Propose a multi-armed bandit system to optimize headlines faster

This tests online learning architecture and the explore-exploit tradeoff. A strong answer contrasts fixed A/B splits with adaptive allocation that minimizes regret, sketches a real-time decision service using Thompson Sampling or UCB with an exploration floor, and notes delayed feedback. It should mention non-stationarity since headlines decay, and explain validating against a holdout. Red flags include claiming bandits eliminate sample size requirements, proposing pure greedy allocation, or ignoring real-time update complexity.