Von Neumann Algebras Unlock Equilibrium in Infinite Multi-Agent AI Systems
Researchers have developed an operator-algebraic framework proving that learning dynamics in games with infinite agents converge to equilibria via von Neumann algebras. The breakthrough introduces an 'ordinal folding index' to measure convergence complexity and solves longstanding allocation problems, with implications for LLM stability and large-scale system design.