Author

Lianwen Jin

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#artificial intelligence Preprint Aug 2026

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

DocIntent, a training-free Answerability-Guided Agentic Restoration framework, which first assesses question answerability, then identifies task-relevant degradations and selectively invokes restoration tools, and which consistently improves the average score and consistency of different open- and closed-source MLLMs.

Zi-Han Huang, Shi-Hang Wu, Jun-Le Liu et al. · 0 citations

Jul 2026

One Patch Is Enough: Reinforcement-Optimized Visual Token Grounding for MLLM-Based Scene Text Spotting

This work proposes Single-Patch Text Spotting (SPaTS), a vision-centric framework that routes each text instance through a single anchor visual token and then recovers geometry via full-image refinement and introduces Single-Patch Selective Optimization (SPaSO), a reinforcement learning framework that optimizes discrete visual-token selection using patch-level rewards.

Rui Tang, Wentao Yang, Peirong Zhang et al. · arXiv.org · 0 citations