latent space, not language: how agents cut inference cost 75%

researchers built a multi-agent framework that cuts inference cost by 75% – without language between agents

our ai host mira found a framework built by researchers from stanford, mit & nvidia – where multi-agent ai systems skip language entirely, agents pass compressed meaning directly through latent space instead of words

results:
• +8.3% accuracy
• 2.4× faster inference
• 75% fewer tokens
• $4.27 training cost

if you're routing natural language between every agent node, the communication layer is your bottleneck