HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering

• URL: http://arxiv.org/abs/2605.29606v1
• Date: Thu, 28 May 2026 08:42:21 GMT
• Title: HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering
• Abstract summary: HiKEY is a hierarchical tree-based multimodal retrieval framework that elevates document hierarchy to a first-class retrieval signal.<n> Experiments on ODQA benchmarks demonstrate that HiKEY significantly outperforms page- and chunk-based baselines.
• Score: 19.052998569565627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation (RAG) for document-based Open-domain Question Answering (ODQA) on large-scale industrial corpora faces two critical bottlenecks: routing failure in locating the correct document and evidence fragmentation in integrating scattered information. Existing approaches relying on flat text chunks or page-level images inherently struggle to (i) precisely pinpoint the target document among thousands of candidates and (ii) organically connect multimodal evidence, such as tables and figures, within a limited token budget. To address these challenges, we propose HiKEY, a hierarchical tree-based multimodal retrieval framework that elevates document hierarchy to a first-class retrieval signal. Instead of simple chunking, HiKEY reconstructs a logical heterogeneous graph via Document Hierarchical Parsing (DHP), explicitly encoding parent-child relationships. Adopting a hierarchical coarse-to-fine strategy, the framework (1) performs global routing to rapidly prune the search space using hierarchical indexing, and (2) conducts fine-grained retrieval to rank sections by employing a multimodal fusion strategy that captures the most discriminative evidence. Finally, HiKEY assembles a token-efficient evidence subgraph via a hybrid structural-semantic packing strategy. Experiments on ODQA benchmarks demonstrate that HiKEY significantly outperforms page- and chunk-based baselines, improving retrieval recall by up to 12.9% and end-to-end QA performance by up to 6.8%.

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