arXiv AI By Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang

From Charts to Code: A Hierarchical Benchmark for Multimodal Models

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv AI
Sep 15

ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation

arXiv:2608.03464v2 Announce Type: replace Abstract: Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large...

By Zhenghan Chen, Zekai Shao, Lidan Tan, Xin Lin, Xingchen Zeng, Yi Shan, Ziyue Lin, Xiaoliang Fu, Xinyuan Liu, Yuetong Guo, Fen Wang, Bongshin Lee, Siming Chen
arXiv AI
Jun 10

ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering

arXiv:2510. 04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textual shortcuts.

By Rachneet Kaur, Nishan Srishankar, Zhen Zeng, Sumitra Ganesh, Manuela Veloso
Hugging Face Trending Papers
Sep 8

Charts Are Beyond Pixels: Probing for Layer-Wise Chart Understanding and Editing

The paper introduces LayerWiseBench, a benchmark that evaluates visual language models on layer-wise chart understanding and editing. It focuses on three core concepts—layer attribution, layer binding, and visibility ordering—by pairing rendered charts with spatially aligned per-layer RGBA assets and functional role labels. The benchmark includes 2,800 charts, 7,329 understanding questions, and 53,791 editing variants, revealing that models excel at attribution and binding but struggle with visibility ordering, especially when editing overlapping components.