Agentic search narrows the gap between dense RAG and GraphRAG but does not remove GraphRAG's advantage on complex multi-hop reasoning.
Youtu-graphrag: Vertically unified agents for graph retrieval-augmented complex reasoning.CoRR, abs/2508.19855
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MemGraphRAG uses a memory-based multi-agent system for globally consistent graph construction from fragmented corpora plus a memory-aware hierarchical retriever, claiming better benchmark performance than prior GraphRAG methods at similar cost.
MoG uses hub graphs for shared context and sparsely activates expert graphs with a topology-aware router, reporting over 20% relative gains on MuSiQue.
GraphReview models paper evaluation as LLM-driven message passing on a semantic paper graph that links intrinsic quality, contemporaneous papers, and prior work, then applies Personalized PageRank for ranking and review generation.
A lightweight hierarchical multimodal graph RAG that fuses entity-grounded visual objects with text nodes and propagates relevance via multi-granularity PPR, delivering SOTA multimodal task performance at far lower construction cost.
CS-RAG is a GraphRAG framework that plans queries as ordered atomic constraints, uses anchor-relation aware retrieval, applies sufficiency checks, and falls back to text recovery to reduce drift and hallucination from imperfect KGs.
G-reasoner uses QuadGraph abstraction and a 34M-parameter graph foundation model integrated with LLMs to enable scalable reasoning over diverse graph-structured knowledge, outperforming baselines on six benchmarks.
citing papers explorer
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Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems
Agentic search narrows the gap between dense RAG and GraphRAG but does not remove GraphRAG's advantage on complex multi-hop reasoning.
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MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation
MemGraphRAG uses a memory-based multi-agent system for globally consistent graph construction from fragmented corpora plus a memory-aware hierarchical retriever, claiming better benchmark performance than prior GraphRAG methods at similar cost.
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MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
MoG uses hub graphs for shared context and sparsely activates expert graphs with a topology-aware router, reporting over 20% relative gains on MuSiQue.
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GraphReview: Scientific Paper Evaluation via LLM-Based Graph Message Passing
GraphReview models paper evaluation as LLM-driven message passing on a semantic paper graph that links intrinsic quality, contemporaneous papers, and prior work, then applies Personalized PageRank for ranking and review generation.
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MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
A lightweight hierarchical multimodal graph RAG that fuses entity-grounded visual objects with text nodes and propagates relevance via multi-granularity PPR, delivering SOTA multimodal task performance at far lower construction cost.
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Toward Robust GraphRAG: Mitigating Retrieval Drift and Hallucination from Imperfect Knowledge Graphs
CS-RAG is a GraphRAG framework that plans queries as ordered atomic constraints, uses anchor-relation aware retrieval, applies sufficiency checks, and falls back to text recovery to reduce drift and hallucination from imperfect KGs.
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G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge
G-reasoner uses QuadGraph abstraction and a 34M-parameter graph foundation model integrated with LLMs to enable scalable reasoning over diverse graph-structured knowledge, outperforming baselines on six benchmarks.