R$^{2}$Adapter is a lightweight plug‑in that dynamically routes user queries between vanilla and graph‑based Retrieval‑Augmented Generation (RAG) systems. By sending only those queries that truly benefit from graph reasoning, it cuts graph‑retrieval overhead by up to 59% while keeping answer accuracy comparable. The adapter also rewrites uncertain graph‑routed queries to better expose multi‑hop reasoning needs, improving retrieval quality without extra supervision.
By Yucan Guo, Miao Su, Saiping Guan, Long Bai, Zhongni Hou, Zixuan Li, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
arXiv:2606. 30133v1 Announce Type: cross Abstract: Retrieval-augmented generation built on knowledge graphs (Graph RAG) outperforms flat passage retrieval on multi-hop question answering by leveraging graph structure.
By Illia Makarov, Mykola Glybovets
arXiv:2606. 02581v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) faces a fundamental three-way tension: deeper retrieval improves factual grounding but inflates token costs and end-to-end latency.
By Sanjay Mishra
arXiv:2609.00513v1 Announce Type: new
Abstract: Retrieval-Augmented Generation (RAG) mitigates large language models (LLMs) hallucinations, yet conventional dense retrieval struggles with the complex...
By Siyuan Zhang, Hanchen Wang, Dong Wen, Ying Zhang, Wenjie Zhang
arXiv:2608. 07954v1 Announce Type: new Abstract: Large language models can answer knowledge-intensive questions more reliably when they are grounded with knowledge graphs, but systems such as Think-on-Graph and Reasoning-on-Graph repeatedly query the same graph neighborhoods across different questions.
By Uros Stanic, Changcheng Yuan, Sabuj Laskar, Ariful Azad
arXiv:2603. 29875v3 Announce Type: replace-cross Abstract: One of the key problems in Retrieval-augmented generation (RAG) systems is that chunk-based retrieval pipelines represent the source chunks as atomic objects, mixing the information contained within such a chunk into a single vector.
By Ryszard Tuora, Mateusz Gali\'nski, Micha{\l} Godziszewski, Micha{\l} Karpowicz, Mateusz Czy\.znikiewicz, Adam Kozakiewicz, Tomasz Zi\k{e}tkiewicz
arXiv:2608. 15919v1 Announce Type: cross Abstract: Retrieval-Augmented Generation over knowledge graphs (Graph-RAG) has emerged as a powerful paradigm for grounding large language models in domain-specific corpora.
By Nicola Cogotti
arXiv:2504. 20114v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems face significant challenges in multi-hop question answering (MHQA), where complex queries require synthesizing information across multiple document chunks.
By Zhonghao Li, Kunpeng Zhang, Jinghuai Ou, Shuliang Liu, Xuming Hu
Retrieval-Augmented Generation over knowledge graphs (Graph-RAG) has emerged as a powerful paradigm for grounding large language models in domain-specific corpora. However, existing systems face persi...
VikingRAG is a directory‑aware semantic data management system that reduces token usage in retrieval‑augmented generation by tightly integrating semantic and structural access. It employs multi‑round retrieval traces as reusable experience edges and an adaptive escalation strategy to avoid unnecessary multi‑round exploration. Experiments show that VikingRAG achieves comparable accuracy to state‑of‑the‑art methods while using only 11.6%–51.9% of their tokens, and further reductions to 5.1%–32.5% with trace reuse and escalation.
By Peiyuan Gao, Gaoyuan Zhang, Haojie Qin, Yahui Sun, Qianyi Zhang, Yunhao Zhang, Zeyu Wang, Wei Lu
arXiv:2603. 24925v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) systems that rely on semantic search often fail to retrieve the complete set of evidence for complex queries, particularly when information is distributed across multiple sources.
By Ruizhong Miao, Yuying Wang, Rongguang Wang, Chenyang Li, Tao Sheng, Sujith Ravi, Dan Roth
MOSAIC is a training‑free framework that adapts Graph Retrieval‑Augmented Generation (GraphRAG) to each query by converting query‑specific evidence needs into a bounded policy over seed selection, traversal, stopping, and evidence selection. It keeps the corpus graph, indexes, scoring, grounding, and answer generation shared, while an LLM analyzer tailors the exploration strategy per query. On GraphRAG‑Bench, MOSAIC improves answer correctness by over 5 points on Medical and 4 points on Novel, achieves high evidence recall and context relevancy, and reduces path and evidence evaluations compared to fixed policies.
By EunKyeong Lee, Kyeong-Jin Oh, Jinwon Kim, Hye Woo Lee, Minsang Song, Hyeongjun Jang, Junyoung Youn