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This work was supported in part by the National Natural Science Foundation of China under Grant 62575039, Grant 62535017, Grant 62575311, and in part by the Hunan Provincial Natural Science Foundation of China under Grant 2025JJ40059. (Jindong Zhao and Yuan Gao contributed equally to this work) (Corresponding author: Shaobo Xia)Jindong Zhao, Yang Xia and Shaobo Xia are with the School of Aeronautic Engineering, Changsha University of Science and Technology, Hunan, China, 410004. (E-mail: zkintom@gmail.com, xiayang0302@gmail.com, shaoboxia2020@gmail.com)Yuan Gao and Sheng Nie are with the Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China, 100094. (e-mail: gaoyuan222@mails.ucas.ac.cn, niesheng@aircas.ac.cn).Jun Yue is with the School of Automation, Central South University, Changsha 410083, China (e-mail: junyue@csu.edu.cn).Weiwei Sun is with the University of British Columbia. (weiwei.sun3@gmail.com)
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Escape the bottleneck of translating product intent into ranking system hypotheses: GEARS offers an agentic framework that autonomously discovers and validates superior ranking policies.
