DocDancer: Towards Agentic Document-Grounded Information Seeking

• URL: http://arxiv.org/abs/2601.05163v1
• Date: Thu, 08 Jan 2026 17:54:32 GMT
• Title: DocDancer: Towards Agentic Document-Grounded Information Seeking
• Authors: Qintong Zhang, Xinjie Lv, Jialong Wu, Baixuan Li, Zhengwei Tao, Guochen Yan, Huanyao Zhang, Bin Wang, Jiahao Xu, Haitao Mi, Wentao Zhang,
• Abstract summary: Document Question Answering (DocQA) focuses on answering questions grounded in given documents.<n>Existing DocQA agents lack effective tool utilization and largely rely on closed-source models.<n>We introduce DocDancer, an end-to-end trained open-source Doc agent.
• Score: 27.08333983540891
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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