Document QA

Document question answering studies how models find and combine evidence in sources such as PDFs, reports, forms, and document collections. Common approaches include retrieval-augmented generation, layout-aware and multimodal models, hierarchical indexes, and agents that navigate pages or inspect selected regions. Benchmarks increasingly test long-context and multi-document reasoning, numerical and temporal questions, and whether answers are supported by precise citations or visual evidence. Models still struggle to gather dispersed evidence and interpret complex layouts, while systems trade accuracy against latency, cost, and reliability.