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Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation

Paper · 2026 · Fangda Ye, Kuicai Dong, Xie Zhifei et al.

Summary

Agentic deep-research systems already plan, search and write long reports with fewer hallucinations, but they work almost entirely in text. Real expert reports also depend on charts, tables and infographics. This paper defines grounded multimodal long-form generation as a task and proposes DEEP-REPORTER, a framework with three agents:

  • Planner: builds section outlines. Each section gets a coarse description plus a checklist of fine-grained "semantic anchors."
  • Searcher-Filter: issues separate text and image queries. LLM and VLM filters then discard irrelevant or decorative content.
  • Reporter: writes the report section by section, compressing earlier history so the context stays manageable while images and citations are placed coherently.

To give open-weight models these skills, the authors curate 8K expert agentic traces for SFT and DPO. They also release M2LongBench, a reproducible offline sandbox benchmark that needs no commercial APIs. Experiments show that the task is hard, especially choosing and integrating visuals. DEEP-REPORTER beats RAG baselines, and post-training on the curated traces narrows the gap. The framework also transfers to the open web.

Key points

  • M2LongBench: 247 research tasks across 9 domains. Its static sandbox holds 95K images and 108M text chunks, drawn from 45K webpages and 6.4K PDFs. Each report has on average 102 images and 168 text chunks annotated as ground-truth evidence.
  • Trace curation: about 1k seed queries produced 17k traces. These were cut to 8K high-quality ones through model scoring and human checks for visual hallucination. On 500 sampled traces, human-model consensus was 92.4% and inter-annotator agreement was 89.5%.
  • Ablations: relevance-aware filtering and recurrent context management complement each other. Filtering mainly improves evidence quality, and context management mainly improves long-range coherence.
  • Training: optimization uses only SFT and DPO. Early GRPO attempts gave limited gains, which the authors attribute to sparse rewards that are hard to define.
  • Limitations: the system is English-only and the corpus is static. Code is available at github.com/fangda-ye/Deep-Report.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 1Research subjects and languages the work is about.Vision & language · 811 works

Cite

Cite as Fangda Ye, Kuicai Dong, Xie Zhifei, Yuxin Hu, Yihang Yin, Shurui Huang, Shikai Dong, Chen Zhang, Jianzhu Bao and Shuicheng Yan. 2026. Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1909

@inproceedings{ye2026deep,
  title = {{Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation}},
  author = {Fangda Ye, Kuicai Dong, Xie Zhifei, Yuxin Hu, Yihang Yin, Shurui Huang, Shikai Dong, Chen Zhang, Jianzhu Bao and Shuicheng Yan},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1909},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1909}
}

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