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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.05163 |
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| _version_ | 1866917190860537856 |
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| author | Zhang, Qintong Lv, Xinjie Wu, Jialong Li, Baixuan Tao, Zhengwei Yan, Guochen Zhang, Huanyao Wang, Bin Xu, Jiahao Mi, Haitao Zhang, Wentao |
| author_facet | Zhang, Qintong Lv, Xinjie Wu, Jialong Li, Baixuan Tao, Zhengwei Yan, Guochen Zhang, Huanyao Wang, Bin Xu, Jiahao Mi, Haitao Zhang, Wentao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05163 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DocDancer: Towards Agentic Document-Grounded Information Seeking Zhang, Qintong Lv, Xinjie Wu, Jialong Li, Baixuan Tao, Zhengwei Yan, Guochen Zhang, Huanyao Wang, Bin Xu, Jiahao Mi, Haitao Zhang, Wentao Computation and Language 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. |
| title | DocDancer: Towards Agentic Document-Grounded Information Seeking |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.05163 |