arXiv AI
ISO-RAG: Isoperimetric Noise Control for Retrieval-Augmented Generation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 30
Query-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge Graphs
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.
arXiv Computation and Language
Sep 10
LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation
arXiv:2609.10239v1 Announce Type: cross Abstract: Graph-based retrieval can improve multi-hop question answering, but existing approaches often incur high query-time costs and produce diffuse, oversi...
arXiv Computation and Language
Oct 22
Graph Topology Meets Energy-Driven Pathfinding: Preserving Contextual Consistency in Retrieval-Augmented Generation
ThreadRAG is a retrieval‑augmented generation framework that preserves contextual consistency by structuring retrieved documents into Semantic Nodes and building a Semantic Topology Graph (STG). It uses Energy‑Driven Pathfinding (EDP) to identify query‑relevant subgraphs and linearize them into coherent textual evidence paths. Experiments on HotpotQA, 2WikiMultihop, and another benchmark show ThreadRAG outperforms strong baselines, achieving a 14.4% EM gain on HotpotQA and a 5.8% EM lead on 2WikiMultihop while cutting token cost to 23.3% of the strongest baseline.
arXiv AI
Jun 30
SemFlowRAG: Directed Semantic Flow from Abstraction to Evidence for Complex Reasoning
arXiv:2606. 28447v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhanced by Knowledge Graphs has shown promise in complex multi-hop reasoning tasks.
arXiv Computation and Language
Sep 4
R$^{2}$Adapter: A Routing and Rewriting Adapter for Efficient Hybrid RAG
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.
arXiv AI
Jun 6
Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)
arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.