Wai-Kit Wong

DocMIDE: Learning Multi-Hop Implicit Derivation in Visually Rich Documents

DocMIDE is introduced, a fine-tuning framework that trains compact vision-language models to retrieve visual evidence explicitly before deriving an answer, and constrains generation to a plan-retrieve-derive structure and optimizes it with Group Relative Policy Optimization under a four-component, rule-based reward.