面向复杂文档RAG的结构保真与质量门控技术研究综述
摘要
成系统多在解析后直接切分入库,使内容识别和结构识别误差沿切分、索引、检索与生成链路放大。围绕文档解析、
结构感知切分、动态检索和端到端评价研究,梳理复杂文档进入知识库前的关键技术,提出结构保真驱动的质量门
控闭环框架。该框架以多维结构保真度作为贯穿解析诊断、选择性修复、知识准入、检索路由、证据回溯与反馈校
准的控制信号,并给出准入、降级准入、修复和拒绝四类决策。所述框架为纯文献综合所得,旨在形成可检验的工
程研究议程,为企业文档知识库的可靠构建提供统一分析视角。
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[1]LEWIS P, PEREZ E, PIKTUS A, et al. RetrievalAugmented Generation for Knowledge-Intensive NLP
Tasks[J]. Advances in Neural Information Processing Systems,
2020, 33: 9459-9474.
[2]WANG B, XU C, ZHAO X, et al. MinerU: An
Open-Source Solution for Precise Document Content
Extraction[EB/OL]. arXiv:2409.18839, 2024.
[3]MA X, LIN S C, LI M, et al. Unifying Multimodal
Retrieval via Document Screenshot Embedding[C]//
Proceedings of EMNLP 2024. 2024: 6492-6505.
[4]YANG C, VU D K, NGUYEN M T, et al.
SuperRAG: Beyond RAG with Layout-Aware Graph
Modeling[C]//Proceedings of NAACL 2025 Industry Track.
2025: 544-557.
[5]YU R, WANG T, LIU R, et al. SF-RAG: StructureFidelity Retrieval-Augmented Generation for Academic
Question Answering[EB/OL]. arXiv:2602.13647, 2026.
[6]SOURATI Z, WANG Z, LIU M M, et al. LADRAG: Layout-aware Dynamic RAG for Visually-Rich
Document Understanding[C]//Proceedings of ACL 2026.
2026: 15945-15968.
[7]YU L, LIU H, WANG K, et al. ParseFixer: An
Agentic Framework for Document Parsing via Selective
Multimodal Correction[EB/OL]. arXiv:2606.11977, 2026.
[8]ZHOU B, XING H, CHEN Y, et al. MPDocBenchParse: Benchmarking Practical Multi-page Document
Parsing[EB/OL]. arXiv:2605.22100, 2026.
[9]ES S, JAMES J, ESPINOSA ANKE L, et al.
RAGAs: Automated Evaluation of Retrieval Augmented
Generation[C]//Proceedings of EACL 2024 System
Demonstrations. 2024: 150-158.
[10]MLCOMMONS. Introducing the MLPerf Endto-End RAG Inference Benchmark[EB/OL]. 2026-08-26.
https://mlcommons.org/2026/08/endtoend-inference/.
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