{"ID":6497844,"CreatedAt":"2026-07-13T01:19:40.13847098Z","UpdatedAt":"2026-07-14T01:36:59.12045529Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.09068","arxiv_id":"2607.09068","title":"OmniMapBench: Benchmarking Visual-Centric Reasoning on Diverse Map Documents","abstract":"Recent advancements in LVLMs necessitate robust benchmarks for complex, visually grounded reasoning. A critical limitation is identified in many document understanding benchmarks: visual content is often reducible to text, enabling high performance without genuine visual grounding. To address this limitation, OmniMapBench is introduced to foster visual-centric reasoning for map documents. The benchmark comprises 2,096 manually annotated question-answer pairs across 1,603 map documents from nine categories. It is designed to probe a hierarchy of skills, ranging from perception to multi-step visual reasoning. To quantify benchmark properties, a simple yet effective benchmark-level metric is proposed: the Visual Dependency Index (VDI), defined as the accuracy drop when images are replaced with question-agnostic descriptions. OmniMapBench exhibits higher VDI than established benchmarks, which quantitatively validates its focus on irreducible visual reasoning. Comprehensive evaluations of 25 leading LVLMs are conducted on OmniMapBench. A significant performance gap is observed, with the top-performing model achieving only 75.03\\% accuracy. This result underscores the challenges posed by OmniMapBench to current LVLMs. This work aims to catalyze progress in visual-centric reasoning for document understanding of LVLMs. The dataset and code are publicly available at https://github.com/SIGMME/OmniMapBench.","short_abstract":"Recent advancements in LVLMs necessitate robust benchmarks for complex, visually grounded reasoning. A critical limitation is identified in many document understanding benchmarks: visual content is often reducible to text, enabling high performance without genuine visual grounding. To address this limitation, OmniMapBe...","url_abs":"https://arxiv.org/abs/2607.09068","url_pdf":"https://arxiv.org/pdf/2607.09068v1","authors":"[\"Yang Chen\",\"Yunwen Li\",\"Yufan Shen\",\"Minghao Liu\",\"Tianyu Zheng\",\"Bin Fu\",\"Qunshu Lin\",\"Zhi Yu\",\"Botian Shi\"]","published":"2026-07-10T03:27:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":614128,"CreatedAt":"2026-07-13T01:19:40.13847098Z","UpdatedAt":"2026-07-13T01:19:40.13847098Z","DeletedAt":null,"paper_id":6497844,"paper_url":"https://arxiv.org/abs/2607.09068","paper_title":"OmniMapBench: Benchmarking Visual-Centric Reasoning on Diverse Map Documents","repo_url":"https://github.com/SIGMME/OmniMapBench","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}