DocDancer: Towards Agentic Document-Grounded Information Seeking

Qintong Zhang, Xinjie Lv, Jialong Wu, Baixuan Li, Zhengwei Tao, Guochen Yan, HalcyonZhang, Bin Wang, Jiahao Xu, Haitao Mi, Wentao Zhang

DocDancer: Towards Agentic Document-Grounded Information Seeking: 7 upvotes on Hugging Face Daily Papers, #19 of 33 papers on 2026-01-09. Day-by-day upvote history.

Document Question Answering (DocQA) focuses on answering questions grounded in given documents, yet existing DocQA agents lack effective tool utilization and largely rely on closed-source models. In this work, we introduce DocDancer, an end-to-end trained open-source Doc agent. We formulate DocQA as an information-seeking problem and propose a tool-driven agent framework that explicitly models document exploration and comprehension. To enable end-to-end training of such agents, we introduce an Exploration-then-Synthesis data synthesis pipeline that addresses the scarcity of high-quality training data for DocQA. Training on the synthesized data, the trained models on two long-context document understanding benchmarks, MMLongBench-Doc and DocBench, show their effectiveness. Further analysis provides valuable insights for the agentic tool design and synthetic data.

Paper page on Hugging Face · arXiv

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