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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.02537 |
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| _version_ | 1866911417600311296 |
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| author | Zhou, Runjie Shao, Youbo Lu, Haoyu Xing, Bowei Bai, Tongtong Chen, Yujie Zhao, Jie Sui, Lin Yao, Haotian Zhao, Zijia Yang, Hao Wu, Haoning Zhou, Zaida Zhu, Jinguo Huang, Zhiqi Bao, Yiping Liu, Yangyang Charles, Y. Zhou, Xinyu |
| author_facet | Zhou, Runjie Shao, Youbo Lu, Haoyu Xing, Bowei Bai, Tongtong Chen, Yujie Zhao, Jie Sui, Lin Yao, Haotian Zhao, Zijia Yang, Hao Wu, Haoning Zhou, Zaida Zhu, Jinguo Huang, Zhiqi Bao, Yiping Liu, Yangyang Charles, Y. Zhou, Xinyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02537 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models Zhou, Runjie Shao, Youbo Lu, Haoyu Xing, Bowei Bai, Tongtong Chen, Yujie Zhao, Jie Sui, Lin Yao, Haotian Zhao, Zijia Yang, Hao Wu, Haoning Zhou, Zaida Zhu, Jinguo Huang, Zhiqi Bao, Yiping Liu, Yangyang Charles, Y. Zhou, Xinyu Computer Vision and Pattern Recognition Machine Learning 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. |
| title | WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2602.02537 |