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MoDora: Tree-Based Semi-Structured Document Analysis System

Summary: MoDora transforms OCR fragments into layout-aware components and a Component-Correlation Tree that preserves hierarchy, spatial distinctions, and cross-region links. Question-type-aware retrieval combines grid-based location search with LLM-guided semantic pruning, improving QA accuracy by 5.97–61.07%. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
h4649af49d421453b
Venue
SIGMOD
Year
2026
Pagerank
5.2283159e-05
Overall Rank
9,066 | 39.05%
DOI
10.1145/3802089

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{xu_sigmod26,
        title = {{MoDora: Tree-Based Semi-Structured Document Analysis System}},
        author = {Xu, Bangrui and Yao, Qihang and Tang, Zirui and Zhou, Xuanhe and He, Yeye and Yu, Shihan and Xu, Qianqian and Wang, Bin and Li, Guoliang and He, Conghui and Wu, Fan},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802089},
        url = {https://dl.acm.org/doi/10.1145/3802089},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
11,011 MoDora: A Multimodal Document AI Assistant Harness 2026 VLDB 4.9793485e-05
11,029 Graph-Based Retrieval-Augmented Generation: Applications, Challenges, Solutions, and Opportunities 2026 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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