The paper introduces Knowledge Cards, a new structured artefact designed to capture validated knowledge about specific concepts that AI systems use to make decisions. Unlike existing model, data, and system cards, Knowledge Cards focus on the layer between inputs and outputs, documenting entities, relationships, reasoning patterns, conditions for validity, and provenance, all grounded in a formal domain ontology and signed off by a domain expert. Prototype cards have been created in the energy and pharmaceutical domains, and the schema is released as a public draft for community engagement.
By Liliana Ferreira
arXiv:2605. 22093v3 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modelling practices, from lightweight vocabularies to richly axiomatised ontologies, makes integration and reuse expensive and brittle.
By Enrico Daga, Valentina Tamma, Terry Payne
The paper critiques the Semantic Web’s failure to deliver machine‑interpretable knowledge, arguing that its standards omitted key elements—conditions for claims, operational grounding, and coverage scope—making truth, applicability, and boundary recognition impossible. It proposes a new framework, Semantic Knowledge Technologies, with a seven‑layer architecture and five measurable tests of understanding (check, connect, derive, act, delimit). The authors introduce concepts such as Large Knowledge Models, SLKMs, and a falsifiable definition of Semantic Artificial General Intelligence, presenting a research agenda to address these gaps.
By Achille Zappa
arXiv:2605. 28787v2 Announce Type: replace-cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows.
By Shiyu Chen, Tarfah Alrashed, Alon Halevy, Natasha Noy
arXiv:2511. 17162v2 Announce Type: replace Abstract: The Belief-Desire-Intention (BDI) model is a cornerstone for representing rational agency in artificial intelligence and cognitive sciences.
By Sara Zuppiroli, Carmelo Fabio Longo, Anna Sofia Lippolis, Rocco Paolillo, Lorenzo Giammei, Miguel Ceriani, Francesco Poggi, Antonio Zinilli, Andrea Giovanni Nuzzolese
The paper proposes five runtime primitives—discovery, identity, governance, attestation, and supply chain—to manage autonomous AI agents in enterprise settings. It argues that traditional control models fail because agents are transient, model-driven, and self‑discoverable, making runtime governance essential. The authors detail an implementation that mediates agent actions against policy, authorizes them via a per‑tenant vocabulary, and records them in a verifiable ledger, noting the associated operational costs and partial deployment status.
By Jiten Oswal, John Cadeddu
arXiv:2607. 12619v1 Announce Type: new Abstract: The rapid emergence of LLM-powered autonomous and semi-autonomous agents is reshaping software systems from static, request-response components into goal-directed, adaptive, and tool-using computational actors.
By Amin Beheshti, Rong N. Chang, Boualem Benatallah, Fabio Casati, Schahram Dustdar, Geoffrey Fox, Quan Z. Sheng, Yan Wang, Jian Yang, Albert Zomaya
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
The paper argues that large language model agents should not be the sole authority on whether they have satisfied a task. It identifies two gaps—understanding–execution and state–authority—where agents may claim completion without actually meeting the specification. The authors propose SpecHarness, a framework that separates agent proposals from authoritative state by requiring evidence from qualified providers to confirm compliance, and demonstrate its effectiveness on guideline‑following and artifact‑generation tasks.
By Haiqing Li, Xin Ma, Yinhao Wu, Wenliang Zhong, Feng Jiang, Thao M. Dang, Xiao Hu, Hehuan Ma, Yuzhi Guo, Junzhou Huang
arXiv:2604. 00555v5 Announce Type: replace Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level.
By Thanh Luong Tuan, Abhijit Sanyal
arXiv:2603. 24747v2 Announce Type: replace Abstract: The emergence of large language model agents capable of invoking external tools has created urgent need for formal verification of agent protocols.
By Andreas Schlapbach
arXiv:2607. 00828v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows.
By Jalal Mahmud, Eser Kandogan