Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports
  From Scratch with Agentic Framework
Authors: name: Zhaorui Yang, name: Bo Pan, name: Han Wang, name: Yiyao Wang, name: Xingyu Liu, name: Minfeng Zhu, name: Bo Zhang, name: Wei Chen

Visualizations play a crucial part in effective communication of concepts and
information. Recent advances in reasoning and retrieval augmented generation
have enabled Large Language Models (LLMs) to perform deep research and generate
comprehensive reports. Despite its progress, existing deep research frameworks
primarily focus on generating text-only content, leaving the automated
generation of interleaved texts and visualizations underexplored. This novel
task poses key challenges in designing informative visualizations and
effectively integrating them with text reports. To address these challenges, we
propose Formal Description of Visualization (FDV), a structured textual
representation of charts that enables LLMs to learn from and generate diverse,
high-quality visualizations. Building on this representation, we introduce
Multimodal DeepResearcher, an agentic framework that decomposes the task into
four stages: (1) researching, (2) exemplar report textualization, (3) planning,
and (4) multimodal report generation. For the evaluation of generated
multimodal reports, we develop MultimodalReportBench, which contains 100
diverse topics served as inputs along with 5 dedicated metrics. Extensive
experiments across models and evaluation methods demonstrate the effectiveness
of Multimodal DeepResearcher. Notably, utilizing the same Claude 3.7 Sonnet
model, Multimodal DeepResearcher achieves an 82\% overall win rate over the
baseline method.