DocDancer: Towards Agentic Document-Grounded Information Seeking
Authors: name: Qintong Zhang, name: Xinjie Lv, name: Jialong Wu, name: Baixuan Li, name: Zhengwei Tao, name: Guochen Yan, name: Huanyao Zhang, name: Bin Wang, name: Jiahao Xu, name: Haitao Mi, name: Wentao Zhang
Organization: Peking University

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.