Structure-Augmented Reasoning Generation
This addresses the implicit reasoning bottleneck in RAG systems for complex question answering, offering enhanced reliability and interpretability.
The paper tackles the problem of complex multi-hop reasoning in Retrieval-Augmented Generation (RAG) systems by introducing SARG, a post-retrieval framework that materializes explicit reasoning structures, achieving substantial improvements over state-of-the-art RAG baselines across domains like general QA, biomedical literature, and financial analysis.
Retrieval-Augmented Generation (RAG) systems fail at complex multi-hop reasoning because they rely on large language models to implicitly connect information from unstructured document collections. This fundamental limitation stems from treating retrieved passages as independent context rather than recognizing the intricate relationships that enable coherent reasoning chains. We introduce SARG (Structure-Augmented Reasoning Generation), a post-retrieval framework that transforms traditional RAG pipelines by materializing explicit reasoning structures. SARG extracts {cause, relation, effect} triples from retrieved documents, constructs domain-adaptive graphs, and performs multi-hop traversal to discover reasoning chains that bridge query concepts to answers. Unlike existing approaches that modify retrieval mechanisms, SARG operates as a plug-and-play reasoning layer compatible with any RAG system. Extensive evaluation across diverse domains: general QA, biomedical literature, and financial analysis demonstrates that SARG achieves substantial improvements over state-of-the-art RAG baselines. Crucially, SARG also provides full reasoning traceability through explicit inference chains, addressing the critical interpretability gap in current RAG systems. Our results establish that explicit structural reasoning is not merely beneficial but essential for reliable complex question answering, offering a solution to RAG's implicit reasoning bottleneck.