DocDancer: Towards Agentic Document-Grounded Information Seeking
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.
696071945b7998385e6394b7
Qintong Zhang
696071945b7998385e6394b8
Xinjie Lv
696071945b7998385e6394b9
Jialong Wu
claimed_verified
2026-01-09T08:35:13.405Z
644a4fbc2166258fccc664bc
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/8k3b44MbhQiWuo6i8BnYl.jpeg
Jialong Wu
callanwu
user
callanwu
696071945b7998385e6394ba
Baixuan Li
696071945b7998385e6394bb
Zhengwei Tao
696071945b7998385e6394bc
Guochen Yan
696071945b7998385e6394bd
Huanyao Zhang
696071945b7998385e6394be
Bin Wang
696071945b7998385e6394bf
Jiahao Xu
696071945b7998385e6394c0
Haitao Mi
696071945b7998385e6394c1
Wentao Zhang
DocDancer: Towards Agentic Document-Grounded Information Seeking
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.
DocDancer is an end-to-end trained open-source document question answering agent that formulates the task as an information-seeking problem and uses a tool-driven framework with exploration and synthesis for training.
document question answering information-seeking problem tool-driven agent framework document exploration document comprehension end-to-end training data synthesis pipeline long-context document understanding MMLongBench-Doc DocBench