Let the Agent Steer: Closed-Loop Ranking Optimization via Influence Exchange

ArXi:2603.27765v1 Announce Type: new Recommendation ranking is fundamentally an influence allocation problem: a sorting formula distributes ranking influence among competing factors, and the business outcome depends on finding the optimal "exchange rates" among them. However, offline proxy metrics systematically misjudge how influence reallocation translates to online impact, with asymmetric bias across metrics that a single calibration factor cannot correct.