Multimodal long-document question answering is difficult because relevant information is scattered across pages and formats. Models must locate sparse evidence, interpret heterogeneous content, and reason over what they collect.
The paper introduces DocPilot, an approach centered on interactive agent orchestration and training. It is designed to improve performance on this task, where even advanced vision-language models struggle.
The abstract does not provide experimental details, but the framing suggests a focus on teaching agents to navigate and reason over long documents rather than relying on a single forward pass.