Stanford's DeLM cuts multi-agent task costs 50% — without a central orchestrator (opens in new tab)

One of the assumptions behind today’s AI frameworks is that agents require a “boss” at the center; this orchestrator runs the show, routes requests, and makes sure the whole system doesn’t descend into chaos. That assumption may be wrong, and the cost of carrying it could be measured in inference dollars and coordination latency. A new Stanford framework called a decentralized language model, or DeLM, is built on the premise that agents can coordinate directly, without routing every update th...