Why securing AI agents at runtime is essential as attackers find new ways to exploit generative orchestration.

In 2026, enterprises will be expected to automate processes that involve judgment, negotiation, compliance interpretation, ...

MemRL separates stable reasoning from dynamic memory, giving AI agents continual learning abilities without model fine-tuning ...

"Welcome to the world of RDHNet, a groundbreaking approach to multi-agent reinforcement learning (MARL) introduced by Dongzi Wang and colleagues from the College of Computer Science at the National ...

CNN reviewed and geolocated videos that demonstrate just some examples of federal agents’ tactics in the Twin Cities.

Abstract: This letter investigates a multi-user multi-input single-output (MISO) system assisted by movable antennas (MAs) and the reconfigurable intelligent surface (RIS). Specifically, the positions ...

Today's AI agents don't meet the definition of true agents. Key missing elements are reinforcement learning and complex memory. It will take at least five years to get AI agents where they need to be.

U.S. Immigration and Customs Enforcement arrested more than 100 illegal alien truck drivers on California highways in Operation Highway Sentinel, launched after a string of deadly crashes linked to ...

You might have seen headlines sounding the alarm about the safety of an emerging technology called agentic AI. That's where Sarra Alqahtani comes in. An associate professor of computer science at Wake ...

Abstract: Multi-Agent Reinforcement Learning (MARL) has shown great potential in solving complex tasks. Despite great success, low training efficiency remains a pervasive and long-standing challenge ...

Major Depressive Disorder (MDD) is a prevalent psychiatric condition requiring long-term pharmacological management, with escitalopram often prescribed as a first-line treatment. However, optimizing ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...