Internalized Reasoning for Long-Context Visual Document Understanding
📰 ArXiv cs.AI
Internalized reasoning improves long-context visual document understanding by generating thinking traces and scoring page relevance
Action Steps
- Generate synthetic data pipeline for reasoning in long-document understanding
- Score each page for question relevance
- Extract textual evidence and order it from most to least relevant
- Use thinking traces to improve model performance
Who Needs to Know This
AI engineers and researchers working on document understanding and visual question answering tasks can benefit from this approach to improve model performance
Key Insight
💡 Internalized reasoning can drive significant improvements in document understanding tasks
Share This
💡 Internalized reasoning boosts long-context visual doc understanding
Key Takeaways
Internalized reasoning improves long-context visual document understanding by generating thinking traces and scoring page relevance
Full Article
Title: Internalized Reasoning for Long-Context Visual Document Understanding
Abstract:
arXiv:2604.02371v1 Announce Type: 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 le
Abstract:
arXiv:2604.02371v1 Announce Type: 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 le
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