Google introduces Neuro-Agentic Control, a novel AI framework that combines LLM-based planning with a Time-Series Foundation Model (TimesFM) to achieve physics-grounded autonomous defense for industrial IoT. This architecture, featuring a "Counterfactual Physics Injection" mechanism, effectively prevents LLM hallucinations, ensuring safe and reliable control over critical security systems in operational technology environments.

Groundbreaking Arxiv research reveals how large language model safety mechanisms are encoded and can be bypassed, introducing novel 'Activation-Guided' adversarial attacks. The study finds safety representations are distributed across model layers, not localized, and proposes a 33x faster attack method, Soft-GCG, offering critical insights for designing more robust AI alignment strategies.