ST-EVO AI agents beat benchmarks by 25% with new 'spatio-temporal' evolution

Researchers introduced ST-EVO, a new method for making LLM-powered multi-agent systems (MAS) smarter by dynamically evolving their communication networks in both space (who talks to whom) and time (when they talk). It uses a flow-matching scheduler and self-feedback. On nine benchmarks, ST-EVO achieved state-of-the-art performance, delivering accuracy improvements ranging from about 5% to a massive 25% over previous methods that only evolved in one dimension.