Recent agentic search frameworks enable deep research via iterative planning and retrieval, reducing hallucinations and enhancing factual grounding. However, they remain text-centric, overlooking the multimodal evidence that characterizes real-world expert reports. We introduce a pressing task: multimodal long-form generation. Accordingly, we propose Deep-Reporter, a unified agentic framework for grounded multimodal long-form generation. It orchestrates: (i) Agentic Multimodal Search and Filtering to retrieve and filter textual passages and information-dense visuals; (ii) Checklist-Guided Incremental Synthesis to ensure coherent image-text integration and optimal citation placement; and (iii) Recurrent Context Management to balance long-range coherence with local fluency. We develop a rigorous curation pipeline producing 8K high-quality agentic traces for model optimization. We further introduce M2LongBench, a comprehensive testbed comprising 247 research tasks across 9 domains and a stable multimodal sandbox. Extensive experiments demonstrate that long-form multimodal generation is a challenging task, especially in multimodal selection and integration, and effective post-training can bridge the gap.
@article{arxiv.2604.10741,
title = {Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation},
author = {Fangda Ye and Zhifei Xie and Yuxin Hu and Yihang Yin and Shurui Huang and Shikai Dong and Jianzhu Bao and Shuicheng Yan},
journal= {arXiv preprint arXiv:2604.10741},
year = {2026}
}
Comments
41 pages, 6 figures, 8 tables. Code available at https://github.com/fangda-ye/Deep-Report. v2: corrected typos and updated experimental results