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GEARS: Agentic Framework for Ranking Optimization

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.

UW Open-Sources MoCo Framework

UW Open-Sources MoCo Framework

University of Washington releases MoCo, a Python framework for multi-model collaboration research. It supports 26 algorithms across API, text, logit, and weight levels. Researchers can customize datasets, models, and hardware to build combinatorial AI systems.

机器之心MediaFeb 16#research#moco#initial