BRIDGE: Quantized Inter-Document Graph With Personalized PageRank for Training-Free Multi-Hop Passage Augmented Retrieval

Abstract

Cross-document knowledge retrieval requires the retrieval and synthesis of evidence scattered across multiple passages linked by implicit or explicit inter-document references. Existing methods either treat the corpus as a flat collection of independent passages, thereby forfeiting the links that connect evidence fragments, or reconstruct those links by generating synthetic entity triples, an expensive process that introduces hallucinated graph edges. To address this, we propose BRIDGE (Bridging Retrieval with Inter-Document Graph Expansion). This four-stage passage-retrieval pipeline exploits inter-document reference signals to recover multi-hop evidence chains without requiring the construction of graphs from expensive pretrained language model calls. To bridge the gap between flat retrieval and complex query resolution, the pipeline begins by fusing lexical term frequency and the dense embedding signals via Reciprocal Rank Fusion using an INT8-quantised vector index that compresses the embedding store by $4\times $ . It then applies a cross-encoder model as an initial precision gate to discover supporting passages that share a minimal lexical overlap with the query. And subsequently, the system traverses an inter-document reference graph anchored on cross-reference structures using Personalised PageRank. Finally, a cross-encoder reranking is performed on the merged candidate pool to ensure consistent scoring across both retrieved and graph-expanded passages. We evaluate BRIDGE on three established multi-hop QA benchmarks: HotpotQA, MuSiQue, and 2WikiMultiHopQA. Without any dataset-specific tuning, our approach achieves Recall@2 of 79.8, 41.5, and 65.7, respectively, matching or exceeding baselines including HippoRAG (59.0, 41.0, 71.5) and ColBERTv2 (64.7, 37.9, 59.2) on two of three benchmarks. Our ablation analysis confirms that reference-graph expansion discovers an average of 7.0 additional relevant passages per query, while the final cross-encoder reranking prevents score displacement and maintains top-of-list precision.