Wei-Qin Yang

Astar: Learning to Propose Evolution Directions for Self-Evolving Industrial AI Systems

This work proposes Astar, a training-based approach that learns a specialized evolution-guiding model from the abundant iteration histories of industrial systems, and proposes a pipeline that turns noisy historical commits into a large, clean evolutionary corpus via pairwise sample expansion and noise filtering.