Not in a zero shot approach. But LLMs are more than capable of solving a similar scenario to the one presented: - Parse all papers you want to audit - Extract

Not in a zero shot approach. But LLMs are more than capable of solving a similar scenario to the one presented: - Parse all papers you want to audit - Extract images (non AI) - Diff images (non AI) - Pull captions / related text near each image (LLM) - For each image > 99% similarity, use LLM to classify if conclusions are different (i.e. highly_similar, similar, highly_dissimilar). Then aggregate the results. It wouldn't prove fraud, but could definitely highlight areas for review. i.e. "This chart was used in 5 different papers with dissimilar conclusions"