arXiv AI June 16, 2026 By Fabio Vitali, Valentina Pasqual

Provenance-Enhanced Statements in Knowledge Graphs

arXiv:2606. 15246v1 Announce Type: cross Abstract: Provenance-enhanced statements of the form "according to $X$, $\varphi$" are pervasive in contemporary knowledge graphs, especially in domains where graph content primarily represents claims, interpretations, and hypotheses (\emph{capta}) rather than observer-independent facts (\emph{data}).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 16

VeriGraph: Towards Verifiable Data-Analytic Agents

arXiv:2606. 16603v1 Announce Type: cross Abstract: LLM-based agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable: a reliance on linear text trajectories makes their reasoning difficult to audit.

By Jiajie Jin, Zhao Yang, Wenle Liao, Yuyang Hu, Guanting Dong, Xiaoxi Li, Yutao Zhu, Zhicheng Dou

llms agents benchmarks

arXiv AI

Aug 25

Walking on the DARKSIDE

arXiv:2608.23370v1 Announce Type: new Abstract: Large Language Models (LLMs) recognise patterns but do not natively track the path of exclusions that a coherent discourse demands. When an input rests...

By Aldo Gangemi, Emanuele Bottazzi

llms safety

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Abstract: Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations.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

Jul 3

Evergreen: Efficient Claim Verification for Semantic Aggregates

arXiv:2604. 26180v2 Announce Type: replace-cross Abstract: With recent semantic query processing engines, semantic aggregation has become a primitive operator, enabling the reduction of a relation into a natural language aggregate using an LLM.

By Alexander W. Lee, Benjamin Han, Shayak Sen, Sam Yeom, Ugur Cetintemel, Anupam Datta

llms benchmarks

arXiv AI

Jun 29

Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification

arXiv:2511. 03217v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information.

By Shaghayegh Kolli, Richard Rosenbaum, Timo Cavelius, Lasse Strothe, Andrii Lata, Jana Diesner

llms agents fine-tuning benchmarks

arXiv AI

Sep 1

Memory-First Fact-Checking: A Knowledge-Graph-Grounded Multi-Agent System for Misinformation Detection

arXiv:2608.29617v1 Announce Type: cross Abstract: This paper introduces a hybrid fact-checking framework that integrates Knowledge Graph-based semantic memory with adversarial multi-agent reasoning f...

By Amelia Petrenciuc, Alexandru Lecu, Adrian Groza

llms agents benchmarks safety

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Related stories

VeriGraph: Towards Verifiable Data-Analytic Agents

Walking on the DARKSIDE

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

Evergreen: Efficient Claim Verification for Semantic Aggregates

Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification

Memory-First Fact-Checking: A Knowledge-Graph-Grounded Multi-Agent System for Misinformation Detection