Decoding the Maze: New Frontiers in Achieving Nash Equilibrium in AI Architectures
summary
Claim: composing convex losses with neural nets creates a hidden game where parameter-to-action maps destroy convexity and introduce indirect competition. Proofs show gradient descent/ascent fails via recurrent cycles rather than convergence. The work builds and analyzes an explicit algorithm—a nonconvex-game solver—that provably escapes those recurrences and finds equilibria in examples. Open engineering questions remain: making it decentralized, parallel, private, and scaling with provable guarantees.
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