Retrieval-Augmented Generation (RAG) en- hances large language models with ex- ternal knowledge but often suffers from query–evidence mismatch due to inappropriate retrieval granularity. Chunk-level retrieval can introduce substantial noise, while graph-based retrieval may suffer from overly fine-grained or densely connected structures that hinder effec- tive multi-hop reasoning. To address this issue, we propose Atom-RAG, a retrieval-augmented framework based on atomic fact–centric knowl- edge representation and granularity-aligned re- trieval. Atom-RAG represents documents as collections of atomic facts and aligns retrieval with the fine-grained information needs of com- plex queries. By decomposing queries into fact-level subqueries, the framework retrieves and aggregates evidence in a manner aligned with the information requirements of multi-hop reasoning, producing a compact and evidence- aligned context for generation. Experiments on benchmarks covering different reasoning depths show that Atom-RAG consistently out- performs strong iterative and graph-based RAG baselines, achieving improved multi-hop QA performance and stronger evidence retrieval. These results highlight the importance of align- ing the granularity of queries and retrieval units for reliable multi-hop reasoning within a unified retrieval pipeline. Code and data are available at https://github.com/cody628/ AtomRAG.

Retrieval-Augmented Generation (RAG) has established itself as an innovative technology enabling Large Language Models (LLMs) to answer difficult questions beyond their internal knowledge. Graph-based RAG, which utilizes a triplet-based graph as a database, offers the advantage of structuring relationships between entities through graph connectivity, thereby capturing connections within the corpus that traditional RAG cannot express. However, this paper points out that the process of converting a corpus into a triplet-based graph using LLMs may be unsuitable for RAG due to the inability to represent hierarchical information and structural rigidity. To address this, we propose a novel database structure called the TRA(Title-Relations-Attributes) structure. Through a series of experiments, we highlight the benefits of indexing TRA Structure as a fact to utilize it as a retrieval-optimized data structure, and demonstrate its powerful multi-hop reasoning performance when combined with query decomposition, while also covering key insights in multi-hop reasoning.

Retrieval-augmented generation (RAG) enables large language models (LLMs) to incorporate external knowledge at inference. Graph-based RAG extends this by organizing corpora into knowledge graphs, improving multi-hop reasoning and offering a global understanding of the corpus. However, triplet-based graphs generated by LLMs are often fragmented and sparsely connected, which reduces coherence and hinders reasoning. Prior enrichment methods such as clustering, community detection, or approximate graph algorithms attempt to restore connectivity but incur high computational cost and risk semantic distortion. To address these issues, we propose TH-RAG, a hierarchical framework that organizes triplets into subtopics and topics, enhancing connectivity, integrating dispersed information, and supporting robust multi-hop reasoning. Experiments on abstractive and specific QA benchmarks show that TH-RAG outperforms strong baselines in accuracy and robustness while remaining efficient, providing a scalable foundation for graph-based RAG.

Recent advancements in Retrieval-Augmented Generation (RAG) have improved large language models (LLMs) by incorporating external knowledge at inference time. Graph-based RAG systems have emerged as promising approaches, enabling multi-hop reasoning by organizing retrieved information into structured graphs. However, when knowledge graphs are constructed from unstructured documents using LLMs, they often suffer from fragmentation—resulting in disconnected subgraphs that limit inferential coherence and undermine the advantages of graph-based retrieval. To address these limitations, we propose ReGraphRAG, a novel framework designed to reconstruct and enrich fragmented knowledge graphs through three core components: Graph Reorganization, Perspective Expansion, and Query-aware Reranking. Experiments on four benchmarks show that ReGraphRAG outperforms state-of-the-art baselines, achieving over 80% average diversity win rate. Ablation studies highlight the key contributions of graph reorganization and especially perspective expansion to performance gains. Our code is available at: https://anonymous.4open.science/r/ReGraphRAG-7B73