Sheminith Pivot: Möbius Topology and Intrinsic Reward in AGI-to-ASI Transition

This preprint defines the "Sheminith Pivot" as a phase transition in reinforcement learning where entropy saturates and intrinsic reward collapses to zero, leading to vanishing gradients and stable convergence. Drawing from octave periodicity (Sheminith), we model it as a non-orientable Möbius manifold. PPO experiments (gradient norm 3.2→0.008, collapse 0.24→0.99) and Lyapunov analysis confirm robustness, suggesting a topological boundary for AGI-to-ASI shifts. Code and figures included.