# DocDancer: Towards Agentic Document-Grounded Information Seeking

arXiv: https://arxiv.org/abs/2601.05163 · HTML: https://mlsanity.com/paper/2601.05163/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 30 of its 39 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 30 of 30)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning](https://mlsanity.com/paper/2408.05517/) (2024)
- [SWE-smith: Scaling Data for Software Engineering Agents](https://mlsanity.com/paper/2504.21798/) (2025)
- [FireAct: Toward Language Agent Fine-tuning](https://mlsanity.com/paper/2310.05915/) (2023)
- [VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents](https://mlsanity.com/paper/2410.10594/) (2024)
- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations](https://mlsanity.com/paper/2407.01523/) (2024)
- [WebWalker: Benchmarking LLMs in Web Traversal](https://mlsanity.com/paper/2501.07572/) (2025)
- [APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay](https://mlsanity.com/paper/2504.03601/) (2025)
- [OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis](https://mlsanity.com/paper/2412.19723/) (2024)
- [Document Understanding Dataset and Evaluation (DUDE)](https://mlsanity.com/paper/2305.08455/) (2023)
- [A Comprehensive Survey on Long Context Language Modeling](https://mlsanity.com/paper/2503.17407/) (2025)
- [mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding](https://mlsanity.com/paper/2409.03420/) (2024)
- [LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating](https://mlsanity.com/paper/2412.18424/) (2024)
- [Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents](https://mlsanity.com/paper/2502.11357/) (2025)
- [MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding](https://mlsanity.com/paper/2503.13964/) (2025)
- [MMDocIR: Benchmarking Multimodal Retrieval for Long Documents](https://mlsanity.com/paper/2501.08828/) (2025)
- [DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading Systems](https://mlsanity.com/paper/2407.10701/) (2024)
- [Benchmarking Retrieval-Augmented Multimodal Generation for Document Question Answering](https://mlsanity.com/paper/2505.16470/) (2025)
- [Visual Document Understanding and Reasoning: A Multi-Agent Collaboration Framework with Agent-Wise Adaptive Test-Time Scaling](https://mlsanity.com/paper/2508.03404/) (2025)
- [PDFTriage: Question Answering over Long, Structured Documents](https://mlsanity.com/paper/2309.08872/) (2023)
- [SimpleDoc: Multi-Modal Document Understanding with Dual-Cue Page Retrieval and Iterative Refinement](https://mlsanity.com/paper/2506.14035/) (2025)
- [RAG-Anything: All-in-One RAG Framework](https://mlsanity.com/paper/2510.12323/) (2025)
- [V-Doc : Visual questions answers with Documents](https://mlsanity.com/paper/2205.13724/) (2022)
- [DocLens : A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding](https://mlsanity.com/paper/2511.11552/) (2025)
- [DocXChain: A Powerful Open-Source Toolchain for Document Parsing and Beyond](https://mlsanity.com/paper/2310.12430/) (2023)
- [AUTO-Explorer: Automated Data Collection for GUI Agent](https://mlsanity.com/paper/2511.06417/) (2025)

## Built on by (most cited first, 12 of 12)

- [AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generation](https://mlsanity.com/paper/2602.24134/) (2026)
- [CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence](https://mlsanity.com/paper/2605.12882/) (2026)
- [AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning](https://mlsanity.com/paper/2606.24526/) (2026)
- [DocArena: Turning Raw Documents into Controllable Training Environments for Document Search Agents](https://mlsanity.com/paper/2606.26122/) (2026)
- [MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QA](https://mlsanity.com/paper/2606.05749/) (2026)
- [Exploring Information Seeking Agent Consolidation](https://mlsanity.com/paper/2602.00585/) (2026)
- [Storyline Trees: Hierarchical Representations for Long-Form Narratives](https://mlsanity.com/paper/2606.20900/) (2026)
- [DocAtlas: Long-Document Understanding as Mutable-State Interaction](https://mlsanity.com/paper/2608.07527/) (2026)
- [DocClaw: A Unified Agentic System for Intelligent Document Processing](https://mlsanity.com/paper/2608.18685/) (2026)
- [Fidelity Is Not Enough: Dispatch-Level Instrumentation for Agentic Datasheet Extraction](https://mlsanity.com/paper/2608.28439/) (2026)
- [Do Coding Agents Reuse Existing Code or Reinvent the Wheel?](https://mlsanity.com/paper/2609.35357/) (2026)
- [TrajLong: Co-Designing Agentic and Long-Context Supervision for Mid-Training](https://mlsanity.com/paper/2610.04973/) (2026)

## Abstract (arXiv)

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.