# DVBench: Benchmarking MLLMs for Understanding Dynamic Charts and Narratives in Data Videos

arXiv: https://arxiv.org/abs/2608.29711 · HTML: https://mlsanity.com/paper/2608.29711/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 30 Aug 2026.

## Builds on (most cited first, 20 of 20)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [LVBench: An Extreme Long Video Understanding Benchmark](https://mlsanity.com/paper/2406.08035/) (2024)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [PlotQA: Reasoning over Scientific Plots](https://mlsanity.com/paper/1909.00997/) (2019)
- [ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning](https://mlsanity.com/paper/2402.12185/) (2024)
- [VisText: A Benchmark for Semantically Rich Chart Captioning](https://mlsanity.com/paper/2307.05356/) (2023)
- [ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering](https://mlsanity.com/paper/2405.07001/) (2024)
- [EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching](https://mlsanity.com/paper/2111.08919/) (2021)
- [CAPability: A Comprehensive Visual Caption Benchmark for Evaluating Both Correctness and Thoroughness](https://mlsanity.com/paper/2502.14914/) (2025)
- [VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos](https://mlsanity.com/paper/2506.10857/) (2025)
- [InfiniBench: A Benchmark for Large Multi-Modal Models in Long-Form Movies and TV Shows](https://mlsanity.com/paper/2406.19875/) (2024)
- [Narrative Player: Reviving Data Narratives with Visuals](https://mlsanity.com/paper/2410.03268/) (2024)
- [ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset](https://mlsanity.com/paper/2501.09349/) (2025)
- [ChartCap: Mitigating Hallucination of Dense Chart Captioning](https://mlsanity.com/paper/2508.03164/) (2025)
- [SeriesBench: A Benchmark for Narrative-Driven Drama Series Understanding](https://mlsanity.com/paper/2504.21435/) (2025)
- [From `Wow' to `Why': Guidelines for Creating the Opening of a Data Video with Cinematic Styles](https://mlsanity.com/paper/2202.02709/) (2022)
- [MapStory: Prototyping Editable Map Animations with LLM Agents](https://mlsanity.com/paper/2505.21966/) (2025)
- [Is It the End? Guidelines for Cinematic Endings in Data Videos](https://mlsanity.com/paper/2303.14491/) (2023)

## Abstract (arXiv)

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 https://bomiaowang.github.io/DVBench/.