Learning What Matters Now: Dynamic Preference Inference under Contextual Shifts

ArXi:2603.22813v1 Announce Type: new Humans often juggle multiple, sometimes conflicting objectives and shift their priorities as circumstances change, rather than following a fixed objective function. In contrast, most computational decision-making and multi-objective RL methods assume static preference weights or a known scalar reward. In this work, we study sequential decision-making problem when these preference weights are unobserved latent variables that drift with context.