DVBench: Benchmarking MLLMs for Understanding Dynamic Charts and Narratives in Data Videos
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Computer Science > Computation and Language
arXiv:2608.29711 (cs)
[Submitted on 30 Aug 2026]
Title:DVBench: Benchmarking MLLMs for Understanding Dynamic Charts and Narratives in Data Videos
View a PDF of the paper titled DVBench: Benchmarking MLLMs for Understanding Dynamic Charts and Narratives in Data Videos, by Bomiao Wang and 5 other authors
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Abstract:While MLLMs have made significant strides in chart comprehension and video understanding, current evaluations largely isolate these capabilities, leaving a critical gap in understanding temporally evolving structured visual information. To address this gap, we introduce DVBench, a benchmark for evaluating MLLMs on data videos, a storytelling medium that integrates dynamic charts with structured narratives. We decompose data video understanding into five dimensions. DVBench comprises 300 real-world data videos and 1,000 human-verified QA pairs curated through a rigorous semi-automated pipeline. Extensive evaluations of nine MLLMs show that Gemini-3.1-Pro achieves the best overall performance, while Kimi-k2.5 is the strongest open-source model. We further identify two notable phenomena: open-source model performance does not scale strictly with parameter size, and narrative proficiency does not guarantee visual capability. Fine-grained analyses and ablation studies further reveal dimension-specific weaknesses and the effects of frame configurations and subtitle inputs, informing future MLLM development. DVBench is publicly available at this https URL.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.29711 [cs.CL] |
| (or arXiv:2608.29711v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.29711
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled DVBench: Benchmarking MLLMs for Understanding Dynamic Charts and Narratives in Data Videos, by Bomiao Wang and 5 other authors
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