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MLLMs struggle with multi-hop reasoning in document-style images, achieving only 62.83% accuracy compared to over 90% for human annotators.
System prompts in commercial AI products are often a mixed bag, with 40% harboring instructions that can undermine user interests, revealing a critical gap in accountability.
