## 1 Introduction  
In the contemporary digital landscape, the exponential growth of organizational documentation has created unprecedented challenges for efficient document management and circulation systems [1]. Traditional document processing workflows, characterized by manual routing, paper-based approvals, and sequential processing, are increasingly inadequate for meeting the demands of modern administrative environments [2]. These conventional approaches suffer from inherent limitations including prolonged processing cycles, susceptibility to human error, limited scalability, and significant security vulnerabilities [3]. Moreover, the environmental impact of paper-intensive workflows, coupled with the substantial costs associated with physical storage and retrieval systems, has intensified the urgency for digital transformation in document management practices [4, 5, 6]. The transition towards automated document processing systems represents a critical paradigm shift that addresses both operational efficiency and sustainability concerns [7]. However, this transformation introduces complex technical challenges that extend beyond simple digitization. Effective automated document systems must not only replicate traditional workflow capabilities but also enhance them through intelligent processing, predictive routing, and adaptive classification mechanisms [8]. The core technical challenges in this domain center on two fundamental problems: achieving accurate and efficient document retrieval from large-scale repositories, and performing reliable document classification that can adapt to diverse content types and organizational contexts [9, 10]. Current document retrieval systems predominantly rely on either sparse retrieval methods, such as BM25, which excel at exact term matching but struggle with semantic understanding, or dense retrieval approaches that capture semantic relationships but may miss important keyword-based signals [11, 12], while hybrid approaches attempt to combine these complementary strengths, existing fusion methods suffer from critical limitations: they apply fixed weighting schemes regardless of query characteristics, ignore consensus information between different retrievers, and provide no confidence estimation for results [13, 14]. These shortcomings result in suboptimal retrieval performance and unreliable results, particularly crucial in document management applications where accuracy and reliability are paramount. Similarly, traditional document classification approaches treat documents as independent entities, failing to leverage the rich relationships and dependencies that exist within document corpora [15]. This limitation becomes particularly pronounced in organizational settings where documents often reference each other, belong to related categories, or share common themes and contexts. Furthermore, the security-critical nature of many document management applications demands robust classification systems that can withstand adversarial attacks and maintain performance under various threat scenarios [16, 17]. To address these fundamental challenges, this paper introduces AttenFlow, a novel context-aware architecture that revolutionizes automated document processing through two core technical innovations designed to provide intuitive, human-interpretable functionality. To clarify the foundational concepts underlying our approach, mutual-attention mechanisms operate analogously to how experienced clerks simultaneously consider document content and routing destinations when making processing decisions, enabling joint analysis of document semantics and target processing paths. High-dimensional semantic features capture deep document meanings, including contextual relationships and implicit intentions, similar to how experts understand complete document significance rather than merely identifying keywords.