WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models
Authors: name: Runjie Zhou, name: Youbo Shao, name: Haoyu Lu, name: Bowei Xing, name: Tongtong Bai, name: Yujie Chen, name: Jie Zhao, name: Lin Sui, name: Haotian Yao, name: Zijia Zhao, name: Hao Yang, name: Haoning Wu, name: Zaida Zhou, name: Jinguo Zhu, name: Zhiqi Huang, name: Yiping Bao, name: Yangyang Liu, name: Y. Charles, name: Xinyu Zhou
Organization: Moonshot AI

We introduce WorldVQA, a benchmark designed to evaluate the atomic visual world knowledge of Multimodal Large Language Models (MLLMs). Unlike current evaluations, which often conflate visual knowledge retrieval with reasoning, WorldVQA decouples these capabilities to strictly measure "what the model memorizes." The benchmark assesses the atomic capability of grounding and naming visual entities across a stratified taxonomy, spanning from common head-class objects to long-tail rarities. We expect WorldVQA to serve as a rigorous test for visual factuality, thereby establishing a standard for assessing the encyclopedic breadth and hallucination rates of current and next-generation frontier models.