RecursiveMAS cuts multi-agent token usage 75% via embedding space

RecursiveMAS from UIUC and Stanford replaces inter-agent text communication with embedding-space messaging, achieving 75% token reduction and 2.4x inference speedup with accuracy gains on coding and medical benchmarks. This directly addresses the prohibitive token costs of production multi-agent LLM systems.