Agentic search narrows the gap between dense RAG and GraphRAG but does not remove GraphRAG's advantage on complex multi-hop reasoning.
Yuyan Liu, Sirui Ding, Sheng Zhou, Wenqi Fan, and Qiaoyu Tan
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FT-RAG introduces a fine-grained graph-based retrieval framework for tables plus a new 9870-pair benchmark, reporting 23.5% and 59.2% gains in table- and cell-level hit rates and 62.2% higher exact-value recall over baselines.
AgentGL is an RL-driven LLM agent framework for agentic graph learning that uses graph-native tools and curriculum training to outperform GraphLLM and GraphRAG baselines by up to 17.5% on node classification and 28.4% on link prediction across text-attributed graph 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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FT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning
FT-RAG introduces a fine-grained graph-based retrieval framework for tables plus a new 9870-pair benchmark, reporting 23.5% and 59.2% gains in table- and cell-level hit rates and 62.2% higher exact-value recall over baselines.
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AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
AgentGL is an RL-driven LLM agent framework for agentic graph learning that uses graph-native tools and curriculum training to outperform GraphLLM and GraphRAG baselines by up to 17.5% on node classification and 28.4% on link prediction across text-attributed graph benchmarks.