TAP-RAG: Task-Aware Policy Control for Long-Document Multimodal QA
TAP-RAG is a new framework for long-document multimodal question answering that introduces a task-aware policy controller to dynamically select evidence strategies for each query. The system combines textual, structural, and visual evidence using specialized modules and a guarded synthesis stage. TAP-RAG achieves state-of-the-art accuracy on DocBench and MMLongBench-Doc, outperforming a multimodal-RAG baseline by +9.1 and +4.5 points, respectively.
Why it matters: This work demonstrates that query-adaptive evidence selection can substantially improve the accuracy of multimodal retrieval-augmented generation on long documents.
Full story at: arXiv Computer Vision ↗