## 5 Results and Analyses
### Experimental Analyses  
#### 5.2.1 Ablation Experiments  
This section presents ablation experiments designed to validate the contribution of each module in the proposed CLEAR model. The following variants were implemented to systematically evaluate the impact of individual components:  
1. Input construction (-IC): The input construction module was replaced with the baseline model's input method to assess the importance of the proposed input encoding strategy. 2. Bridge entity removal (-BR): Bridge entities were removed from the relation matrix construction process to evaluate their role in cross-document reasoning. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **F1 (\%)** & **AUC (\%)** \\ \hline GPT-3.5-turbo & 28.05 & 31.24 \\ GPT-4 (zero-shot) & 42.18 & 44.67 \\ GPT-4 (few-shot) & 48.34 & 51.28 \\ InstructUIE [13] & 51.23 & 53.89 \\ \hline
**CLEAR (RoBERTa)** & **68.42** & **68.78** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with large language models on CodRED test set. 3. Cross-path interaction removal (-CP): Cross-document path relation interactions were eliminated, retaining only intra-document path interactions, to examine the necessity of inter-path information exchange. 4. Threshold loss replacement (-TL): The global threshold loss was replaced with cross-entropy loss to investigate the effectiveness of adaptive threshold learning. 5. Uniform attention (-UA): The distance-based weighting mechanism in PDWA (Path Distance-Weighted Attention) was removed, and uniform attention was assigned to all document path instances to analyze the significance of distance-aware attention allocation. 6. Relation-aware context filtering removal (-RF): The relation-aware filtering component was replaced with traditional entity-only filtering, eliminating the use of relation type descriptions during context selection. This ablation evaluates our claim that incorporating relation-specific information during filtering significantly enhances the model's ability to identify subtle but crucial evidence for complex relations. 7. Distance weighting mechanism removal (-DW): The semantic distance calculation between document paths was eliminated from the attention mechanism, resulting in path representations that do not account for varying semantic distances in the feature space. This ablation tests our hypothesis that explicitly modeling semantic distances between document paths is crucial for effective reasoning across documents. The results of the ablation experiments are summarized in Table 5. The key findings are as follows: (1) -IC: Replacing the proposed input construction module with the baseline method led to a performance decline of 1.46 percentage points in F1 and 1.56 in AUC. This underscores the importance of selectively integrating bridge entity-related information during input encoding for effective relation extraction. (2) -BR: The exclusion of bridge entities from the relation matrix construction process resulted in the most significant performance degradation (3.11 decrease in F1 score and 3.10 decrease in AUC). This highlights their critical role in facilitating cross-document relation reasoning by connecting disparate pieces of information. (3) -CP: Eliminating cross-document path interactions while retaining intra-document path relations caused a substantial drop in performance (2.09 reduction in F1 score and 2.17 reduction in AUC). This demonstrates the necessity of modeling interactions across different document paths to capture complex relational dependencies. (4) -TL: Substituting the global threshold loss with cross-entropy loss led to a marginal but consistent performance decline (0.75 decreases in F1 score and 0.87 decrease in AUC). This suggests that adaptive threshold learning provides a more nuanced optimization objective for cross-document relation extraction compared to traditional loss functions.