DoCoG: Mask-based Multi-Type Grounded Chain-of-Thought for Document QA

Existing methods for Visual Document Question Answering(DocVQA) often predict answers without explicit intermediate analysisor produce ungrounded intermediate steps. The few approaches whichemploy grounding produce rectangular bounding boxes which fail to ac-curately represent complex structures such as pie-chart wedges, flowchartnodes, and curved text. We present DoCoG, a mask-based multi-typegrounded step-wise Chain-of-Thought architecture for Document QA.The model supports multi-type grounding, i.e., it localizes both textualcontent and fine-grained graphical elements such as legends, pie slices,and flowchart nodes. It generates step-wise intermediate outputs and fi-nal answers grounded through precise segmentation masks, enabling con-sistent evidence alignment. DoCoG integrates a Vision-Language Modelwith a promptable segmenter via a novel Grounding Interaction Module(GIM). During decoding, special [GND] tokens trigger mask prediction,linking each step and the final answer to supporting evidence. GIM re-fines [GND] tokens during decoding to improve spatial consistency andalignment. We introduce DoCoG-QA, a large-scale diverse dataset com-prising 325K documents and 1.5M step-wise grounded QA pairs. Wefurther release DoCoG-PQA with 20K preference pairs to improve align-ment using Direct Preference Optimization (DPO), and DoCoG-Bench,a human-verified benchmark. Experiments show that DoCoG achievesstate-of-the-art performance across multiple benchmarks, improving an-swer accuracy and grounding precision.