The paper reviews 66 studies on large language models (LLMs) applied to HVAC operations in building energy systems, categorizing them by application and method families and evaluating their evidence realism and deployment readiness. It finds that most work focuses on building energy modelling, with only four studies reaching pilot-level evidence and none reporting sustained operational deployment. LLMs are currently best suited as semantic and workflow layers—such as point‑name normalisation and document‑grounded operator support—rather than autonomous HVAC controllers, and future research should target field‑validated benchmarks and safe, low‑latency LLM‑MPC/RL integrations.
By Alexander Neubauer, Tianzhen Hong, Han Li, Mengbo Yu, Amin Darbandi, Yannick F\"urst, Martin Kriegel
arXiv:2606. 17637v1 Announce Type: new Abstract: Building Management Systems (BMS) are essential for optimizing energy efficiency and operational performance in modern buildings.
By Yiyue Qian, Shinan Zhang, Huan Song, Negin Sokhandan, Hannah Marlowe, Diego Socolinsky
The paper introduces a SAREF-compliant ontology designed to represent distributed AI workflows across edge, fog, and cloud environments. It extends the SAREF4SYST ontology with concepts for AI pipelines, executable jobs, resources, deployment constraints, and communication links, creating a unified semantic model for both AI workflows and heterogeneous infrastructures. Evaluation through smart‑grid energy service scenarios and competency questions demonstrates successful deployment, reasoning, and workload adaptation, achieving 90‑100% deployment success and sub‑80 ms orchestration times.
By Viorica Rozina Chifu, Tudor Cioara, Vasile Ofrim, Liana Toderean, Ionut Anghel, Laura Daniele, Cornelis Bouter
This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components. The approach uses formalized...
arXiv:2608.22974v1 Announce Type: new
Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks,...
By Xiaohui Zhang, Zequn Sun, Chengyuan Yang, Yuanning Cui, Lingbing Guo, Wei Hu
arXiv:2608. 16421v1 Announce Type: new Abstract: This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components.
By Jan Novacek, Alexander Viehl, Oliver Bringmann, Wolfgang Rosenstiel
arXiv:2607. 17963v1 Announce Type: new Abstract: Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete.
By Anna Sofia Lippolis, Mohammad Javad Saeedizade, Stefan Schmid, Simon Blattner, Robin Keskis\"arkk\"a, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese
arXiv:2606. 02253v1 Announce Type: new Abstract: Increasing the circularity of resource use in our society has been recognized as a path to sustainability, i.
By Huanyu Li, Els de Vleeschauwer, Robin Keskis\"arkk\"a, Mikael Lindecrantz, Mina Abd Nikooie Pour, Ying Li, Ben De Meester, Patrick Lambrix, Eva Blomqvist
arXiv:2607. 29553v1 Announce Type: new Abstract: Organizations increasingly define operational metrics in structured, machine-readable formats to monitor systems, processes, and compliance.
By Hussain Hussain, Stefan Sch\"oberl, Angelika Schneider, Verena Geist
The paper proposes a four‑dimensional formal framework—Semantic Expressivity, Agentic Discoverability, Task‑Relative Grounding, and Epistemic Trust Scope—to extend current KG metadata standards (VoID and DCAT). It introduces the Agentic Affordance Profile (AAP), a semantic layer that enables agents to select, compose, and diagnose failures in knowledge graphs at planning time. A scholarly‑search example illustrates the framework and outlines a five‑point research agenda for scaling AAP‑based affordance matching.
By Terry R. Payne, Valentina Tamma, Enrico Daga
arXiv:2507. 21438v2 Announce Type: replace Abstract: Ontologies and knowledge graphs require continuous evolution to remain comprehensive and accurate, but manual curation is labor intensive.
By Vishal Raman, Vijai Aravindh R, Abhijith Ragav
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