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HIEVI-RAG is introduced, a hierarchical, evidence-driven multimodal RAG framework for closed-domain document understanding that significantly outperforms existing open-source baselines and exceeds the strongest reported baseline by an average of 8.05% in accuracy.
HierDoc, a hierarchical evidence-routing framework that formulates long-document evidence acquisition as two-stage set prediction from pages to regions, achieves state-of-the-art or competitive performance among open-weight systems, improving LongDocURL by 16.87% relative to the strongest reported open-weight baseline.
Experiments across various datasets indicate that the proposed method achieves superior reasoning accuracy without compromising, and even facilitating, the overall accuracy, and ablation studies show that the proposed mechanisms can provide flexible control interfaces for the tradeoff between the reasoning accuracy and efficiency of LRMs.