Abstract: This letter investigates the integrated communication and control (ICAC) co-design for uncrewed aerial vehicle (UAV) swarms using multi-agent reinforcement learning, which involves ...

The technique, called Reinforcement Learning with Verifiable Rewards with Self-Distillation (RLSD), combines the reliable ...

Defunct startups are being liquidated for their Slack archives, Jira tickets, and email threads—operational exhaust that AI labs now treat as premium training data. When Shanna Johnson was winding ...

As the electricity market is progressively liberalized, virtual bidding has emerged as a novel participation mechanism attracting increasing attention. This paper integrates evolutionary game theory ...

Training standard AI models against a diverse pool of opponents — rather than building complex hardcoded coordination rules — is enough to produce cooperative multi-agent systems that adapt to each ...

Anthropic unveiled a multi-agent Code Review system for Claude Code that automatically reviews every PR for bugs. It dispatches parallel AI agents to review pull requests and adds inline comments ...

According to God of Prompt on X, a developer demonstrated a multi-agent orchestration system powered by Opus 4.6 that watches YouTube tutorials and autonomously executes the demonstrated workflows. As ...

Researchers have developed a new artificial intelligence approach that exposes critical weaknesses in multi-agent reinforcement learning systems, enabling stronger coordinated attacks with broad ...

Multi-AI agents – multiple artificial intelligence agents working together in a shared environment – can be used to address persistent workflow challenges in clinical decision support, drawing from ...

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.