AINeutralarXiv – CS AI · Jun 237/10
🧠Researchers propose a three-layer framework integrating large language models with digital twins and automation systems to enable adaptive industrial autonomous systems. The TPSR model transforms user tasks into executable processes through LLM-based reasoning, demonstrated across five peer-reviewed studies with prototypes showing improved task executability and reduced manual effort.
AIBearisharXiv – CS AI · May 287/10
🧠Researchers have identified critical vulnerabilities in machine learning-based fault detection systems used in cyber-physical infrastructure, demonstrating that backdoor attacks can compromise these safety-critical systems with poisoning rates as low as 10%. This threat directly impacts smart grids, industrial automation, and other essential infrastructure that increasingly rely on AI models for anomaly detection and system recovery.
AIBearisharXiv – CS AI · Apr 157/10
🧠Researchers empirically evaluated 450 LLM-generated Python scripts for construction safety and found alarming reliability gaps, including a 45% silent failure rate where code executes but produces mathematically incorrect safety outputs. The study demonstrates that current frontier LLMs lack the deterministic rigor required for autonomous safety-critical engineering applications, necessitating human oversight and governance frameworks.
🧠 GPT-4🧠 Claude🧠 Gemini
AINeutralarXiv – CS AI · Jun 256/10
🧠Researchers present new confidence sequence methods for statistical model checking of Markov decision processes in online settings, achieving 50x sample efficiency improvements over previous approaches. The work addresses the practical problem of obtaining meaningful guarantees when exact transition probabilities are unknown, with applications to cyber-physical and biological systems.
AINeutralarXiv – CS AI · Jun 116/10
🧠Researchers propose a runtime enforcement framework using Hybrid Automata to actively prevent safety violations in autonomous and cyber-physical systems by monitoring and modifying unsafe behaviors in real time. The approach combines discrete-event editing with continuous monitoring and is validated through an Adaptive Cruise Control case study, demonstrating effective safety compliance with minimal computational overhead.
AI × CryptoBullisharXiv – CS AI · Jun 96/10
🤖A research paper proposes blockchain as foundational infrastructure for embodied AI systems, addressing the dual challenge of securing data economies while defending against quantum computing threats. The work integrates post-quantum cryptography, cross-organizational governance, and scalable architectures to create trustworthy decentralized environments for AI-driven cyber-physical systems.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers propose Product-Aware Deep Autoencoders to improve anomaly detection in multi-product manufacturing environments, addressing a critical vulnerability where traditional global models fail to detect cyber-physical attacks. Testing on the Tennessee Eastman Process benchmark demonstrates the approach achieves 100% detection accuracy versus 22.2% for conventional models under attack scenarios.
AINeutralarXiv – CS AI · Apr 146/10
🧠Researchers propose a reactor-model-of-computation approach using the Lingua Franca framework to address nondeterminism challenges in AI-powered human-in-the-loop cyber-physical systems. The study uses an agentic driving coach as a case study to demonstrate how foundation models like LLMs can be deployed in safety-critical applications while maintaining deterministic behavior despite unpredictable human and environmental variables.
