Stanford's DeLM cuts multi-agent task costs 50% — without a central orchestrator

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 through a central controller. DeLM's shared knowledge base serves as a “common communication substrate” so that agents can build upon one another’s verified progress without having to route every interaction through a main agent to “merge, filter, and rebroadcast,” Yuzhen Mao and Azalia Mirhoseini, co-developers of the framework, explain in a research paper . It’s a system that’s not only possible, but desirable in certain instances. “Agents can build on prior findings, avoid repeated failures, prese

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