arXiv:2511.22521v3 Announce Type: replace-cross Abstract: Document visual question answering requires models not only to answer questions correctly, but also to precisely localize answers within complex document layouts. While large vision-language models (VLMs) achieve strong spatial grounding, their inference cost and latency limit real-world deployment. Compact VLMs are more efficient, but they often suffer substantial localization degradation under standard fine-tuning or distillation. To address this gap, we propose DocVAL, a validated chain-of-thought (CoT) distillation framework that tr
The proliferation of complex document layouts and the drive for more efficient AI deployments are pushing research into advanced VQA models that balance accuracy and computational cost.
This development addresses critical limitations in deploying powerful VLM capabilities for document understanding, enabling broader applications in enterprise and specialized fields that rely heavily on visual document analysis.
The ability to distill sophisticated VLM capabilities into more compact, efficient models without significant loss of localization accuracy changes the landscape for real-world VQA adoption and operational efficiency.
- · AI model developers
- · Enterprise document processing
- · Companies using VQA for data extraction
- · Developers of compact VLMs
- · High-latency VQA solutions
- · Companies reliant on expensive, large VLM inference
More efficient and accurate document visual question answering becomes widely available for commercial applications.
This leads to increased automation in tasks requiring detailed document analysis and information extraction.
Reduced operational costs and higher accuracy could accelerate the digital transformation of document-heavy industries, potentially impacting white-collar work requiring manual data validation.
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Read at arXiv cs.AI