Agentic Harmonization for Document AI

Organizations building document AI systems often combine datasets that use incompatible annotation standards, creating hidden quality and performance issues during model training. This research explains how agentic harmonization uses vision-language reasoning to align document layout annotations, bounding box granularity, and semantic labels before fine-tuning object detection models. The paper demonstrates that resolving annotation inconsistencies improves detection accuracy, spatial consistency, table extraction quality, and representation learning across heterogeneous document datasets. For AI and platform leaders, the key implication is that scalable document intelligence depends not only on model architecture, but also on harmonized supervision and consistent data semantics across tra