M a G i X : A Multi-Granular Adaptive Graph Intelligence Framework for Enhancing Cross-Lingual RAG (2025)

Abstract: Retrieval-Augmented Generation (RAG) enhances large language models by grounding their outputs in external knowledge. Recent advances in Graph-based RAG (GRAG) frameworks, such as GraphRAG, LightRAG, and HippoRAG2, integrate knowledge graphs into the retrieval process to improve multi-hop reasoning and semantic coherence. While effective in monolingual settings, these methods remain underexplored in cross-lingual scenarios and face limitations in semantic granularity and entity alignment. In this work, we propose MaGiX, the first GRAG framework tailored for English–Vietnamese cross-lingual question answering. MaGiX constructs a multi-granular cross-lingual knowledge graph using fine-grained attribute descriptions and cross-synonym edges, and incorporates a custom multilingual embedding model trained with contrastive learning for semantic alignment. During retrieval, MaGiX leverages graph-based reasoning and a semantic-aware reranking strategy to enhance cross-lingual relevance. Experiments across five benchmarks show that MaGiX substantially outperforms prior GRAG systems in both retrieval accuracy and generation quality, advancing structured retrieval for multilingual QA.

## introduction in the rapidly evolving landscape of multilingual information access, the ability to retrieve and generate knowledge across languages is increasingly essential. while large language models (llms) have shown impressive capabilities in reasoning and generation, they often struggle to integrate new knowledge efficiently, particularly in multilingual or low-resource settings @xcite @xcite . retrieval-augmented generation (rag) @xcite * equal contribution † corresponding author: linhnv@soict.hust.edu.vn frameworks offer a promising solution by allowing llms to ground their outputs in external documents. however, rag systems face several challenges in continuous knowledge acquisition, including ensuring high-quality retrieval, effectively managing long contexts, and seamlessly integrating newly acquired knowledge @xcite . besides, traditional rag systems depend heavily on dense embedding similarity and often treat documents as flat, unstructured units-limiting their performance on complex, multi-hop, or semantically rich queries @xcite @xcite . to address these limitations, recent developments such as graphrag @xcite , ligh-trag @xcite and hipporag 2 @xcite have incorporated knowledge graph (kg) into the retrieval pipeline, enabling the modeling of semantic relationships between entities. collectively, these graph-based rag (grag) frameworks improve retrieval precision and reasoning performance by representing knowledge in a structured and interconnected form, which allows for more effective extraction of relevant information grounded in the contextual and relational structure of the underlying data. overall, these grag methodologies generally operate in two main stages: offline knowledge graph construction (offline indexing) and online graph-based inference retrieval (online retrieval). in the offline stage, unstructured documents are processed (often via an llm-based pipeline) to identify key entities and semantic relations, and these extracted elements are assembled into a knowledge graph; each entity node is further enriched with a concise description and each relation edge with a brief contextual annotation to capture their meaning. the indexing process also includes canonicalization, merging duplicate or co-referent entities into unified nodes to eliminate redundancy and maintain a clean graph index. in the online retrieval stage, the user's query is encoded or analyzed to identify its key concepts, which are matched to corresponding seed nodes in the knowledge graph. the system then traverses outward from these seed nodes to explore neighboring nodes and retrieve a relevant subgraph. the candidate documents or chunks linked to this subgraph are aggregated and reranked by relevance, ensuring the most pertinent and contextually connected evidence for answer generation. although these approaches show promise of kg in enhancing rag systems, they still exhibit several limitations. (1) underexploration in cross-lingual settings: most existing grag implementations have seen limited application in crosslingual question answering tasks. as a result, the potential of graph-based approaches in multilingual scenarios remains largely unexplored. (2) limitation in knowledge graph construction graphrag and lightrag typically concatenate all descriptions of entities and relations extracted from multiple text chunks into a single representation, resulting in overly long and ambiguous summaries that hinder fine-grained query matching and accurate localization of relevant chunks. furthermore, these frameworks often lack rich synonym edges and rely on exact string matching for entity deduplication, which is ineffective in crosslingual contexts where semantically equivalent entities may appear in different surface forms. while hipporag 2 introduces synonym edges based on entity name embedding similarity, this method remains limited in multilingual scenarios, as it depends solely on short entity names and fails to leverage the richer semantic from contextual descriptions. (3) limitation in graph-based inference retrieval prior grag frameworks typically rank or score candidate text chunks based on the connectivity of retrieved seed nodes within the graph. for instance, lightrag prioritizes passages containing a high number of neighboring entities to the seed node, while hipporag 2 leverages personalized pagerank (ppr), which inherently favors nodes with greater structural connectivity. however, such approaches may overemphasize co-occurrence patterns and underweight semantic relevance, potentially biasing retrieval toward text chunks that are densely linked rather than contextually appropriate. this limitation may be further exacerbated in cross-lingual scenarios, where semantically aligned entities across languages are often not directly connected or co-located in the same passage, leading to the misranking for a text chunk and underestimating its relevance level. in this paper, we present magix -a multi-granular adaptive graph intelligence framework that is the first to explore graph-based rag techniques for english-vietnamese cross-lingual question answering. by addressing these challenges, magix not only expands the applicability of grag to low-resource languages but also offers methodological innovations that improve retrieval accuracy and cross-lingual alignment (comparison with prior grag models shown in ## conclusion in this work, we introduced magix, the first graph-based rag framework designed for english-vietnamese cross-lingual question answering. by addressing key limitations in prior grag systems; such as limited multilingual adaptability, lack of fine-grained entity modeling, and reliance on shallow graph connectivity, we proposed a multigranular architecture that integrates contextual attribute descriptions, cross-synonym reasoning, and multi-aspect semantic-aware reranking. our framework leverages a domain-adapted multilingual embedding model and a structured retrieval strategy to achieve robust semantic alignment across languages. extensive experiments on diverse crosslingual benchmarks demonstrate that magix significantly improves both retrieval and generation quality. we believe this work lays the foundation for future research in cross-lingual structured retrieval, especially in low-resource and complex multilingual settings.