arXiv:2604.02371v2 Announce Type: replace-cross Abstract: Visual long-document understanding is critical for enterprise, legal, and scientific applications, yet the best performing open recipes have not explored reasoning, a capability which has driven leaps in math and code performance. We introduce a synthetic data pipeline for reasoning in long-document understanding that generates thinking traces by scoring each page for question relevance, extracting textual evidence and ordering it from most to least relevant. We apply SFT to the resulting traces within \texttt{ } tags, gated by a \textt
The paper addresses the current limitations in reasoning capabilities for long-context visual document understanding, a critical gap for real-world enterprise, legal, and scientific applications.
Improving visual document understanding with internalized reasoning can significantly enhance the automation of complex analytical tasks across various industries.
AI models will become more adept at processing and drawing conclusions from extensive visual documents, moving beyond simple information extraction to true comprehension.
- · Enterprise AI providers
- · Legal tech firms
- · Scientific research institutions
- · Knowledge workers
- · Manual data processing services
- · Basic OCR solutions
- · Companies reliant on human data analysis
Increased efficiency and accuracy in processing large volumes of visual information, such as contracts or research papers.
Acceleration of research and development in fields heavily dependent on document analysis, leading to faster innovation cycles.
Potential for new AI-driven business models centered on advanced document intelligence and automated decision support systems.
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Read at arXiv cs.AI