Multi-agent systems can be made faster and more efficient by having agents refine their reasoning through recursive loops in latent space rather than text-based communication, achieving 1.2-2.4× speedup with 35-76% fewer tokens.
This paper introduces RecursiveMAS, a framework that improves multi-agent AI systems by having agents collaborate through repeated refinement cycles in a shared latent space rather than exchanging text.