## 1 Introduction  
Relation extraction (RE) is a fundamental task in natural language processing that identifies semantic relationships between entities in text, serving as a critical component for knowledge graph construction, question answering, and information retrieval systems [1, 2, 3]. While conventional RE approaches have focused primarily on single-sentence or document contexts [4, 5, 6, 7], real-world knowledge is often distributed across multiple documents, necessitating cross-document reasoning capabilities. Cross-document relation extraction (CodRE) extends the relation extraction paradigm beyond single-document boundaries, aiming to identify relationships between entities that appear in different documents. Unlike traditional relation extraction that is confined to single texts, CodRE must reason across multiple document paths that connect the target entities, often through intermediate \"bridge entities\" [8]. This task presents unique challenges as relevant evidence is scattered across disparate sources, and document paths are typically lengthy with considerable noise. Consider determining whether \"AlphaGo\" was developed by \"Google\". One document path might connect texts mentioning \"AlphaGodefeated [PERSON] in 2016\" and \"DeepMind's AI system made history in Seoul\", while another path might link documents stating \"DeepMind developed advanced game-playing algorithms\" and \"Google acquired DeepMind in 2014 for $500 million\". This shows that effective context filtering must consider both entities and their potential relations. Second, document paths have varying semantic relevance to the target relation, highlighting the need for mechanisms that can differentiate path contributions. CodRE research has progressed significantly in recent years. [PERSON] et al. [9] pioneered this direction by introducing the CodRED dataset, where documents connected through bridge entities are organized as text paths, and proposing BERT-based models for the task. However, their approaches suffered from irrelevant context in model inputs and inadequate cross-path connection modeling. [PERSON] et al. [8] addressed these limitations with their Entity-based Cross-path Relation Inference Method (ECRIM), which incorporated an entity-centered noise filter for context refinement and a cross-path entity relation attention mechanism. [PERSON] et al. [10] further advanced the field with their local-to-global causal reasoning model (LGCR), utilizing graph-based representations and causality estimation for noise filtering. To alleviate the burden of sentence selection, REIC [11] presented the first reinforcement learning selector for CodRE that extracts sentences based on target relation scores. Despite these advances, current CodRE methods face two critical limitations. Firstly, previous methods only consider the entity information for context filtering. Typical of previous approaches, [PERSON] et al. [8] introduce bridge entities for context filtering to collect semantically relevant context around target entities. This approach does not consider the influence of potential relation types during filtering. Consequently, important relation-specific textual clues may be overlooked or irrelevant noise retained. For example, when determining if an entity is the \"founder of\" another entity, text describing foundation events is critical, but current filters might miss such context if not directly surrounding the target entities. The failure to incorporate relation-specific information during filtering significantly limits the model's ability to identify subtle but crucial evidence for complex relations. Secondly, current methods fail to account for the varying semantic distances between different document paths in the feature space when distributing attention. Intuitively, path pairs with shorter semantic distances (i.e., higher semantic similarity) should receive greater attention weight compared to paths with larger semantic distances, but existing attention mechanisms treat all paths uniformly [12].