Scientists Found A Better Language For AI Agents

As the number of AI agents grows, coordinating them effectively using standard natural language is challenging due to inherent inefficiencies, risks of prompt injection, and coordination errors. The 'RecursiveMAS' framework addresses these issues by enabling agents to communicate through raw, un-decoded brain-like latent signals. By treating agents as a unified system and linking their internal states, this method demonstrates significant improvements in task performance—such as solving complex mathematical problems—while substantially reducing token usage and costs.