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Large vision-language models (VLMs) have demonstrated remarkable abilities in understanding everyday content, however, their performance in the domain of art remains less explored. Evaluation of art, particularly in regard to its authenticity, can be approached from art historical, scientific and archival / provenance methods. This talk will discuss the advantages of applying AI to the data generated from such research and, in particular,  we will demonstrate how our VLM interprets the decoration on 3D objects, such as Chinese porcelain. Relying on Dr Ni Yibin’s vast database of images found on Chinese art objects of all media, we have focussed on three primary tasks: identifying salient visual elements, matching these elements with their symbolic meanings, and interpreting the intended messages. We will discuss the added complexity incurred by the Chinese language and the use of “pun rebuses” in its art and culture. Finally, we will show how our AI assisted programme could support museums, dealers and collectors in the future.