DocIntent: Answerability-Guided Agentic Restoration for Real-World Document Visual Question Answering

2026-08-29

AI Summary

针对文档视觉问答中现实世界退化问题，提出DocIntent框架，通过评估问题可回答性指导自适应恢复过程，无需额外预训练模型，提升多模态大语言模型性能。

Abstract

Real-world degradations such as blur, shadow, distortion, and moire patterns severely impair the document question-answering capabilities of Multimodal Large Language Models (MLLMs). Applying restoration tools before Visual Question Answering (VQA) is an intuitive solution. However, existing restoration approaches remain limited, as manually designing and executing restoration strategies is labor-intensive and requires domain expertise. Agentic restoration offers new possibilities for automation, yet existing frameworks primarily target natural images and pursue perceptual quality, overlooking that restoration should serve downstream tasks rather than optimize generic image quality metrics. To this end, we explore the value of agentic restoration for real-world degraded document VQA and propose DocIntent, a training-free Answerability-Guided Agentic Restoration framework. DocIntent first assesses question answerability, then identifies task-relevant degradations and selectively invokes restoration tools. A Comparison-Based Rollback mechanism validates each restoration step and reverts it when question-relevant evidence becomes less decipherable. The entire process requires no additional pretrained degradation classifier or image quality assessment model. Extensive experiments on the WildDoc benchmark show that DocIntent consistently improves the average score and consistency of different open- and closed-source MLLMs. The code and experimental data will be publicly available.

Problem

• Document Visual Question Answering
• Real-World Degradations
• Agentic Restoration
• Answerability-Guided

Innovation

• Answerability-Guided Agentic Restoration
• Comparison-Based Rollback
• Real-World Document VQA