## 5 Conclusions
The consensus-based fusion paradigm introduced by RCCF provides a template for improving reliability in various information retrieval applications, while the hybrid-dimensional graph attention mechanism of AM-HDGAT offers promising directions for text analysis tasks beyond classification. The successful integration of adversarial training demonstrates the feasibility of developing robust NLP systems for security-critical applications. Building on the current architecture's demonstrated strengths in consensus-driven fusion and hybrid graph-based modeling, we will prioritize enhancing AttenFlow's scalability and adaptability. Our central hypothesis is that the parallelization and sparsification of graph operations can allow us to achieve near-linear scalability without compromising accuracy. To test this, we plan to develop distributed graph construction algorithms using mini-batch partitioning and asynchronous message passing, optimized for deployment on GPU clusters. These algorithms will be evaluated on corpora exceeding one million documents with the goal of maintaining <20% latency overhead compared to baseline settings. We also aim to implement dynamic graph update mechanisms using incremental node-edge integration and temporal decay functions to model evolving inter-document relationships in near real time. To overcome current monolingual limitations, we hypothesize that integrating cross-lingual pre-trained models with language-agnostic attention mechanisms will enable robust document modeling across diverse languages. We will construct a multilingual processing pipeline by extending RCCF's consensus scoring to handle token alignment mismatches and by adapting AM-HDGAT with cross-lingual attention modules that share parameters across aligned semantic spaces. A staged evaluation will be conducted on bilingual Chinese-English datasets, targeting <10% accuracy degradation compared to monolingual settings, with scalability tested on administrative corpora from multilingual regions. Finally, we envision applying AttenFlow's core mechanisms to adjacent domains with rich inter-document structures, such as legal reasoning, scientific discovery support, and enterprise knowledge mining. We hypothesize that the existing architecture can generalize with minor adaptations--such as domain-specific token embeddings and schema-aware edge definitions. For legal document analysis, we plan to incorporate citation graphs and apply RCCF over verdict similarity networks; for biomedical literature, we will integrate external ontologies (e.g., MeSH) into AM-HDGAT's relational graph to guide entity-level reasoning. These domain-specific extensions will be benchmarked on datasets such as LeCaRD and PubMed to evaluate transferability and robustness under knowledge-intensive scenarios. **Author Contributions:** Conceptualization, L.C. ; Methodology, X.Z. and B.H. ; Software, X.Z. and Q.S. ; Validation, S.L. and Q.S. ; Formal analysis, X.Z. and S.L. ; Investigation, Q.S. ; Resources, L.C. ; Data curation, B.H. ; Writing--original draft, X.Z., B.H. and S.L. ; Writing--review & editing, B.H. ; Visualization, X.Z., B.H. and Q.S. ; Supervision, S.L., Q.S. and L.C. ; Project administration, S.L. and L.C. ; Funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data are not publicly available due to privacy restrictions. **Conflicts of Interest:** The authors declare no conflicts of interest.