Regularized last-iterate solvers select the maximum-entropy Nash equilibrium while regret-averaging methods select lower-entropy faces on zero-sum Nash polytopes, verified on analytic testbeds and a 180-game ensemble.
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AI-emulated APTs compromise enterprise hosts and weaponize defender tools in 8/10 trials while military ranges resist, indicating TTP attribution fails when agents can be scaffolded to mimic threat actors.
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Which Nash Equilibrium? Solver-Dependent Selection on Zero-Sum Nash Polytopes
Regularized last-iterate solvers select the maximum-entropy Nash equilibrium while regret-averaging methods select lower-entropy faces on zero-sum Nash polytopes, verified on analytic testbeds and a 180-game ensemble.