[2603.27765] Let the Agent Steer: Closed-Loop Ranking Optimization via Influence Exchange

Computer Science > Artificial Intelligence arXiv:2603.27765 (cs) [Submitted on 29 Mar 2026] Title:Let the Agent Steer: Closed-Loop Ranking Optimization via Influence Exchange Authors:Yin Cheng, Liao Zhou, Xiyu Liang, Dihao Luo, Tewei Lee, Kailun Zheng, Weiwei Zhang, Mingchen Cai, Jian Dong, Andy Zhang View a PDF of the paper titled Let the Agent Steer: Closed-Loop Ranking Optimization via Influence Exchange, by Yin Cheng and 9 other authors View PDF HTML (experimental) Abstract: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. We present Sortify, the first fully autonomous LLM-driven ranking optimization agent deployed in a large-scale production recommendation system. The agent reframes ranking optimization as continuous influence exchange, closing the full loop from diagnosis to parameter deployment without human intervention. It addresses structural problems through three mechanisms: (1) a dual-channel framework grounded in Savage's Subjective Expected Utility (SEU) that decouples offline-online transfer correction (Belief channel) from constraint penalty adjustment (Preference channel); (2) an LLM m...