arXiv:2608. 14104v1 Announce Type: cross Abstract: The Shapes Constraint Language (SHACL) is a W3C recommendation to express syntactic constraints, called shapes, on RDF graphs.
By Anouk Oudshoorn, Piotr Gorczyca, D\"orthe Arndt
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Abstract: Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations.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
arXiv:2608. 03609v1 Announce Type: new Abstract: Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world workflows where they act on persistent operational data.
By Alejandro J. Mercado, Alessio Lomuscio
The paper presents a design-time verification framework for truly fearless concurrent and distributed systems. It describes a compiler that tracks wait‑for edges, preserves proofs while lowering code directly to native CPU, GPU, NPU, and FPGA targets, and uses a Program Semantic Graph to maintain the values and relationships that justify boundary contracts. Four verification tiers—structural inference, local arithmetic checks, reusable domain lemmas, and probabilistic relational proofs—enable comprehensive verification without developer annotations, supporting distributed proofs with Iris, Actris, and Aneris.
By Houston Haynes
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
arXiv:2606. 16010v1 Announce Type: cross Abstract: Large language models have achieved impressive performance on reasoning tasks spanning mathematics, science, programming, and commonsense inference.
By Raghu Anantharangachar