Results show that observed evidence can guide graph retrieval toward the part of a supporting chain left underspecified by the original question, and introduce EviReform, which separates revising the retrieval request from aggregating evidence in the graph.
Abstract
Multi-hop retrieval must recover passages that provide sufficient evidence together. An initial passage often resolves an entity or relation implicit in the question, making the missing evidence easier to describe only after retrieval begins. Graph retrieval improves access to related evidence through stored corpus structure, but its retrieval signal is commonly derived from the original question. Complementary evidence must then be reached through stored relations even when an observed passage provides a more direct semantic cue. We introduce EviReform, which separates revising the retrieval request from aggregating evidence in the graph. Retrieved source passages formulate residual queries for the unresolved information need. The original and residual retrieval signals are normalized separately, combined, and propagated between propositions that share entities. On 2WikiMultiHopQA, HotpotQA, and MuSiQue, EviReform exceeds the strongest baseline by up to 5.59 Recall@5 points and 4.50 F1 points. These results show that observed evidence can guide graph retrieval toward the part of a supporting chain left underspecified by the original question. Code is available at https://github.com/XrazyMee/EviReform.
Reasoning-intensive retrieval addresses queries whose relevance cannot be identified by surface-level matching, thereby requiring multi-step reasoning. Because relevant documents rarely appear in the initial candidate set, retrieval systems suffer from the bounded recall problem. Existing methods iteratively expand a candidate pool at the document level over a corpus graph, examining each neighbor in isolation and drifting toward a narrow region of the corpus. To address this problem, we propose Group-Aware Adaptive Retrieval for Evidence Navigation (GAREN), which explores the corpus graph through group-level expansion. GAREN organizes documents into semantically coherent and distinguishable groups based on their connections in the corpus graph. The information in each group indicates what can be accessed by expanding through it, providing guidance beyond individual document-level signals. At each iteration, GAREN uses a group-level navigator to identify promising expansion directions, retrieves documents from the selected groups, and applies a document-level reranker to evaluate the updated candidate set. Extensive experiments show that GAREN achieves up to 8.0% improvement over the strongest baseline on BRIGHT. The source code is available at https://github.com/KJ12124/GAREN
June Park, Jun Kwon, Jonghyo Kim et al.· 0 citations
Experiments demonstrate that PAGE-RAG achieves competitive answer quality while improving retrieval efficiency and knowledge reliability, highlighting the importance of projection-aware graph modeling, adaptive retrieval, and explicit knowledge boundary control for trustworthy GraphRAG systems.
Xingyu Chen, Junxiu An, Jun Guo et al.· arXiv.org· 1 citation
Retrieval-augmented generation (RAG) critically depends on retrieving the evidence necessary for effective reasoning. However, this remains particularly challenging in multi-hop question answering (QA), where supporting passages are often linked through intermediate entities and relations that must be progressively uncovered. Existing retrieval approaches typically rely on a single retrieval intent or one-shot query expansion, limiting their ability to adapt to newly retrieved evidence and potentially introducing noisy or redundant retrieval signals. To address these limitations, we propose a training-free multi-hop retrieval framework that integrates evidence-conditioned exploration, passage-specific contrastive refinement, and coverage-aware final ranking. During offline indexing, the framework constructs passage-specific contrastive facets that characterize each passage relative to its semantically similar neighbors, providing fine-grained signals to distinguish closely related candidates. At inference time, the framework iteratively retrieves evidence, generates probes targeting unresolved information needs, refines candidate relevance using the contrastive facets, and selects a complementary set of passages that collectively cover diverse evidence-seeking intents. Experiments on MuSiQue, HotpotQA, and 2WikiMultihopQA demonstrate consistent improvements in retrieval quality and downstream QA performance over baselines.
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.
MEGRAG is an answer-aware framework that represents multi-hop reasoning as a path-structured multi-granular evidence graph and uses the resulting intermediate answer and prior reasoning to decide whether the Initial Query has been resolved.
Weidong Bao, Yingying Sun, Jun Yang et al.· 0 citations
The proposed LineageRAG is a graph-based Retrieval-Augmented Generation method, which constructs one evidence for each query-derived evidence demand and completes it with a verbatim source span when the selected evidence supports that demand.
Linyao Zheng, Xuhang Shi, Zhifang Mao et al.· 0 citations
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