## 1 Introduction
Adversarial training systematically exposes the system to challenging scenarios during learning, comparable to training staff with complex cases to enhance decision-making resilience under unusual circumstances. The first innovation, retriever consensus confidence fusion (RCCF), transforms document retrieval by introducing consensus-based fusion strategies that dynamically adapt to retriever agreement levels while providing confidence estimates for results. Unlike traditional fusion methods that apply fixed weights regardless of query characteristics, RCCF measures consensus between different retrievers and adjusts fusion strategies accordingly, amplifying high-consensus results while adopting conservative approaches for uncertain cases. This adaptive mechanism not only improves retrieval accuracy by up to 16.9% but also provides valuable confidence information that enables downstream applications to assess result reliability and implement effective quality control measures. The second core innovation, AM-HDGAT for text (adversarial mutual-attention hybrid-dimensional graph attention network for text), addresses the fundamental limitations of traditional document classification approaches that treat documents as isolated entities. This method models complex inter-document relationships through sophisticated graph structures while seamlessly integrating complementary feature representations from multiple abstraction levels. The approach combines high-dimensional semantic features derived from pre-trained language models with carefully engineered low-dimensional statistical features through a novel mutual-attention mechanism, enabling comprehensive document understanding by capturing both deep semantic meaning and surface-level characteristics. The integration of adversarial training components further enhances model robustness against potential security threats and distributional shifts, making the system particularly suitable for security-critical document processing applications. The AttenFlow architecture represents a significant advancement by synergistically integrating these innovations within a unified, scalable framework that addresses multiple technical challenges simultaneously. The system's multi-layer design ensures computational efficiency and horizontal scalability while supporting flexible deployment across diverse organizational contexts. Through a strategic integration of RCCF and AM-HDGAT components, AttenFlow creates a feedback-enhanced processing pipeline where retrieval confidence estimates inform classification decisions, and classification results provide a semantic context that continuously refines retrieval performance. This symbiotic relationship achieves substantial improvements in document retrieval accuracy, classification performance with an average F1-score improvement of 2.23%, and processing efficiency with a 26.1% reduction in processing time. Comprehensive experimental validation across multiple benchmark datasets and real-world deployment scenarios demonstrates the substantial effectiveness of our proposed innovations. The system consistently outperforms established methods across diverse tasks while maintaining 82.4% performance retention under adversarial conditions and achieving 95.7% user satisfaction in production environments processing over 10,000 documents monthly. By systematically addressing the fundamental limitations of existing approaches through principled technical solutions, AttenFlow establishes a new paradigm for intelligent document processing that seamlessly combines computational efficiency, accuracy optimization, and security assurance within a production-ready framework suitable for large-scale organizational deployment.