Topic
Recursivemas
2 episodes
-
RecursiveMAS cuts multi agent AI costs by 75%: researchers
Justy and Cody dig into RecursiveMAS, a research framework that lets multi-agent systems pass latent embeddings instead of text, cutting token usage and speeding up inference while keeping base model weights frozen.
-
Recursive Multi Agent Systems
RecursiveMAS is a new multi-agent framework from researchers at UIUC, Stanford, NVIDIA, and MIT that replaces text-based agent handoffs with latent-space recursion — cutting token usage by up to 75%, speeding up inference 2.4x, and improving accuracy by 8.3% across nine benchmarks. Justy and Cody dig into why passing hidden states instead of words is such a big deal, what the RecursiveLink module actually does, and whether any of this is shippable today.