Retrieval-augmented generation (RAG) for document-based open-domain question answering (ODQA) over large industrial corpora faces two core bottlenecks: routing to the correct document and combining scattered evidence.Flat text chunks and page-level images often fail to (i) identify the right document among thousands of candidates and (ii) connect multimodal evidence, such as tables and figures, within a fixed token budget.We propose HiKEY, a hierarchical tree-based multimodal retrieval framework that treats document hierarchy as a first-class retrieval signal.Rather than simply chunking text, HiKEY uses Document Hierarchical Parsing (DHP) to reconstruct a logical heterogeneous graph with explicit parent-child relations.At query time, HiKEY follows a hierarchical coarse-to-fine process: it first performs global routing with hierarchical indexes to prune the corpus, and then ranks sections with a multimodal fusion strategy that selects the most discriminative evidence.It finally builds a token-efficient evidence subgraph through hybrid structural-semantic packing.Experiments on ODQA benchmarks show that HiKEY outperforms page-and chunk-based baselines, improving retrieval recall by up to 12.9 points and end-to-end QA by up to 6.8 points.

RAG-based QA has emerged as a powerful method for processing long industrial documents.However, conventional text chunking approaches often neglect the complex structures of long industrial documents, causing information loss and reduced answer quality.To address this, we introduce MultiDocFusion, a multimodal chunking pipeline that integrates: (i) detection of document regions using visionbased document parsing, (ii) text extraction from these regions via OCR, (iii) reconstruction of document structure into a hierarchical tree using large language model (LLM)based document section hierarchical parsing (DSHP-LLM), and (iv) construction of hierarchical chunks through DFS-based Grouping.Extensive experiments across industrial benchmarks demonstrate that MultiDocFusion improves retrieval precision by 8-15% and ANLS QA scores by 2-3% compared to baselines, emphasizing the critical role of explicitly leveraging document hierarchy for multimodal document-based QA.These significant performance gains underscore the necessity of structure-aware chunking in enhancing the fidelity of RAG-based QA systems.