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

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 only 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 its training signal from self-supervised training pairs constructed from the base graph, uses a topology-aware GNN to score subgraphs for missingness, and applies evidence-grounded LLM completion only to the selected high-scoring regions. Experiments on three LLM-based GraphRAG frameworks across four multi-hop and long-document QA benchmarks show improvements across all framework–dataset–metric comparisons, confirming the benefit of cross-chunk graph augmentation for retrieval-based question answering. Our code is available at https://anonymous.4open.science/r/CrossAug-72BB/.