferring page shape and restoring shuffled order from the stored vectors alone under zero side information, and redrawing the page with a conditional flow-matching inverter; on ViDoRe v3, pages inverted from the raw index recover 47% of words and 45% of sensitive tokens and rank their source page first 98.4% of the time.