Industrial Control Systems
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The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems
Announce Type: new Abstract: Reinforcement learning has shown promising results for optimizing the control of industrial energy systems, yet most existing studies remain limited to the application in simulation environments. We investigate the challenges of deploying reinforcement learning in a real-world industrial energy system, considering a thermal heating network as a use case. We formulate the task as a Markov Decision Process and systematically analyze the associated challenges along...
IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems
arXiv:2606.01691v1 Announce Type: new Abstract: Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety incidents. However, existing tools exhibit limited effectiveness in real-time anomaly detection due to the complex dependencies among sensors and actuators. To tackle this, we present IstGPT, the first industrial anomaly detection tool based on LLMs and graph learning to provide real-time protection...
Artificial-reference tracking MPC with probabilistically validated performance on industrial embedded systems
arXiv:2511.03603v2 Announce Type: replace Abstract: Industrial embedded systems are typically used to execute simple control algorithms due to their low computational resources. Despite these limitations, the implementation of advanced control techniques such as Model Predictive Control (MPC) has been explored by the control community in recent years, typically considering simple linear formulations or explicit ones to facilitate the online computation of the control input. These...
Scheduling Mechanisms in Wireless Sensor-Actuator Networks for Multi-rate Periodic Control in Industry 4.0
arXiv:2605.30520v1 Announce Type: new Abstract: This paper investigates scheduling strategies for wireless sensor-actuator networks (WSANs) in Industry 4.0 scenarios. In particular, we address the problem of real-time scheduling for multi-rate control systems by proposing a novel framework.
Anthropic calls for global AI slowdown, says systems may outpace human control
Anthropic calls for global AI slowdown, says systems may outpace human control The developer of Claude says a pause in the AI race would 'likely be a good thing' and warns that cutting-edge models are beginning to show signs they could become increasingly difficult for humans to control. Artificial intelligence company Anthropic suggested Thursday a global pause on building the most powerful AI systems as the latest models are beginning to show signs they could escape human control.
Real-World Deployment of a 5G-Connected Edge-Controlled Aerial Robot in Industrial Subterranean Mines
arXiv:2606.04818v1 Announce Type: new Abstract: This article presents the first real-world autonomous flight of a 5G-connected aerial robot controlled by an edge-offloaded controller, and aims to bridge the gap between controlled and factual setups. The robot operates within an active industrial subterranean mine, while the high-level controller is deployed in a nearby Kubernetes-based edge cluster. Communication between the robot and the edge is enabled via a 5G New Radio (NR) Standalone...
Self-Conditioned Positional HNSW for Overlap-Aware Retrieval in Chunked-Document RAG Systems: Method and Industrial Evidence-Quality Audit
Announce Type: new Abstract: Chunked-document retrieval is a common component of retrieval-augmented generation (RAG) systems. Documents are split into overlapping chunks, embedded, and indexed with approximate nearest-neighbor search such as hierarchical navigable small world graphs (HNSW). Overlap improves boundary coverage but induces a practical failure mode: top-k retrieval often returns near-adjacent chunks that repeat evidence and waste prompt budget.
Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis
Announce Type: new Abstract: Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems, demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that...
Jamming-Resilient PRB Reservation for Latency-Critical O-RAN Network Slicing
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Anthropic calls for ‘brake pedal’ before AI develops itself without human oversight
Anthropic co-founder Jack Clark said AI agents might soon be able to build and train models themselves and, if that happens, humans could lose control over AI systems. Anthropic co-founder Jack Clark wants the AI industry to pump the brakes before the technology starts further developing itself without human input. Speaking to the BBC, Clark said 80% of Anthropic’s coding work is already being done by its AI Claude, and that it could go up to 100% in a couple of years.