Microsoft Research By Chenglong Wang, Alper Sarikaya, Scott Tsukamaki, Michel Galley, Jianfeng Gao

Flint: A visualization language for the AI era

Read the original on Microsoft Research →

Short chart specifications are easy to write, but often produce uninspiring results. Flint is an open-source visualization language that offers a middle path, letting AI agents create expressive charts from compact, human-editable specifications.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Microsoft Research.

Microsoft Research
Oct 22

Flint-interactive: A language for creating interactive data visualizations

Flint‑interactive is a language developed by Microsoft Research that enables AI agents to generate interactive data visualizations. It allows users to click on charts, filter data, highlight related content, and explore beyond the initially chosen view. The system is designed to enhance user interaction with visual data representations.

By Chenglong Wang, Yuwei Xiao, Alper Sarikaya, Michel Galley, Chris Quirk, Jianfeng Gao
Hugging Face Trending Papers
Aug 4

ChartAnno: Evaluating MLLMs for Chart Annotation Generation

Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains underexplored. Annotating charts is a common yet challenging communicative task, requiring models to infer intended messages, interpret chart semantics, and place appropriate textual or graphical elements.

arXiv AI
Aug 5

ChartAnno: Evaluating MLLMs for Chart Annotation Generation

arXiv:2608. 03464v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains underexplored.

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
Aug 5

Automated Visualization Code Synthesis via Multi-Path Reasoning and Feedback-Driven Optimization

arXiv:2502. 11140v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have become a cornerstone for automated visualization code generation, enabling users to create charts through natural language instructions.

By Wonduk Seo, Daye Kang, Hyunjin An, Taehan Kim, Soohyuk Cho, Seungyong Lee, Minhyeong Yu, Jian Park, Yi Bu, Seunghyun Lee