TVIR: Building Deep Research Agents Towards Text--Visual Interleaved Report Generation
Authors: name: Xinkai Ma, name: Zhiqi Bai, name: Dingling Zhang, name: Pei Liu, name: Yishuo Yuan, name: He Zhu, name: Jiakai Wang, name: Qianqian Xie, name: Yifan Zhao, name: Xinlong Yang, name: Hao Cong, name: Zhiheng Yao, name: Fengxia Xie, name: Zihao Xu, name: Haoran Xu, name: Zhaohui Wang, name: Minghao Liu, name: Shirong Lin, name: Yingshui Tan, name: Yuchi Xu, name: Wenbo Su, name: Zhaoxiang Zhang, name: Bo Zheng, name: Jiaheng Liu
Organization: NJU-LINK Lab

Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form report generation, but existing benchmarks and systems remain predominantly text-centric, with limited evaluation of whether visual elements are factually reliable and well aligned with the surrounding analysis. To address this gap, we introduce TVIR (Text--Visual Interleaved Report Generation), which includes TVIR-Bench, a benchmark of 100 expert-curated multimodal deep research tasks that require visual elements to serve specific analytical sub-goals, and TVIR-Agent, a hierarchical multi-agent framework that serves as a strong baseline for constructing outlines, retrieving images, generating charts with traceable sources, and composing reports through context-aware sequential writing. We further develop a dual-path evaluation framework that combines Textual Assessment and Visual Assessment. Experiments across nine deep research systems show that TVIR-Agent achieves strong overall performance, underscoring the importance of explicit multimodal design and evaluation for evidence-driven report generation.