## 6 Conclusions  
We introduced CLEAR (Cross-document Link-Enhanced Attention for Relations), addressing critical limitations in cross-document relation extraction through two key innovations: the Relation-aware Context Filter, incorporating relation type descriptions; and the Path Distance-Weighted Attention (PDWA) mechanism, modeling semantic distances between document paths. In summary, CLEAR achieves significant improvements over state-of-the-art baselines: 68.42% F1 score (3.40% improvement over REIC) on CodRED and competitive performance on ECB+ cross-domain evaluation. Ablation studies confirm both components' contributions, with relation-aware filtering preserving crucial relation-specific evidence and PDWA ensuring semantically similar paths receive higher attention weights. From a theoretical perspective, this study (1) establishes a framework for incorporating relation-specific information into cross-document context filtering, (2) introduces semantic distance weighting in cross-document attention mechanisms, and (3) demonstrates enhanced model interpretability through relation-aware representations. Practically, CLEAR offers value across domains: knowledge graph construction benefits from reduced manual annotation overhead, business intelligence gains enhanced cross-document reasoning capabilities, and NLP developers can integrate the modular architecture into existing pipelines with minimal changes. The CLEAR framework offers substantial practical value for stakeholders in document relation extraction by addressing diverse needs across domains. For knowledge graph construction, its relation-aware filtering capability reduces manual annotation overhead by automatically identifying relevant relational contexts in cross-document settings. Business intelligence analysts can enhance their analysis of competitive landscapes and market trends through CLEAR's cross-document reasoning, which synthesizes insights from heterogeneous data sources. Academic researchers benefit from the framework's interpretable attention mechanisms, facilitating systematic investigation of complex relationship patterns in the scientific literature. Legal professionals may employ CLEAR to efficiently map connections between entities and documents in large legal corpora, while NLP developers can integrate its modular architecture into existing pipelines, with configurable components enabling domain-specific customization. Limitations include reliance on predefined relation schemas, potential performance degradation with sparse bridge entity distributions, and assumptions about document connectivity through shared entities. Future work should explore flexible relation representation learning and implicit relationship modeling to address these constraints. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**MUC**} & \multicolumn{3}{c}{**B\({}^{3}\)**} \\ \cline{2-7}  & **R** & **P** & **F1** & **R** & **P** & **F1** \\ \hline GPT-4 & 79.8 & 78.0 & 78.9 & 76.3 & 78.1 & 77.2 \\ [PERSON] et al. (2023) [32] & 80.0 & 87.3 & 83.5 & 79.6 & 85.4 & 82.4 \\
**Our method (CLEAR)** & **86.4** & **84.1** & **85.2** & **87.1** & **84.5** & **82.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Cross-domain evaluation results on ECB+ dataset. **Author Contributions:** Investigation J.Z. ; project administration, Y.S. ; resources, T.T. ; supervision, J.Z. ; validation, T.T. ; writing--original draft, Y.S. ; data curation, Y.S. ; writing--review and editing, J.Z. ; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is supported in part by the National Natural Science Foundation of China (62106179). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author.