We introduce CARTBENCH, a museum-grounded benchmark for evaluating vision-language models (VLMs) on Chinese artworks beyond short-form recognition and QA. CARTBENCH comprises four subtasks: CURATORQA for evidence-grounded recognition and reasoning, CATALOGCAPTION for structured four-section expert-style appreciation, REINTERPRET for defensible reinterpretation with expert ratings, and CONNOISSEURPAIRS for original-copy discrimination under visually similar confounds. CARTBENCH is built by aligning image-bearing Palace Museum objects from Wikidata with authoritative catalog pages, spanning five art categories across multiple dynasties. Across evaluated VLMs, high overall CURATORQA accuracy can mask sharp drops on evidence-heavy and style-to-period questions; long-form appreciation remains far from expert references; and original-copy discrimination remains near chance. A held-out protocol-replication pilot on 120 artworks from five external institutions reproduces the main task-difficulty pattern, while not preserving the exact overall model ranking.