Neuro-Agentic Control Framework Uses LLM and Time-Series Foundation Model for Industrial Cybersecurity

Jul 13, 2026

Researchers propose a neuro-agentic control framework that combines an LLM-based planner (such as Gemini 2.5 Flash-Lite) with a pre-trained Time-Series Foundation Model (TimesFM) for autonomous defense in industrial IoT. The framework introduces a 'Counterfactual Physics Injection' mechanism, which simulates LLM-proposed interventions in the foundation model's latent space before actuation, allowing the system to reject hallucinated or unsafe actions. Evaluated on the SWaT dataset, the framework prevented five breaches (33.3%) below threshold, outperforming LSTM (26.7%) and TCN (13.3%) baselines, with zero physically invalid actions executed.

Why it matters: This work demonstrates a practical method to ground LLM-based agents with physics-aware foundation models, addressing safety concerns for closed-loop control in critical infrastructure.

Full story at: arXiv AI/ML ↗