解码机器学习决策：面向大规模排序系统的智能体推理框架

摘要

现代大规模排序系统运行于一个集多元目标、运营约束与动态产品需求于一体的复杂环境中。该领域的进展日益受限于工程语境约束——即将模糊的产品意图转化为合理、可执行、可验证假设的艰巨过程，而非仅受建模技术制约。我们提出GEARS（生成式智能排序系统引擎），该框架将排序优化重构为可编程实验环境中的自主发现过程。GEARS不再将优化视为静态模型选择，而是通过专用智能体技能将排序专家知识封装为可复用的推理能力，使操作者能够通过高层意图氛围个性化引导系统。此外，为确保生产可靠性，该框架集成验证钩子以强化统计稳健性，过滤过度拟合短期信号的脆弱策略。在多类产品界面上的实验验证表明，GEARS通过算法信号与深度排序语境的协同作用，持续识别出接近帕累托最优的优质策略，同时保持严格的部署稳定性。

English

Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product requirements. Progress in this domain is increasingly bottlenecked by the engineering context constraint: the arduous process of translating ambiguous product intent into reasonable, executable, verifiable hypotheses, rather than by modeling techniques alone. We present GEARS (Generative Engine for Agentic Ranking Systems), a framework that reframes ranking optimization as an autonomous discovery process within a programmable experimentation environment. Rather than treating optimization as static model selection, GEARS leverages Specialized Agent Skills to encapsulate ranking expert knowledge into reusable reasoning capabilities, enabling operators to steer systems via high-level intent vibe personalization. Furthermore, to ensure production reliability, the framework incorporates validation hooks to enforce statistical robustness and filter out brittle policies that overfit short-term signals. Experimental validation across diverse product surfaces demonstrates that GEARS consistently identifies superior, near-Pareto-efficient policies by synergizing algorithmic signals with deep ranking context while maintaining rigorous deployment stability.

解码机器学习决策：面向大规模排序系统的智能体推理框架

Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System

摘要

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