G e AR : Graph-enhanced Agent for Retrieval-augmented Generation (2025)

Abstract: Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios. In this paper, we introduce \text{G\small{E}\normalsize{AR}} , a system that advances RAG performance through two key innovations: (i) an efficient graph expansion mechanism that augments any conventional base retriever, such as BM25, and (ii) an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. Our evaluation demonstrates \text{G\small{E}\normalsize{AR}} ‘s superior retrieval capabilities across three multi-hop question answering datasets. Notably, our system achieves state-of-the-art results with improvements exceeding 10% on the challenging MuSiQue dataset, while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. The project page is available at https://gear-rag.github.io.

## introduction retrieval-augmented generation (rag) has enhanced the performance of large language models (llms) @xcite in question answering (qa) tasks @xcite . while effective for simple queries, multi-hop qa presents a more complex challenge, requiring reasoning across several passages or documents. consider the example in to address these complex reasoning requirements, researchers have increasingly turned to graph-based approaches (fang et al., 2024; li et al., 2024; edge et al., 2024; gutierrez et al., 2024; † the authors contributed equally to this work. in what year did stephen curry's father join the team from which he started his college basketball career? in this paper, we present gear, a graphenhanced agent for retrieval-augmented generation. during the offline stage, we align passages with their extracted triples to create interconnected indices. this alignment allows passages to be connected through graphs of triples. gear features a graph-based passage retrieval component called syncge. unlike previous approaches that require expensive llm calls for graph exploration, our method uses an llm only to locate initial nodes (triples) and then employs a generic semantic model to expand the triple sub-graph by exploring diverse triple beams. additionally, gear uses multi-hop context retrieved by syncge to construct a memory that summarises information for multi-step retrieval. our work refines the neurobiology-inspired paradigm proposed by @xcite by modeling the communication between the hippocampus and neocortex during episodic memory formation. in our design, an array of proximal triples functions as a memory gist learned through the hippocampus within one or a few shots (iterations), which is then projected back to the neocortex for later recall stages @xcite . we highlight the complementary potential between our graph retrieval approach and an llm, which, within our system, emulates the synergy between the hippocampus and neocortex, offering insights from a biomimetic perspective. we evaluate the retrieval performance of gear on three multi-hop qa benchmarks: musique, hotpotqa, and 2wikimultihopqa. gear pushes the state of the art, achieving significant improvements in both single-and multi-step retrieval settings, with gains exceeding 10% on the most challenging musique dataset. furthermore, we demonstrate that our framework can address multi-hop questions in fewer iterations while consuming significantly fewer llm tokens. even with a single iteration, gear offers a more efficient alternative to other iterative retrieval methods, such as hipporag w/ ircot. our contributions can be summarised as follows: our work draws inspiration from two branches of research: (i) retrieval-augmented models for qa and (ii) multi-hop qa using combinations of llms with graphical structures. ## preliminaries let @xmath0, c 2 , . . . , c c } be an index of passages and @xmath1, t 2 , . . . , t t : t @xmath2, p j , o j )} be another index representing a set of triples associated with the passages in c s.t. ∀t @xmath3, where s j , p j and o j the respective subject, predicate and object of the j-th triple. in this setup, each triple is uniquely linked to exactly one passage, and a passage can potentially be associated with multiple triples. given an input query q and an index of interest @xmath4, . . . , r r }, retrieving items from r relevant to q can be achieved by using a base retrieval function h k base (q, r) ⊆ r that returns a ranked list of k items from r in descending order, according to a retrieval score. bm25 or a conventional dense retriever can serve as a base retrieval function, without requiring any multi-hop capabilities. our goal is to retrieve relevant passages from c that enable a retrieval-augmented model to answer multi-hop queries @xcite . to this end, we introduce gear, which is a graph-enhanced framework of retrieval agent (see figure 1 ). ## retrieval with synchronised graph expansion given an input query q, let c ′ @xmath5, c) be a list of passages returned by the base retriever. given this list, c ′ q , our goal is to derive relevant multi-hop contexts (passages) by retrieving a subgraph of triples that interconnect their source passages. two challenges arise in materialising such sub-graph retrieval: (i) locating initial triples (i.e. starting nodes) t q , and (ii) expanding the graph from these initial triples while reducing the search space. the following sections address these challenges within gear. ## multi-step extension we further present an agentic framework that models a human-like information-seeking process through multi-turn interactions with the graphenhanced retriever. the resulting agent is referred to as gear. we focus on: ## experimental setup we evaluate our framework on three open-domain multi-hop qa datasets: musique @xcite , @xcite , and 2wiki-multihopqa (2wiki) @xcite . for musique and 2wiki, we use the data provided in the ircot paper @xcite which includes the full corpus, while for hotpotqa, we follow the same setting as hipporag @xcite to limit experimental costs. more details are provided in appendix a. we measure both retrieval and qa performance, with our primary contributions focused on the retrieval component. for retrieval evaluation, we use recall@k (r@k) for @xmath6, 10, 15}, showing the percentage of questions where the correct entries are found within the top-k retrieved passages. we include an analysis about the selected recall ranks in appendix d. following standard practices, qa performance is evaluated with exact match (em) and f1 scores @xcite . ## conclusion we propose gear, a novel framework that incorporates a graph-based retriever within a multi-step retrieval agent to model the information-seeking process for multi-hop question answering. we showcase the synergy between our proposed graph retriever (i.e. syncge) and the llm within the gear framework. syncge leverages the llm to synchronise information from passages with triples and expands the graph by exploring diverse beams of triples that link multi-hop contexts. our experiments reveal that this strategy improves over more naive implementations, demonstrating the llm's capability to guide the exploration of initial nodes for graph expansion. furthermore, gear utilises multi-hop contexts returned by syncge and constructs a gist memory which is used for effectively summarising information across iterations. gear achieves superior performance compared to other multi-step retrieval methods while requiring fewer iterations and llm tokens.