How can agents reach consensus without communication?
Noncooperative Equilibrium Selection via a Trading-based Auction
This paper introduces TACO (Trading Auction for Consensus), a decentralized algorithm for multi-agent systems to reach agreement on a single choice even with conflicting preferences. Agents offer and accept trades based on private valuations of a secondary asset, like carbon credits, to reach consensus without direct communication.
For LLM-based multi-agent systems, TACO offers a mechanism for LLMs acting as agents to coordinate actions by exchanging virtual resources, potentially improving collaboration and task completion without revealing the internal preference models of each LLM. The decentralized and privacy-preserving nature of TACO is particularly relevant for deploying LLMs in distributed environments. The auction-like mechanism could be adapted to utilize the output probabilities of LLMs as bids and valuations.