Beyond Chunk-Local Extraction: Cross-Chunk Graph Augmentation for GraphRAG

• URL: http://arxiv.org/abs/2605.28004v1
• Date: Wed, 27 May 2026 05:55:53 GMT
• Title: Beyond Chunk-Local Extraction: Cross-Chunk Graph Augmentation for GraphRAG
• Abstract summary: GraphRAG extends retrieval-augmented generation by organizing corpora as explicit knowledge graphs.<n>Existing frameworks extract entities and relations within individual chunks, leaving cross-chunk relations systematically absent from the index.<n>We present CrossAug, a GNN-guided CROSS-Chunk Graph AUGmentation method that enriches GraphRAG indices with cross-chunk relational structure.
• Score: 11.699085890492833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: GraphRAG extends retrieval-augmented generation by organizing corpora as explicit knowledge graphs, enabling graph-based retrieval for complex question answering. However, existing frameworks extract entities and relations within individual chunks, leaving cross-chunk relations -- those whose evidence spans multiple passages -- systematically absent from the index. Exhaustive LLM-based recovery of such relations is impractical due to the combinatorial explosion of chunk combinations. We present CrossAug, a GNN-guided CROSS-Chunk Graph AUGmentation method that enriches GraphRAG indices with cross-chunk relational structure as an offline step before query-time retrieval. CrossAug derives training supervision through self-supervised graph corruption, uses a topology-aware GNN to score subgraphs for missingness, and applies evidence-grounded LLM completion only to selected high-scoring regions. Experiments on three LLM-based GraphRAG frameworks across four multi-hop and long-document QA benchmarks demonstrate that CrossAug consistently improves performance, confirming the benefit of cross-chunk graph augmentation for retrieval-based question answering. Our code is available at https://github.com/DonFinliani/CrossAug.

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