Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 6397-6408· 0 citations· 16 references
Abstract
Retrieval-augmented generation (RAG) has become a standard paradigm for knowledge-intensive question answering by grounding large language models (LLMs) in external evidence. However, open-domain multi-hop question answering (QA) remains challenging for two reasons. First, evidence dispersion across documents and non-contiguous spans means that critical bridge evidence can be weakly related to query and is easy to miss. Second, semantic-resolution mismatch complicates retrieval: coarser retrieval views offer better global coherence but may obscure the exact bridging detail, while finer-grained views highlight specific mentions but may omit the context needed to reveal the relation. In this paper, we propose MCoRe, a multi-entry complementary retrieval framework with reflection-guided iteration for multi-hop QA. To mitigate the semantic-resolution mismatch, MCoRe enables multi-entry complementary retrieval by indexing entry units at multiple semantic resolutions (entities, sentences, and summaries) with explicit links to chunk evidence, mapping all hits back to chunks, and fusing cross-resolution hits via chunk-level voting to form a compact evidence set for answer generation. To cope with evidence dispersion, MCoRe performs reflection-guided iteration: when evidence is insufficient, it identifies the missing bridge cue and issues a gap-focused follow-up query to recover it. Empirical results demonstrate the effectiveness of MCoRe, which consistently outperforms state-of-the-art baselines by 6.77 EM points and 8.79 F1 points averaged over three multi-hop QA benchmarks, with gains of up to 12.70 EM and 14.06 F1 points on 2Wiki.
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.
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
Dynamic Multi-Path Retrieval for KB-VQA (DMRAG) is proposed, which re-trieves candidates through multiple retrieval paths that capture complementary visual and semantic cues and performs Question-Adaptive Gated Fusion to balance contributions from different modalities according to the query’s information need.
Zeyu Song, Yimin Deng, Yuxin Zhang et al.· 0 citations
The Document Question Answering (DocQA) task necessitates the synergistic interpretation of visual and textual information embedded within documents. Although Retrieval-Augmented Generation (RAG) has enhanced the capabilities of Large Vision-Language Models (LVLMs), existing approaches still encounter significant bottlenecks when processing large-scale documents: the inability to capture long-range contextual dependencies within non-textual modalities, the difficulty in facilitating interaction and mutual complementation between different modalities, and the inefficient integration of heterogeneous modal information. To address these challenges, we introduce MMHRAG, a novel Multi-Modal Hierarchical Retrieval-Augmented Generation framework that achieves cross-modal interaction on DocQA for the first time. First, we construct a cross-modal hierarchical retrieval tree via a bottom-up recursive clustering and summarization mechanism. A key innovation of our approach is the structural injection of visual information, where image semantics are integrated as high-level abstract summaries of textual segments, thereby bridging the semantic gap between modalities. Furthermore, we design a Summarizing Agent to resolve logical conflicts, information redundancy, and granularity discrepancies among retrieved cross-modal evidence. Extensive experiments across multiple multi-modal long-document benchmarks demonstrate that MMHRAG significantly outperforms state-of-the-art baselines, achieving superior accuracy and consistency in complex reasoning tasks.
Jiayuan Wang, Jie Lian, Fu Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
Graph-based retrieval-augmented generation increasingly relies on multi-hop retrieval, where answering a query requires composing multiple connected knowledge-graph triplets. However, existing retrievers often rank triplets independently via global semantic matching. Moreover, many multi-hop benchmarks provide only final answers, which limits supervision for query--triplet alignment and causes structurally necessary but weakly aligned facts to be missed. To address these issues, we propose a knowledge-aligned multi-hop retriever, KAMR, which distinguishes anchor triplets that are strongly constrained by the query from connected triplets that are weakly aligned yet structurally linked to the anchors. To mitigate the lack of query-triplet alignment supervision, we build a partial alignment dataset by masking triplet elements and prompting an LLM to generate corresponding queries, and optimize two contrastive objectives for pair-level and element-level matching. At inference time, KAMR retrieves anchors globally and then expands locally to collect connected evidence. Across four benchmarks, three LLM backbones, and fourteen baselines, KAMR consistently improves multi-hop retrieval and downstream question answering performance.
Xiaochen Wang, Yuan Zhong, Haoyu Wang et al.· arXiv.org· 0 citations
This study proposes STaR, a novel retriever fine-tuning framework that integrates BM25 similarity graph-based soft labeling with a triplet similarity learning strategy based on Sentence-BERT (SBERT), and introduces a triplet-aware SBERT training architecture that explicitly models relative semantic distances between queries and candidate passages, significantly enhancing retrieval ranking precision and semantic robustness.
Jiali Jiang, Chih-Yung Chang, Youxi Li et al.· Multimedia Systems· 0 citations
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