Computing Equilibrium beyond Unilateral Deviation in Multi-Agent AI Systems
arXiv·low signal
Extends equilibrium computation beyond Nash and correlated equilibrium to settings where coalitions of agents can deviate together — directly relevant to multi-agent LLM systems where agents may implicitly coordinate. Provides new algorithmic approaches for computing stable outcomes in cooperative-competitive hybrid settings that arise in multi-agent architectures.