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Reinforcement learning agents can adapt and improve without collapsing into deterministic dominance if sovereignty constraints are enforced at every update step, enabling structural diversity.
Forget aligning AI intent: mechanically bounding an agent's "selection power" – its ability to determine which options are even considered – offers a more direct path to safe deployment in high-stakes domains.
