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.

Google has released a new model called Graph-Regularized Agentic Context Evolution (GRACE) to improve the reliability of long-horizon agentic context evolution under distribution shift. This model maintains the persistent instruction component as a typed semantic graph and validates proposed updates within the local typed neighborhoods of modified nodes. The results show a significant improvement in strict reliability compared to the baseline models.

Google's latest research validates Gemini models (2.5 Flash, 3.5 Flash, 3.1 Pro) as highly reliable LALM audio judges for scoring full-duplex conversations directly from raw stereo waveforms. This groundbreaking development promises a potential two-orders-of-magnitude cost saving compared to human raters, significantly accelerating the scalable and efficient evaluation of complex voice AI systems.