## 4 Experimental Settings
### Baseline Model  
We compare our proposed model with six baseline models for effectiveness verification. * The pipeline model [9] is a multi-stage process, where each stage builds upon the previous one, consisting of the following steps: (1) First, extracting relational graphs within individual documents using a BERT-based model; (2) then, reasoning over potential connected entities to predict relations across documents based on the extracted graphs; and (3) finally, aggregating relation scores from head and tail entity representations to determine the final relation. * The end-to-end model introduced by [PERSON] et al. [9] offers a unified approach that processes text directly and predicts relations simultaneously, mitigating error propagation risks. This approach employs a four-stage architecture: (1) Encoding text with BERT to identify intra-document relations through distant supervision; (2) concatenating and tokenizing documents with entity markers to obtain contextualized representations; (3) applying selective attention mechanisms to synthesize information-rich paths and derive aggregated entity pair representations; and (4) feeding these representations into a neural network to predict relation distributions between entity pairs. * ECRIM [8] introduces the bridge entity concept and employs a four-stage processing pipeline: bridge entity-based context filtering, encoding, attention-based relation cap \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{2}{c}{**General Training**} & \multicolumn{2}{c}{**Model Architecture**} & \multicolumn{2}{c}{**PDWA Mechanism**} \\ \hline Batch size & 4 & Transformer layers & 3 & GumbekSoftmax temp (\(\tau\)) & 0.5 \\ Learning rate & \(3\times 10^{-5}\) & Embedding dimension & 768 & Distance normalization & L2 \\ Weight decay & \(1\times 10^{-5}\) & Hidden dimension & 768 & PDWA layers & 2 \\ Epochs & 30 & Attention heads & 12 & Path attention heads & 8 \\ Warmup steps & 1000 & Dropout rate & 0.1 & Distance threshold & 0.8 \\ Gradient clipping & 1.0 & & & & \\ \hline \multicolumn{2}{c}{**Context Filter**} & \multicolumn{2}{c}{**Auxiliary Training**} & \multicolumn{2}{c}{**Selection Parameters**} \\ \hline Direct co-occurrence & 0.1 & Auxiliary weight (\(\lambda\)) & 0.3 & Top k sentences & 20 \\ Indirect co-occurrence & 0.01 & Auxiliary learning rate & \(1\times 10^{-5}\) & Max path length & 3 \\ Potential co-occurrence & 0.001 & Entity type emb dim & 128 & Min entity distance & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Complete hyperparameter specifications for CLEAR framework. ture, and classification. At its core, the cross-path entity relation attention module computes relation representations between entity pairs, constructs a comprehensive entity relation matrix, applies Transformer self-attention to model complex interdependencies, and extracts path-specific target relation representations from the resulting Transformer layers. * MR.COD [22] employs a sophisticated multi-hop evidence retrieval approach for cross-document relation extraction through a three-phase process: (1) constructing a comprehensive graph representation where nodes comprise document passages and entities; then employing depth-first search algorithms to identify potential evidence paths; (2) scoring these paths using dual retrieval mechanisms--Dense Passage Retrieval (DPR) and Multi-hop Dense Retrieval (MDR)--to prioritize information-rich sequences; and (3) optimizing passage representations through dynamic length adjustment, either trimming redundant content or strategically appending contextual information.