Active Discovery of Preference Cycles with Anytime-Valid Inference

Preference-based A/B comparisons guide model updates, yet a sequence of locally winning updates can conceal declining underlying strength. Preference cycles expose matchup advantages that no single ranking preserves, but noisy comparisons require evidence for every direction. Uniform sampling can leave a promising cycle waiting on one unresolved pair. We develop an active procedure that targets cycles by estimated remaining evidence cost and shares observations across overlapping and later-proposed candidates. Under stationary independent Bernoulli streams, confidence sequences control the probability of any false report throughout the adaptive search. A fixed-order analysis charges the first positive cycle and the evidence needed to refute earlier candidates, counting shared pairs once. With identical confidence bounds and all initial labels charged, targeting reduces mean first-report cost by 15%-88% across four cyclic environments relative to a representative adaptive transitivity-testing method that we adapt to continued discovery. Updating priorities concentrates queries on cycles that can be completed, while shared evidence supports subsequent discoveries within the same budget.