C o RAG : Enhancing Hybrid Retrieval-Augmented Generation through a Cooperative Retriever Architecture (2025)

Abstract: Retrieval-Augmented Generation (RAG) is introduced to enhance Large Language Models (LLMs) by integrating external knowledge. However, conventional RAG approaches treat retrieved documents as independent units, often overlooking their interdependencies. Hybrid-RAG, a recently proposed paradigm that combines textual documents and graph-structured relational information for RAG, mitigates this limitation by collecting entity documents during graph traversal. However, existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base, which often miss relevant information located further away from a global view. To overcome the above challenges, we propose CoRAG that dynamically chooses whether to retrieve information through direct textual search or explore graph structures in the knowledge base. Our architecture blends different retrieval results, ensuring the potentially correct answer is chosen based on the query context. The textual retrieval components also enable global retrieval by scoring non-neighboring entity documents based on semantic relevance, bypassing the locality constraints of graph traversal. Experiments on semi-structured (relational and textual) knowledge base QA benchmarks demonstrate the outstanding performance of CoRAG.

## introduction recent advances in large language models (llms) @xcite have led to impressive achievements across diverse tasks such as question answering @xcite and drug discovery @xcite . despite their capabilities, llms remain prone to factual errors and hallucinations (e.g., answering "the capital of canada is toronto"). to mitigate this, retrieval-augmented generation (rag) @xcite enhances llms by retrieving relevant knowledge from external data sources @xcite . however, traditional rag treats retrieved documents independently, ignoring their interdependencies @xcite . graph-rag @xcite addresses this by leveraging graph-structured knowledge bases @xcite to model relationships among retrieved entities, enabling richer context and deeper semantics for improved generation @xcite . most graph-rag methods focus on graph traversal over knowledge graphs @xcite to retrieve relevant graph components, such as nodes @xcite , triplets @xcite , and paths @xcite , based on the relationships between entities in the graph. despite their strong performance in knowledge base question answering (kbqa) @xcite , they still heavily rely on graph structures of the knowledge base and lack consideration of textual information. such a paradigm restricts their effectiveness in handling questions that require a higher level of textual comprehension @xcite . specifically, kbs only represent knowledge as discrete triplets (e.g., (high-altitude residents, prone to, oxygen deficiency)), which lack the contextual understanding needed to answer analytical questions like "why are people in high-altitude regions more prone to oxygen deficiency?" recently, there has been increasing attention on using semi-structured knowledge bases (skb) @xcite to complement kbqa with supplementary textual information, where each entity is associated with a document. this has led to the development of hybrid-rag @xcite , a paradigm that integrates relational and textual information for retrieval within the rag framework. as shown in figure 1 , we retrieve entities either from the entire set of documents based on textual relevance (textual), or from graph neighbours centered on topic entities in the query (relational). a topic entity refers to the main subject of a question, e.g., 'usa' in "who's the president of the usa?" the selected strategy picks the better retriever for each question based on ground-truth answers, serving as an approximate upper bound. while the two base retrievers perform comparably, selected substantially outperforms both on skbs, underscoring the potential of hybrid retrieval. nevertheless, existing hybrid-rag works still face two critical challenges. (1) lack of global information. most works first identify topic entities within the query and subsequently apply egograph searching @xcite or path searching @xcite to leverage relational information. however, relevant entities and documents may be located at a considerable distance from the identified topic entities. the restriction on the locality of graph traversal limits the flexibility and effectiveness of retrieval. (2) lack of query-aware retriever selection. existing hybrid-rag methods typically retrieve both relational and textual information and concatenate them as input, regardless of their relevance to the current query @xcite . this indiscriminate fusion can confuse the llm, as it may struggle to prioritize the retrieved documents between redundant or conflicting sources. worse still, excessive inclusion of irrelevant text inflates the input length and increases reasoning complexity @xcite . to overcome these limitations, we propose corag, a novel hybrid-rag framework that adaptively combines textual search with graphbased relational search depending on the query. the core idea of corag is to enable a multipleretriever framework that autonomously integrates relational and textual information during each retrieval request. unlike previous methods that are restricted to local graph neighborhoods, corag can flexibly retrieve global relevant information through query-document text embedding matching. moreover, corag intelligently blends textual and relational retrieval results using a cooperative retriever architecture, ensuring a coherent and effective integration of diverse knowledge sources. specifically, our hierarchical gating mechanism @xcite intelligently combines retrieval results from both textual and relational sources, thereby enhancing the model's overall performance and robustness. overall, our contributions are as follows: ## preliminary in this paper, we focus on the semi-structured knowledge base question answering (skbqa) task @xcite , which extends the traditional knowledge base question answering (kbqa) task @xcite . in skbqa, the llms must not only retrieve relevant information from the graph structures of skb, but also from an external semi-structured knowledge base (skb) by introducing entity-level documents. a detailed definition is provided below: definition 2.1 (skbqa). skbqa consists of two components: (i) a semi-structured knowledge base (skb) and (ii) a question-answering (qa) dataset. skb is a document-enhanced knowledge graph @xmath0, t , d, t e , t r }, where e, t , and d denote the entity set, the triplet set, and the document set, respectively. each entity @xmath1. t e and t r represent the collections of all entity 16089 types and relation types, respectively. each triplet @xmath2, r, e 2 ), where {e 1 , e 2 } ⊂ e, @xmath3. qa dataset consists of question/answers pairs: d @xmath4, a)}, where the question q appears in the form of a string (e.g., "what drugs target the cyp3a4 enzyme and are used to treat strongyloidiasis?"), and the answer set a is a set of entities (e.g., {"ivermectin"} for the before-mentioned q) which satisfies a ⊂ e. given a skb and a d qa , the skbqa task requires the model to predict a set â ⊂ e consisting of k potential answer entities, aiming to maximize the coverage of all groundtruth answer entities: max â(| â∩a|). we quantify the task objective using hits@k metrics. ## methodology in this section, we introduce corag, a hybrid-rag framework that dynamically integrates both relational knowledge and textual information. we begin by extracting topic entities ( § 3.1) from the question, which will be used in the subsequent retrieval process. next, we design two distinct types of retrievers to separately fetch textual and relational information ( § 3.2). in section 3.3, we present the core component of our approach, the cooperative-retrievers (cor). as illustrated in figure 2 , cor employs a hierarchical architecture to combine these two sources of information, which utilizes a gate network that dynamically assigns higher attention scores to the retrievers most relevant to answering the question. lastly, we introduce corag ( § 3.4), which integrates cor with an llm reranker, leveraging llms' reasoning strengths in the skbqa task. ## conclusion we propose corag, a hybrid retrieval-augmented generation framework that dynamically integrates relational and textual knowledge through a cooperative-retrievers architecture. by enabling global retrieval beyond local graph neighborhoods and adaptively fusing heterogeneous information, corag overcomes key limitations of existing hybrid-rag methods. experiments on semi-structured knowledge base qa benchmarks demonstrate that corag achieves state-of-the-art performance, outperforming both standard rag and graph-based rag methods. our results emphasize the importance of adaptive retrieval selection in enhancing hybrid-rag performance.