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cs.IR2026
Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems
Dongzhe Fan, Zheyi Xue, Siyuan Liu +1
Retrieval-augmented generation (RAG) and its graph-based extensions (GraphRAG) are effective paradigms for improving large language model (LLM) reasoning by grounding generation in…
cs.IR2026
GraphRAG-Router: Learning Cost-Efficient Routing over GraphRAGs and LLMs with Reinforcement Learning
Dongzhe Fan, Chuanhao Ji, Zimu Wang +2
Graph-based retrieval-augmented generation (GraphRAG) has recently emerged as a powerful paradigm for knowledge-intensive question answering, especially for tasks that require stru…