Simple Links, Strong Retrieval: Local Entity Expansion for Multi-Hop Evidence Retrieval

Retrieving complete supporting evidence from document collections remains a central challenge for knowledge-intensive language systems, particularly when answering a question requires connecting information across multiple sources. Similarity-based retrieval can overlook passages whose relevance depends on intermediate evidence, while graph-based and iterative approaches often introduce query decomposition, global propagation, or repeated reasoning to recover these connections. We present SCOPE, an entity-guided retrieval framework that addresses this challenge through a deliberately simple design: local evidence expansion followed by explicit evidence selection. SCOPE turns precomputed passage–entity links into access paths beyond the initial dense ranking, then combines semantic, lexical, and entity-based signals with listwise reranking to select supporting passages. This modular design enables complementary evidence discovery without query decomposition or global graph propagation. On the established multi-hop QA benchmarks MuSiQue, 2Wiki, and HotpotQA, SCOPE outperforms leading graph-based baselines, including BrowseNet and HippoRAG 2, in both evidence retrieval and downstream question answering. On LoCoMo, however, reranked graph-free retrieval performs better, highlighting a limitation of entity-guided expansion in conversational memory. These results demonstrate that a simple local-expansion design can deliver stronger retrieval and answer quality than more elaborate retrieval pipelines.