Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.

Overview:  Reinforcement learning in 2025 is more practical than ever, with Python libraries evolving to support real-world simulations, robotics, and deci ...

Patronus AI unveiled “Generative Simulators,” adaptive “practice worlds” that replace static benchmarks with dynamic reinforcement-learning environments to train more reliable AI agents for complex, ...

Reinforcement Learning, Explainable AI, Computational Psychiatry, Antidepressant Dose Optimization, Major Depressive Disorder, Treatment Personalization, Clinical Decision Support Share and Cite: de ...

This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

At HIMSS26, Dr. Nathan Moore of the BJC Accountable Care Organization will show how health systems can move beyond chatbots ...

You might have seen headlines sounding the alarm about the safety of an emerging technology called agentic AI.

Artificial Intelligence (AI) has achieved remarkable successes in recent years. It can defeat human champions in games like Go, predict protein structures with high accuracy, and perform complex tasks ...

Fluid–structure interaction (FSI) governs how flowing water and air interact with marine structures—from wind turbines to ...

Swarm finance is set to transform financial infrastructure through decentralized, adaptive agent systems that enhance ...

A practical guide to the four strategies of agentic adaptation, from "plug-and-play" components to full model retraining.

Meet NVIDIA Nitrogen, a generalist gaming agent trained on 40,000 hours of video, so you can understand how imitation learning scales.