
GEARS: Agentic Framework for Ranking Optimization
GEARS reframes large-scale ranking as an agentic discovery process in a programmable environment, addressing engineering bottlenecks over modeling limits. It encapsulates expert knowledge into reusable agent skills for high-level intent steering and includes validation hooks for robust, stable policies. Experiments across product surfaces show superior near-Pareto-efficient outcomes.





