## 2 Related Work  
Research in document processing and workflow management has yielded significant advancements in efficiency, accuracy, and system performance, laying the groundwork for our work [10; 18; 19; 20]. However, critical limitations persist, particularly in modeling document relationships and adaptive information fusion, which our proposed AttenFlow architecture specifically addresses. In document information extraction and clustering, substantial efforts focus on handling unstructured data. [PERSON] et al. emphasized AI's potential for automating extraction but identified significant challenges, such as dataset creation difficulties and the need for robust validation, especially for diverse layouts [21]. While valuable, this work primarily addresses single-document processing and does not tackle the complexities arising from inter-document dependencies within a corpus, a gap our AM-HDGAT module aims to fill. [PERSON] et al. introduced an innovative text clustering method by combining the hybrid swarm intelligence algorithm with K-means to enhance efficiency and reliability [22], and [PERSON] conducted a thorough investigation into meta-heuristic optimization algorithms for text clustering, demonstrating AI's role in improving these tasks [23]. These methods improve intra-document organization but, like most traditional approaches, treat documents as isolated entities, failing to capture the contextual relationships between documents that are crucial for holistic understanding and flow management, a core focus of our graph-based AM-HDGAT. Within workflow management, researchers have proposed solutions for optimizing document circulation. [PERSON] et al. introduced a blockchain-based solution for digitizing land titles, offering advantages like faster processing, cost reduction, and transparency [24]. [PERSON] applied CERT values and the Delphi method to evaluate workflow system performance, aiding process improvement [25]. [PERSON] focused on object-oriented hypermedia design for a web-based electronic document management system, achieving high user satisfaction [26]. [PERSON] et al. compared workflow management solutions, emphasizing the Eclipse Arrowhead framework for microservice architecture [27]. These contributions enhance system design and process management but typically operate at a macro level or focus on specific document types, often overlooking the need for fine-grained, dynamic modeling of document relationships and content-dependent flow within complex document streams--a challenge our integrated AttenFlow framework tackles. Advances in document retrieval have been transformative. [PERSON] et al. proposed a deep relevance matching model that significantly improved ad hoc retrieval ranking through neural architectures [11]. [PERSON] et al. developed dense passage retrieval methods that achieved breakthrough performance in open-domain question answering using dense vectors [12]. For retrieval fusion, [PERSON] et al. introduced the reciprocal rank fusion (RRF) method, which effectively combines results from multiple systems without requiring score normalization [14], and [PERSON] et al. provided comprehensive insights into pre-trained Transformers for text ranking [13]. While these represent major strides, traditional fusion approaches like RRF have inherent limitations, as highlighted by [PERSON] et al. : they typically ignore the consensus patterns among different retrievers and apply fixed fusion strategies regardless of retriever agreement levels [13]. Furthermore, they lack mechanisms to assess the confidence or consensus strength of the fused results. This rigidity and absence of confidence estimation hinder optimal performance, especially when retrievers disagree significantly or results are ambiguous. This critical shortcoming is directly addressed by our proposed RCCF method, which revolutionizes fusion by dynamically evaluating retriever consensus and providing essential confidence estimates. The application of attention mechanisms has revolutionized understanding within documents.