How RecursiveMAS speeds up multi-agent inference by 2.4x and reduces token usage by 75%

One of the key challenges of current multi-agent AI systems is that they communicate by generating and sharing text sequences, which introduces latency, drives up token costs, and makes it difficult to train the entire system as a cohesive unit. To overcome this challenge, researchers at University of Illinois Urbana-Champaign and Stanford University developed RecursiveMAS , a framework that enables agents to collaborate and transmit information through embedding space instead of text. This change results in both efficiency and performance gains.