# DocSeeker: Structured Visual Reasoning and Evidence Localization to Tackle Long Document Understanding Challenges

> This article introduces the DocSeeker framework, which addresses the low signal-to-noise ratio and weak supervision signals of multimodal large models in long document understanding through a three-stage workflow of "Analysis-Localization-Reasoning" and a two-stage training strategy, enabling robust generalization from short-document training to ultra-long documents.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-14T14:39:26.000Z
- 最近活动: 2026-04-15T01:53:54.809Z
- 热度: 130.8
- 关键词: DocSeeker, 长文档理解, 视觉推理, 证据定位, 多模态大模型, 知识蒸馏, 强化学习, RAG
- 页面链接: https://www.zingnex.cn/en/forum/thread/docseeker
- Canonical: https://www.zingnex.cn/forum/thread/docseeker
- Markdown 来源: floors_fallback

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## DocSeeker Framework: Core Solution to Tackle Long Document Understanding Challenges

The DocSeeker framework addresses the low signal-to-noise ratio and weak supervision signals of multimodal large models in long document understanding through a three-stage workflow of "Analysis-Localization-Reasoning" and a two-stage training strategy, enabling robust generalization from short-document training to ultra-long documents. This framework focuses on structured visual reasoning and evidence localization, providing an effective technical path for long document processing.

## Two Core Challenges in Long Document Understanding

In long document understanding, the performance of existing multimodal large models drops sharply as document length increases, rooted in two points: 1. **Signal-to-Noise Ratio Dilemma**: Key information (signal) is overwhelmed by a large amount of irrelevant content (noise); 2. **Scarcity of Supervision Signals**: Existing datasets only provide final answers, lacking annotations of evidence sources, making it difficult for models to learn to localize evidence.

## DocSeeker's Solutions and Technical Innovations

DocSeeker adopts a structured visual reasoning paradigm, with a three-stage workflow including: 1. **Analysis Stage**: Understand the problem requirements and form a search strategy; 2. **Localization Stage**: Explicitly output evidence positions (page/region/text level) to enhance interpretability and accuracy; 3. **Reasoning Stage**: Generate answers based on localized evidence. The training uses a two-stage strategy: first, fine-tune using supervision data generated from a teacher model via knowledge distillation, then optimize evidence localization and answer correctness using evidence-aware reinforcement learning. Innovations include **Evidence-Guided Resolution Allocation** (dynamically allocate computing resources) and **Natural Synergy with RAG Systems** (micro-localization + macro-retrieval).

## Experimental Validation: Proof of Performance and Generalization Ability

DocSeeker performs excellently in multiple benchmark tests: 1. **Leading Performance**: Outperforms existing methods, especially with obvious advantages in complex localization problems; 2. **Robust Generalization**: After short-document training, it can generalize to ultra-long documents of hundreds of pages; 3. **Domain Transfer**: Performs well in out-of-domain tasks. Ablation experiments verify: Removing the explicit localization or reinforcement learning stage leads to a significant drop in performance; Uniform resolution processing is inferior to evidence-guided allocation in both efficiency and performance.

## Practical Application Scenarios of DocSeeker

DocSeeker can be applied in multiple fields: 1. **Legal Document Analysis**: Quickly locate contract clauses and compare versions; 2. **Financial Report Review**: Extract key financial report indicators and identify risks; 3. **Medical Record Processing**: Locate patient medical history information to support clinical decisions; 4. **Scientific Research Assistance**: Assist in literature reviews and accelerate knowledge discovery.

## Limitations, Future Directions, and Conclusion

**Limitations**: High computing cost for processing ultra-long documents; mainly supports English; complex multi-hop reasoning ability needs to be enhanced; real-time interaction response delay needs optimization. **Future Directions**: Optimize computing efficiency, expand multi-language support, enhance multi-hop reasoning, and improve real-time performance. **Conclusion**: DocSeeker effectively addresses the core challenges of long document understanding through structured reasoning and evidence localization, providing a foundation for building trustworthy AI systems and serving as an important technical path in the era of information explosion. Paper link: http://arxiv.org/abs/2604.12812v1