arXiv:2607.02891v1 Announce Type: new Abstract: Many online decision-making problems involve both round-specific feasible actions and drifting reward models: eligible ad impressions, feasible prices, and available treatments can change over time, while user preferences, demand curves, and patient responses may evolve. Motivated by these applications, we study non-stationary linear bandits with round-specific feasible decision sets. Existing methods that obtain the optimal \(\widetilde O(T^{2/3}P_T^{1/3})\) dependence, where \(P_T\) is the path length of the reward-parameter sequence, impose an
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