arXiv AI August 11, 2026 By Uros Stanic, Changcheng Yuan, Sabuj Laskar, Ariful Azad

KGCache: Amortized Subgraph Retrieval for KG Reasoning with LLMs

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 7

Risk-Constrained Freshness-Aware Semantic Caching for Open-Web Retrieval-Augmented LLMs

arXiv:2607. 04281v1 Announce Type: cross Abstract: Semantic caching reduces the latency and cost of retrieval-augmented generation (RAG) by serving cached answers to semantically similar queries, but most existing methods do not model the time-varying freshness of open-web evidence.

By Muhammad Mansoor, Tahir Ahmad, Yeo-Chan Yoon

llms rag benchmarks

arXiv AI

1d ago

Noesis: Bidirectional Graph-RAG with Adaptive Parallelism and Cross-Knowledge-Base Semantic Discovery

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

llms rag efficiency

arXiv AI

Jun 3

Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs

arXiv:2606. 03705v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations.

By Weiwei Ding, Zixuan Li, Long Bai, Zhuo Chen, Kun Su, Fei Wang, Xiaolong Jin, Jin Zhang, Jiafeng Guo, Xueqi Cheng

llms benchmarks safety

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.

By Illia Makarov, Mykola Glybovets

rag diffusion nlp benchmarks

arXiv Machine Learning

Jun 2

KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering

arXiv:2606. 00328v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for knowledge base question answering (KBQA), where answering requires selecting entities from a question-specific knowledge-graph subgraph.

By Albert Sawczyn, Piotr Bielak, Tomasz Kajdanowicz

llms rag nlp benchmarks safety

arXiv AI

Jun 2

From Graph Retrieval to Schema Realization: Counterfactual Validation for Text-to-SPARQL over Heterogeneous Knowledge Graphs

arXiv:2508. 01815v2 Announce Type: replace-cross Abstract: Text-to-SPARQL maps natural-language questions to executable SPARQL queries over RDF knowledge graphs.

By Yang Zhao, Chengxiao Dai, Yue Xiu, Dusit Niyato

agents nlp benchmarks safety

Related stories

Risk-Constrained Freshness-Aware Semantic Caching for Open-Web Retrieval-Augmented LLMs

Noesis: Bidirectional Graph-RAG with Adaptive Parallelism and Cross-Knowledge-Base Semantic Discovery

Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs

Query-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge Graphs

KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering

From Graph Retrieval to Schema Realization: Counterfactual Validation for Text-to-SPARQL over Heterogeneous Knowledge Graphs