Chinese historical documents are invaluable cultural treasures, but their complexity poses significant comprehension challenges for non-specialists. Despite the success of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) in modern document tasks, their capabilities in Long (full-book) Historical Do...
Zeyu Shan, Yong-Xin Shi, Shi-Hang Wu et al.· 0 citations
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
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 discret...